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Ren K, Zou L, Yang J, Wang Y, Min L. The Role of Autophagy and Cell Communication in COPD Progression: Insights from Bioinformatics and scRNA-seq. COPD 2025; 22:2444663. [PMID: 39991824 DOI: 10.1080/15412555.2024.2444663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2024] [Revised: 12/07/2024] [Accepted: 12/14/2024] [Indexed: 02/25/2025]
Abstract
Chronic obstructive pulmonary disease (COPD) is characterized by restricted airflow that leads to significant respiratory difficulties. This progressive disease often results in diminished pulmonary function and the onset of additional respiratory conditions. Autophagy, a critical cellular homeostasis mechanism, plays a significant role in the exacerbation of COPD. In this study, we utilized various bioinformatics tools to identify autophagy-related genes activated by smoking in individuals with COPD. Furthermore, we explored the immune landscape of COPD through these genes, analyzing cell communication patterns using scRNA-seq data. This analysis focused on key pathways between epithelial cells and other cellular subpopulations with different autophagy scores, essential for understanding the initiation and progression of COPD.
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Affiliation(s)
- Kaiqi Ren
- Department of Pulmonary and Critical Care Medicine, Northern Jiangsu People's Hospital, Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Lu Zou
- Yzngzhou Municipal Health Commission, Yangzhou, China
| | - Jingjing Yang
- Department of Pulmonary and Critical Care Medicine, Northern Jiangsu People's Hospital, Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Yuxiu Wang
- Department of Pulmonary and Critical Care Medicine, Northern Jiangsu People's Hospital, Clinical Medical College, Yangzhou University, Yangzhou, China
| | - Lingfeng Min
- Department of Pulmonary and Critical Care Medicine, Northern Jiangsu People's Hospital, Clinical Medical College, Yangzhou University, Yangzhou, China
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Huang L, Chen J, Yang R, Shi J, Zhou C, Chen T, Feng S, Huang C, Huang J, Xue J, Zhou Z, Zhu J, Wu S, Zhan X, Liu C. Deciphering distinct pathogenic mechanisms of ankylosing spondylitis and systemic sclerosis via shared biomarkers ZSWIM6 and CCL3L3: Insights from an integrative bioinformatics approach. Autoimmunity 2025; 58:2445557. [PMID: 39727004 DOI: 10.1080/08916934.2024.2445557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2024] [Revised: 11/14/2024] [Accepted: 12/16/2024] [Indexed: 12/28/2024]
Abstract
Ankylosing Spondylitis (AS) and Systemic Sclerosis (SSc) are both autoimmune diseases, albeit with distinct anatomical targets. AS primarily affects the spine and sacroiliac joints, triggering inflammation and eventual fusion of the vertebrae. SSc predominantly impacts the skin and connective tissues, leading to skin fibrosis, thickening, and potential damage to vital organs such as the lungs, heart, and kidneys. Despite their differing anatomical manifestations, inflammation serves as a pivotal factor in both conditions. Exploring the causes of the different pathogenesis of inflammation in AS and SSc could provide new insights into their treatment. We selected RNA-seq profiles of peripheral blood mononuclear cells (PBMCs) from the GEO datasets GSE73754 and GSE19617. DEGs were identified using the Limma R package with an adjusted p-value cutoff of < 0.05. Gene Ontology pathway analysis, SVM recursive feature elimination, and Gene Set Enrichment Analysis (GSEA) were conducted to analyze the DEGs. CIBERSORT was applied to estimate immune cell composition and its correlation with hub genes. Single-cell RNA sequencing data from peripheral blood mononuclear cells in the GSE194315 dataset were included to support differential expression analysis and biomarker identification. Additionally, single-cell RNA sequencing data from bone marrow blood samples were utilized to further validate these findings, offering complementary insights into biomarker expression across distinct sample types. A total of 762 DEGs were identified between AS patients and controls, and 441 DEGs between SSc patients and controls. Both conditions showed enrichment in the Natural killer cell mediated cytotoxicity pathway. ZSWIM6 and CCL3L3 were identified as potential biomarkers in AS and SSc, with significant diagnostic capabilities demonstrated by ROC analysis. Correlation analysis revealed associations between these biomarkers and specific immune cell types. The study utilizing ZSWIM6 and CCL3L3 as potential biomarkers provides deep insights into the distinct molecular mechanisms of SSc and AS. These findings lay the foundation for future research on targeted therapies and enhance our understanding of immune cell interactions in these autoimmune diseases.
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Affiliation(s)
- Liangyu Huang
- Spine Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, People's Republic of China
- First Clinical Medical College, Guangxi Medical University, Nanning, Guangxi, People's Republic of China
| | - Jiarui Chen
- Spine Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, People's Republic of China
| | - Renbang Yang
- First Clinical Medical College, Guangxi Medical University, Nanning, Guangxi, People's Republic of China
| | - Jianjun Shi
- First Clinical Medical College, Guangxi Medical University, Nanning, Guangxi, People's Republic of China
| | - Chenxing Zhou
- Spine Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, People's Republic of China
| | - Tianyou Chen
- Spine Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, People's Republic of China
| | - Sitan Feng
- Spine Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, People's Republic of China
| | - Chengqian Huang
- Spine Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, People's Republic of China
| | - Jieping Huang
- Emergency Department, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, Guangxi, People's Republic of China
- Key Laboratory of Molecular Pathology in Tumors of Guangxi Higher Education Institutions, Baise, Guangxi, People's Republic of China
| | - Jiang Xue
- Spine Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, People's Republic of China
| | - Zhongxian Zhou
- Spine Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, People's Republic of China
| | - Jichong Zhu
- Spine Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, People's Republic of China
| | - Shaofeng Wu
- Spine Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, People's Republic of China
| | - Xinli Zhan
- Spine Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, People's Republic of China
| | - Chong Liu
- Spine Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, People's Republic of China
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Xu Z, Zhang K, Zeng A, Yin Y, Chen K, Wang C, Fang X, Abuduwayiti A, Wang J, Dai J, Jiang G. Identifying GAP43, NMU, and TEX29 as Potential Prognostic Biomarkers for COPD Combined With Lung Cancer Patients Using Machine Learning. J Gene Med 2025; 27:e70020. [PMID: 40394719 DOI: 10.1002/jgm.70020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2025] [Revised: 03/25/2025] [Accepted: 04/20/2025] [Indexed: 05/22/2025] Open
Abstract
Chronic obstructive pulmonary disease (COPD) and lung cancer, frequently comorbid conditions intricately linked through smoking, represent significant global health challenges. COPD is a common comorbidity in nonsmall cell lung cancer (NSCLC) patients and has been shown to negatively impact prognosis. However, the molecular mechanisms underlying the interplay between COPD and lung cancer remain unclear. This study aims to identify differentially expressed genes (DEGs) associated with COPD-related lung cancer and, using various machine learning (ML) algorithms, uncover potential biomarkers for prognosis. We analyzed RNA sequencing data from 41 lung cancer patients (with and without COPD) and identified 61 DEGs, all of which were upregulated in solitary lung cancer compared to COPD-associated cases. Functional enrichment analysis revealed that these genes are involved in biological processes such as granulocyte chemotaxis and smooth muscle contraction and molecular functions including neuropeptide receptor binding. Three ML methods-support vector machine recursive feature elimination (SVM-RFE), least absolute shrinkage and selection operator (LASSO), and random forest-were applied to prioritize key biomarkers. Three genes, GAP43, NMU, and TEX29, were consistently selected across all methods. Further analysis demonstrated significant correlations between these genes and immune cell infiltration, with notable differences in immune cell composition observed in COPD-associated lung cancer. High expression levels of GAP43, NMU, and TEX29 were associated with poor survival outcomes in lung cancer patients, as validated through survival analysis of TCGA database data. Our findings suggest that these genes may serve as diagnostic and prognostic biomarkers for COPD-related lung cancer, thereby providing insights into potential therapeutic targets. Further studies with larger cohorts are required to validate these results and elucidate the underlying molecular mechanisms.
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Affiliation(s)
- Zhilong Xu
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Kaiyao Zhang
- Jinshan Branch of Shanghai Sixth People's Hospital, Shanghai, China
| | - Ao Zeng
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Yanze Yin
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - KeYi Chen
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Chao Wang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Xinyun Fang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Abudumijiti Abuduwayiti
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - JiaRui Wang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Jie Dai
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
| | - Gening Jiang
- Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China
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Huang S, Zhang L, Liu X. Bioinformatics Approach to Identifying Molecular Targets of Isoliquiritigenin Affecting Chronic Obstructive Pulmonary Disease: A Machine Learning Pharmacology Study. Int J Mol Sci 2025; 26:3907. [PMID: 40332792 PMCID: PMC12027559 DOI: 10.3390/ijms26083907] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2025] [Revised: 04/11/2025] [Accepted: 04/16/2025] [Indexed: 05/08/2025] Open
Abstract
To identify the molecular targets and possible mechanisms of isoliquiritigenin (ISO) in affecting chronic obstructive pulmonary disease (COPD) by regulating the glycolysis and phagocytosis of alveolar macrophages (AM). Datasets GSE130928 and GSE13896 were downloaded from the Gene Expression Omnibus (GEO) database. Genes related to glycolysis and phagocytosis phenotypes were obtained from the GeneCards and MSigDB databases, respectively. Weighted gene co-expression network analysis (WGCNA) and differential analysis were conducted on GSE130928 to identify potential target genes for COPD (gene list 1). ISO target genes were gathered from the Traditional Chinese Medicine System Pharmacology (TCMSP) database, as well as the Comparative Toxicogenomic Database (CTD) and PubChem databases (gene list 2). COPD-related targets were gathered from the CTD and GeneCards databases, and the predicted targets of COPD were obtained by taking the intersection of these sources (gene list 3). From the three gene lists, key pathways were identified. The protein-protein interaction (PPI) network was created by extracting the common genes found in all key pathways and ISO targets. Candidate therapeutic targets were identified using the Minimum Common Oncology Data Element (MCODE) algorithm. These targets were then intersected with glycolysis and phagocytic phenotype-associated genes. The resulting intersection underwent further screening using eight distinct machine learning methods to identify phenotype-related key therapeutic targets. Clinical diagnostic evaluations-including nomogram analysis, receiver operating characteristic (ROC) analysis, correlation studies, and inter-group expression comparisons-were subsequently performed on these key targets. The research findings were validated using the independent dataset GSE13896. Additionally, gene set enrichment analysis (GSEA) was conducted to explore their functional relevance. Immune cell profiling was performed using mRNA expression data from AM in COPD patients. Molecular docking was then carried out to predict interactions between ISO and the identified key target genes. Differential expression analysis and WGCNA module analysis identified a total of 890 potential targets for COPD. Additionally, 3265 predicted targets for COPD were obtained through the intersection of two disease databases. Database searches also yielded 142 targets for ISO. Enrichment analysis identified 20 key pathways, from which three key targets (AKT1, IFNG, and JUN) were ultimately selected based on their overlap with enriched genes, ISO targets, and glycolysis- and phagocytosis-related genes. They were also validated using external datasets. Further analysis of signaling pathways and immune cell profiles highlighted the influence of immune infiltration in COPD and underscored the critical role of macrophages in disease pathology. Molecular docking simulations predicted the binding interactions between ISO and the three key targets. AKT1, IFNG, and JUN may be the key targets of ISO in regulating glycolysis and phagocytosis to affect COPD.
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Affiliation(s)
- Sha Huang
- The First Clinical Medical College, Lanzhou University, Lanzhou 730000, China; (S.H.)
- Department of Gerontal Respiratory Medicine, The First Hospital of Lanzhou University, Lanzhou 730000, China
| | - Lulu Zhang
- The First Clinical Medical College, Lanzhou University, Lanzhou 730000, China; (S.H.)
- Department of Gerontal Respiratory Medicine, The First Hospital of Lanzhou University, Lanzhou 730000, China
| | - Xiaoju Liu
- Department of Gerontal Respiratory Medicine, The First Hospital of Lanzhou University, Lanzhou 730000, China
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Zhong X, Song J, Lei C, Wang X, Wang Y, Yu J, Dai W, Xu X, Fan J, Xia X, Zhang W. Machine learning-based screening of asthma biomarkers and related immune infiltration. FRONTIERS IN ALLERGY 2025; 6:1506608. [PMID: 39963184 PMCID: PMC11831286 DOI: 10.3389/falgy.2025.1506608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2024] [Accepted: 01/03/2025] [Indexed: 02/20/2025] Open
Abstract
Introduction Asthma has an annual increasing morbidity rate and imposes a heavy social burden on public healthcare systems. The aim of this study was to use machine learning to identify asthma-specific genes for the prediction and diagnosis of asthma. Methods Differentially expressed genes (DEGs) related to asthma were identified by examining public sequencing data from the Gene Expression Omnibus, coupled with the support vector machine recursive feature elimination and least absolute shrinkage and selection operator regression model. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene set enrichment analysis and correlation analyses between gene and immune cell levels were performed. An ovalbumin-induced asthma mouse model was established, and eukaryotic reference transcriptome high-throughput sequencing was performed to identify genes expressed in mouse lung tissues. Results Thirteen specific asthma genes were obtained from our dataset analysis (LOC100132287, CEACAM5, PRR4, CPA3, POSTN, LYPD2, TCN1, SCGB3A1, NOS2, CLCA1, TPSAB1, CST1, and C7orf26). The GO analysis demonstrated that DEGs linked to asthma were primarily related to positive regulation of guanylate cyclase activity, gpi anchor binding, peptidase activity and arginine binding. The renin-angiotensin system, arginine biosynthesis and arginine and proline metabolism were the key KEGG pathways of DEGs. Additionally, the genes CEACAM5, PRR4, CPA3, POSTN, CLCA1, and CST1 expression levels were positively associated with plasma cells and resting mast cells. The mouse model revealed elevated nos2 and clca1 expression in the asthmatic mouse group compared with that in normal mice, which was consistent with the findings in asthmatic patients. Discussion This study identified new marker genes for the prediction and diagnosis of asthma, which can be further validated and applied clinically.
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Affiliation(s)
- Xiaoying Zhong
- Allergy and Clinical Immunology Center, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
- Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
- The 2nd Ward of Pediatrics, Jinhua Maternal and Child Health Care Hospital, Jinhua, Zhejiang, China
| | - Jingjing Song
- Allergy and Clinical Immunology Center, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
- Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Changyu Lei
- Renji College, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Xiaoming Wang
- Allergy and Clinical Immunology Center, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
- Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Yufei Wang
- Allergy and Clinical Immunology Center, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
- Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Jiahui Yu
- Allergy and Clinical Immunology Center, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
- Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Wei Dai
- Allergy and Clinical Immunology Center, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
- Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Xinyi Xu
- Allergy and Clinical Immunology Center, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
- Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Junwen Fan
- Allergy and Clinical Immunology Center, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
- Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Xiaodong Xia
- Allergy and Clinical Immunology Center, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Weixi Zhang
- Allergy and Clinical Immunology Center, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
- Department of Pediatric Allergy and Immunology, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China
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Luo J, An J, Jia R, Liu C, Zhang Y. Identification and Verification of Metabolism-related Immunotherapy Features and Prognosis in Lung Adenocarcinoma. Curr Med Chem 2025; 32:1423-1441. [PMID: 38500277 DOI: 10.2174/0109298673293414240314043529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 02/21/2024] [Accepted: 03/04/2024] [Indexed: 03/20/2024]
Abstract
BACKGROUND Lung cancer is a frequent malignancy with a poor prognosis. Extensive metabolic alterations are involved in carcinogenesis and could, therefore, serve as a reliable prognostic phenotype. AIMS Our study aimed to develop a prognosis signature and explore the relationship between metabolic characteristic-related signature and immune infiltration in lung adenocarcinoma (LUAD). OBJECTIVE TCGA-LUAD and GSE31210 datasets were used as a training set and a validation set, respectively. METHODS A total of 513 LUAD samples collected from The Cancer Genome Atlas database (TCGA-LUAD) were used as a training dataset. Molecular subtypes were classified by consensus clustering, and prognostic genes related to metabolism were analyzed based on Differentially Expressed Genes (DEGs), Protein-Protein Interaction (PPI) network, the univariate/multivariate- and Lasso- Cox regression analysis. RESULTS Two molecular subtypes with significant survival differences were divided by the metabolism gene sets. The DEGs between the two subtypes were identified by integrated analysis and then used to develop an 8-gene signature (TTK, TOP2A, KIF15, DLGAP5, PLK1, PTTG1, ECT2, and ANLN) for predicting LUAD prognosis. Overexpression of the 8 genes was significantly correlated with worse prognostic outcomes. RiskScore was an independent factor that could divide LUAD patients into low- and high-risk groups. Specifically, high-risk patients had poorer prognoses and higher immune escape. The Receiver Operating Characteristic (ROC) curve showed strong performance of the RiskScore model in estimating 1-, 3- and 5-year survival in both training and validation sets. Finally, an optimized nomogram model was developed and contributed the most to the prognostic prediction in LUAD. CONCLUSION The current model could help effectively identify high-risk patients and suggest the most effective drug and treatment candidates for patients with LUAD.
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Affiliation(s)
- Junfang Luo
- Department of Geriatric Respiratory, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Jinlu An
- Department of Geriatric Respiratory, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Rongyan Jia
- Department of Anesthesiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Cong Liu
- Department of Geriatric Respiratory, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Yang Zhang
- Department of Geriatric Respiratory, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
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Li X, Liu J, Jing Z, Li S. SLC27A3 downregulation restores Th17/Treg balance and alleviates COPD via JAK2/STAT3 pathway inhibition. Allergol Immunopathol (Madr) 2025; 53:91-98. [PMID: 39786880 DOI: 10.15586/aei.v53i1.1215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2024] [Accepted: 11/26/2024] [Indexed: 01/12/2025]
Abstract
The main goal of this investigation is to find out how solute carrier family 27 member 3 (SLC27A3) is expressed in the lung tissue of mice with chronic obstructive pulmonary disease (COPD), and how it relates to lung function. A model of COPD was established by exposing organisms to cigarette smoke, followed by investigating the role of SLC27A3 in COPD through experiments conducted both in living organisms and in laboratory settings. Knockout mice lacking SLC27A3 were produced through siRNA transfection to investigate lung function and inflammatory response, using methods such as hematoxylin-eosin staining and enzyme-linked immunosorbent assay. Western blotting was carried out to analyze the expression of SLC27A3. Naïve CD4+ T-cells were stimulated with anti-CD3, anti-CD28, transforming growth factor (TGF)-β, and/or interleukin (IL)-6, and their differentiation into Th17 or Treg cells was promoted, as assessed by flow cytometry. The pathway expression of JAK2/STAT3 was detected using Western blotting. Mice with COPD that had higher expression levels of SLC27A3 in their lung tissue display abnormalities in lung architecture and function, as well as an imbalance between Th17 and Tregs and an elevated inflammatory response. In COPD mice with SLC27A3 knockdown, the JAK2/STAT3 pathway was repressed, lung inflammation was decreased, Th17/Treg balance was improved, and lung functioning was improved. In conclusion, the findings of this study suggest that downregulating SLC27A3 has the potential to attenuate the inflammatory response, mitigate COPD progression, and rebalance the Th17/Treg ratio by inhibiting the JAK2/STAT3 signaling pathway. These results lay a foundation for utilizing SLC27A3 as a potential therapeutic target to modulate the JAK2/STAT3 pathway for the treatment of COPD, with the aim of enhancing lung function, reducing inflammation, and restoring Th17/Treg equilibrium in a clinical context.
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Affiliation(s)
- Xiaoping Li
- Department of Geriatric Medicine, Qinghai University Affiliated Hospital, Xining, Qinghai, China
| | - Ji Liu
- Department of Geriatric Medicine, Qinghai University Affiliated Hospital, Xining, Qinghai, China;
| | - Zehui Jing
- Department of Geriatric Medicine, Qinghai University Affiliated Hospital, Xining, Qinghai, China
| | - Shuxia Li
- Department of Geriatric Medicine, Qinghai University Affiliated Hospital, Xining, Qinghai, China
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Wei S, Zhou J, Dong B. A novel risk model consisting of nine platelet-related gene signatures for predicting prognosis, immune features and drug sensitivity in glioma. Hereditas 2024; 161:52. [PMID: 39707577 DOI: 10.1186/s41065-024-00355-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2024] [Accepted: 12/11/2024] [Indexed: 12/23/2024] Open
Abstract
BACKGROUND Glioma is a malignancy with challenging clinical treatment and poor prognosis. Platelets are closely associated with tumor growth, propagation, invasion, and angiogenesis. However, the role of platelet-related genes in glioma treatment and prognosis remains unclear. RESULTS A prognostic risk model was established using nine platelet-related prognostic signature genes (CAPG, CLIC1, GLB1, GNG12, KIF20A, PDIA4, SULF2, TAGLN2, and WEE1), and the risk score of samples were calculated. Subsequently, the glioma samples were divided into high- and low-risk groups based on the median values of risk scores. scRNA-seq analysis revealed that the prognostic genes were primarily located in astrocytes and natural killer cells. The immune infiltration proportions of most immune cells differed significantly between high- and low-risk groups. Moreover, we found AZD7762 as a potential candidate for glioma treatment. CONCLUSION Nine platelet-related prognostic genes identified as prognostic signatures for glioma were closely associated with the TME and may aid in directing the clinical treatment and prognosis of gliomas.
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Affiliation(s)
- Sanlin Wei
- Dalian Medical University, Dalian, Liaoning Province, 116000, China
- Department of Neurosurgery, the First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning Province, 116000, China
| | - Junke Zhou
- Department of Nephrology, the First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning Province, 116000, China
| | - Bin Dong
- Dalian Medical University, Dalian, Liaoning Province, 116000, China.
- Department of Neurosurgery, the First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning Province, 116000, China.
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Chen Y, Liang R, Zheng X, Fang D, Lu WW, Chen Y. Identification of ZNF652 as a Diagnostic and Therapeutic Target in Osteoarthritis Using Machine Learning. J Inflamm Res 2024; 17:10141-10161. [PMID: 39649418 PMCID: PMC11624598 DOI: 10.2147/jir.s488841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2024] [Accepted: 11/17/2024] [Indexed: 12/10/2024] Open
Abstract
Purpose Osteoarthritis (OA) is the most common degenerative joint disease. However, its etiology remains largely unknown. Zinc Finger Protein 652 (ZNF652) is a transcription factor implicated in various biological processes. Nevertheless, its role in OA has not been elucidated. Methods The search term "osteoarthritis" was utilized to procure transcriptome data relating to OA patients and healthy people from the Gene Expression Omnibus (GEO) database. Then a screening process was initiated to identify differentially expressed genes (DEGs). The DEGs were discerned using three distinct machine learning methods. The accuracy of these DEGs in diagnosing OA was evaluated using the Receiver Operating Characteristic (ROC) Curve. A competitive endogenous RNA (ceRNA) visualization network was established to delve into potential regulatory targets. The ZNF652 expression was confirmed in the cartilage of OA rats using quantitative reverse transcription polymerase chain reaction (qRT-PCR) and Western blotting (WB) and analyzed using an independent t-test. Results ZNF652 was identified as a DEG and exhibited the highest diagnostic value for OA according to the ROC analysis. The GO and KEGG enrichment analyses suggest that ZNF652 plays a vital role in OA development through processes including nitric oxide anabolism, macrophage proliferation, immune response, and the PI3K/Akt and the MAPK signaling pathways. The increased expression of ZNF652 in OA was validated in qRT-PCR (1.193 ± 0.005 vs 1.000 ± 0.005, p < 0.001) and WB (0.981 ± 0.055 vs 0.856 ± 0.026, p = 0.012) analysis. Conclusion ZNF652 was found to be related to OA pathogenesis and can potentially serve as a diagnostic and therapeutic target of OA. The underlying mechanism is that ZNF652 was related to nitric oxide anabolism, macrophage proliferation, various signaling pathways, and immune cells and their functions in OA. Nevertheless, the findings need to be confirmed in clinical trials and the molecular mechanism requires further study.
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Affiliation(s)
- Yeping Chen
- Department of Bone and Joint Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, People’s Republic of China
| | - Rongyuan Liang
- Department of Bone and Joint Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, People’s Republic of China
| | - Xifan Zheng
- Department of Bone and Joint Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, People’s Republic of China
| | - Dalang Fang
- Department of Thyroid and Breast Surgery, Affiliated Hospital of Youjiang Medical College of Nationalities, Baise, Guangxi, People’s Republic of China
| | - William W Lu
- Department of Orthopedics and Traumatology, The University of Hong Kong, Hong Kong, People’s Republic of China
| | - Yan Chen
- Department of Bone and Joint Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, People’s Republic of China
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10
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Xiao X, Ding Z, Shi Y, Zhang Q. Causal Role of Immune Cells in Chronic Obstructive Pulmonary Disease: A Two-Sample Mendelian Randomization Study. COPD 2024; 21:2327352. [PMID: 38573027 DOI: 10.1080/15412555.2024.2327352] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 02/27/2024] [Indexed: 04/05/2024]
Abstract
Accumulating evidence has highlighted the importance of immune cells in the pathogenesis of chronic obstructive pulmonary disease (COPD). However, the understanding of the causal association between immunity and COPD remains incomplete due to the existence of confounding variables. In this study, we employed a two-sample Mendelian randomization (MR) analysis, utilizing the genome-wide association study database, to investigate the causal association between 731 immune-cell signatures and the susceptibility to COPD from a host genetics perspective. To validate the consistency of our findings, we utilized MR analysis results of lung function data to assess directional concordance. Furthermore, we employed MR-Egger intercept tests, Cochrane's Q test, MR-PRESSO global test, and "leave-one-out" sensitivity analyses to evaluate the presence of horizontal pleiotropy, heterogeneity, and stability, respectively. Inverse variance weighting results showed that seven immune phenotypes were associated with the risk of COPD. Analyses of heterogeneity and pleiotropy analysis confirmed the reliability of MR results. These results highlight the interactions between the immune system and the lungs. Further investigations into their mechanisms are necessary and will contribute to inform targeted prevention strategies for COPD.
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Affiliation(s)
- Xinru Xiao
- Department of Respiratory and Critical Care Medicine, The Affiliated Changzhou NO.2 People's Hospital of Nanjing Medical University, Changzhou, China
| | - Ziqi Ding
- Department of Respiratory and Critical Care Medicine, The Affiliated Changzhou NO.2 People's Hospital of Nanjing Medical University, Changzhou, China
| | - Yujia Shi
- Department of Respiratory and Critical Care Medicine, The Affiliated Changzhou NO.2 People's Hospital of Nanjing Medical University, Changzhou, China
| | - Qian Zhang
- Department of Respiratory and Critical Care Medicine, The Affiliated Changzhou NO.2 People's Hospital of Nanjing Medical University, Changzhou, China
- Changzhou Medical Center, Nanjing Medical University, Changzhou, China
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11
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Shou L, He H, Wei Y, Xu X, Wang W, Zheng J. Identification of TXN and F5 as novel diagnostic gene biomarkers of the severe asthma based on bioinformatics and machine learning analysis. Autoimmunity 2024; 57:2427085. [PMID: 39531229 DOI: 10.1080/08916934.2024.2427085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2024] [Revised: 10/22/2024] [Accepted: 11/02/2024] [Indexed: 11/16/2024]
Abstract
Asthma poses a major threat to human health. The aim of this study was to identify genetic markers of severe asthma and analyze the relationship between key genes and immune infiltration. Differentially expressed genes (DEGs) were first screened by downloading the training set GSE69683 and validation set GSE137268 from the GEO dataset. SVM-RFE analysis and the LASSO regression model were used to screen key genes, and CIBERSORT was used to assess immune infiltration in the samples. A total of 20 DEGs were identified in this study, mainly enriched for lymph node-like receptors, b-cell receptors, and neutrophil extracellular trap pathway. Comparative validation set GSE137268 identified thioredoxin (TXN) and coagulation factor V (F5) were identified as diagnostic markers of severe asthma. CIBERSORT analysis revealed that TXN and F5 are associated with multiple immune cell infiltrates. In addition, we identified miRNA and TF at the transcriptional level that may regulate F5 and TXN, and found that several commonly used drugs may exert therapeutic effects by targeting F5 and TXN. Taken together, TXN and F5 may be key genes in the development of severe asthma and are associated with immune infiltration. Our study can help to better understand the pathogenesis of asthma and provide new ideas for clinical treatment.
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Affiliation(s)
- Lu Shou
- Tongde Hospital of Zhejiang Province, Pulmonary and Critical Care Medicine, Hangzhou, Zhejiang, China
| | - Haidong He
- Tongde Hospital of Zhejiang Province, Pulmonary and Critical Care Medicine, Hangzhou, Zhejiang, China
| | - Yi Wei
- Tongde Hospital of Zhejiang Province, Pulmonary and Critical Care Medicine, Hangzhou, Zhejiang, China
| | - Xianrong Xu
- Tongde Hospital of Zhejiang Province, Pulmonary and Critical Care Medicine, Hangzhou, Zhejiang, China
| | - Wenmin Wang
- The Yangtze River Delta Biological Medicine Research and Development Center of Zhejiang Province, Yangtze Delta Region Institution of Tsinghua University, Hangzhou, Zhejiang, China
| | - Jisheng Zheng
- Tongde Hospital of Zhejiang Province, Pulmonary and Critical Care Medicine, Hangzhou, Zhejiang, China
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12
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Yang X, Li Q, Wang L, Chen J, Quan Z. MUC1 and CREB3 are Hub Ferroptosis Suppressors for Nucleus Pulposus and Annulus Fibrosus Degeneration by Integrated Bioinformatics and Experimental Verification. J Inflamm Res 2024; 17:8965-8984. [PMID: 39583856 PMCID: PMC11584408 DOI: 10.2147/jir.s489052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2024] [Accepted: 11/14/2024] [Indexed: 11/26/2024] Open
Abstract
Purpose Ferroptosis is an underlying mechanism for various degenerative diseases, but its role in intervertebral disc degeneration remains elusive. This study aims to explore the key ferroptosis-related genes and its role in nucleus pulposus (NP) and annulus fibrosus (AF) degeneration. Methods We analyzed the gene expression profiles of NP and AF from the Gene Expression Omnibus database. The ferroptosis-related differentially expressed genes (FRDEGs) in degenerated NP and AF were filtered, followed by GO and KEGG analysis. Feature FRDEGs were identified by the LASSO and SVM-RFE algorithms, and then Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were conducted. Immune infiltration analysis was conducted by CIBERSORT algorithm. We established drug networks via the Drug-Gene Interaction Database and competitive endogenous RNA (ceRNA) networks via miRanda, miRDB, and TargetScan database. The expression levels of the feature FRDEGs were assessed by the validation sets, single-cell RNA-seq, and experimental verification. Results A total of 15 and 18 FRDEGs were obtained for NP and AF, respectively. GO and KEGG analysis revealed their implication in oxidative stress. Four (AKR1C1, AKR1C3, MUC1, ENPP2) and five (SCP2, ABCC1, KLF2, IDO1, CREB3) feature genes were identified for NP and AF, respectively. The GSEA and GSVA analysis showed that these feature genes were enriched in lots of biological functions, including immune response. CREB3 in degenerated AF was negatively correlated with Eosinophils via CIBERSORT algorithm. The drugs and ceRNAs targeting CREB3 and MUC1 were identified. Experimental verification and single-cell RNA-seq analysis revealed that MUC1 and CREB3 were downregulated in degenerated NP and AF, respectively. Conclusion MUC1 and CREB3 were considered novel biomarkers for NP and AF ferroptosis, respectively. Drug and ceRNA networks were constructed for future drug development and investigation of new mechanisms of ferroptosis.
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Affiliation(s)
- Xinyu Yang
- Department of Orthopedics, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, People’s Republic of China
- Orthopedic Laboratory of Chongqing Medical University, Chongqing, 400016, People’s Republic of China
| | - Qiaochu Li
- Department of Orthopedics, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, People’s Republic of China
- Orthopedic Laboratory of Chongqing Medical University, Chongqing, 400016, People’s Republic of China
| | - Linbang Wang
- Department of Orthopedics, Peking University Third Hospital, Beijing, 100191, People’s Republic of China
| | - Jiaxing Chen
- Department of Orthopedics, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, People’s Republic of China
- Orthopedic Laboratory of Chongqing Medical University, Chongqing, 400016, People’s Republic of China
| | - Zhengxue Quan
- Department of Orthopedics, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, People’s Republic of China
- Orthopedic Laboratory of Chongqing Medical University, Chongqing, 400016, People’s Republic of China
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Wei GH, Wei XY, Fan LY, Zhou WZ, Sun M, Zhu CD. Comprehensive assessment of the association between tumor-infiltrating immune cells and the prognosis of renal cell carcinoma. World J Clin Oncol 2024; 15:1280-1292. [DOI: 10.5306/wjco.v15.i10.1280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2024] [Revised: 08/07/2024] [Accepted: 08/13/2024] [Indexed: 09/29/2024] Open
Abstract
BACKGROUND According to current statistics, renal cancer accounts for 3% of all cancers worldwide. Renal cell carcinoma (RCC) is the most common solid lesion in the kidney and accounts for approximately 90% of all renal malignancies. Increasing evidence has shown an association between immune infiltration in RCC and clinical outcomes. To discover possible targets for the immune system, we investigated the link between tumor-infiltrating immune cells (TIICs) and the prognosis of RCC.
AIM To investigate the effects of 22 TIICs on the prognosis of RCC patients and identify potential therapeutic targets for RCC immunotherapy.
METHODS The CIBERSORT algorithm partitioned the 22 TIICs from the Cancer Genome Atlas cohort into proportions. Cox regression analysis was employed to evaluate the impact of 22 TIICs on the probability of developing RCC. A predictive model for immunological risk was developed by analyzing the statistical relationship between the subpopulations of TIICs and survival outcomes. Furthermore, multivariate Cox regression analysis was used to investigate independent factors for the prognostic prediction of RCC. A value of P < 0.05 was regarded as statistically significant.
RESULTS Compared to normal tissues, RCC tissues exhibited a distinct infiltration of immune cells. An immune risk score model was established and univariate Cox regression analysis revealed a significant association between four immune cell types and the survival risk connected to RCC. High-risk individuals were correlated to poorer outcomes according to the Kaplan-Meier survival curve (P = 1E−05). The immunological risk score model was demonstrated to be a dependable predictor of survival risk (area under the curve = 0.747) via the receiver operating characteristic curve. According to multivariate Cox regression analysis, the immune risk score model independently predicted RCC patients' prognosis (hazard ratio = 1.550, 95%CI: 1.342–1.791; P < 0.001). Finally, we established a nomogram that accurately and comprehensively forecast the survival of patients with RCC.
CONCLUSION TIICs play various roles in RCC prognosis. The immunological risk score is an independent predictor of poor survival in kidney cancer cases.
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Affiliation(s)
- Guo-Hao Wei
- Department of Oncology, The Second Hospital of Nanjing, Affiliated to Nanjing University of Chinese Medicine, Nanjing 210003, Jiangsu Province, China
| | - Xi-Yi Wei
- The State Key Laboratory of Reproductive, Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210003, Jiangsu Province, China
| | - Ling-Yao Fan
- Department of Oncology, The Second Hospital of Nanjing, Affiliated to Nanjing University of Chinese Medicine, Nanjing 210003, Jiangsu Province, China
| | - Wen-Zheng Zhou
- Department of Oncology, The Second Hospital of Nanjing, Affiliated to Nanjing University of Chinese Medicine, Nanjing 210003, Jiangsu Province, China
| | - Ming Sun
- Department of Oncology, The Second Hospital of Nanjing, Affiliated to Nanjing University of Chinese Medicine, Nanjing 210003, Jiangsu Province, China
| | - Chuan-Dong Zhu
- Department of Oncology, The Second Hospital of Nanjing, Affiliated to Nanjing University of Chinese Medicine, Nanjing 210003, Jiangsu Province, China
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14
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Wang W, Chen G, Zhang W, Zhang X, Huang M, Li C, Wang L, Lu Z, Xia J. The crucial prognostic signaling pathways of pancreatic ductal adenocarcinoma were identified by single-cell and bulk RNA sequencing data. Hum Genet 2024; 143:1109-1129. [PMID: 38526745 PMCID: PMC11485037 DOI: 10.1007/s00439-024-02663-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Accepted: 02/24/2024] [Indexed: 03/27/2024]
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a malignant tumor with poor prognosis and high mortality. Although a large number of studies have explored its potential prognostic markers using traditional RNA sequencing (RNA-Seq) data, they have not achieved good prediction effect. In order to explore the possible prognostic signaling pathways leading to the difference in prognosis, we identified differentially expressed genes from one scRNA-seq cohort and four GEO cohorts, respectively. Then Cox and Lasso regression analysis showed that 12 genes were independent prognostic factors for PDAC. AUC and calibration curve analysis showed that the prognostic model had good discrimination and calibration. Compared with the low-risk group, the high-risk group had a higher proportion of gene mutations than the low-risk group. Immune infiltration analysis revealed differences in macrophages and monocytes between the two groups. Prognosis related genes were mainly distributed in fibroblasts, macrophages and type 2 ducts. The results of cell communication analysis showed that there was a strong communication between cancer-associated fibroblasts (CAF) and type 2 ductal cells, and collagen formation was the main interaction pathway.
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Affiliation(s)
- Wenwen Wang
- Department of Health Statistics, School of Military Preventive Medicine, Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, Fourth Military Medical University, Xi'an, 710032, Shaanxi Province, China.
| | - Guo Chen
- Shaanxi Provincial Key Laboratory of Infection and Immune Diseases, Shaanxi Provincial People's Hospital, Xi'an, 710068, Shaanxi Province, China
| | - Wenli Zhang
- Shaanxi Provincial Key Laboratory of Infection and Immune Diseases, Shaanxi Provincial People's Hospital, Xi'an, 710068, Shaanxi Province, China
| | - Xihua Zhang
- Department of Health Statistics, School of Military Preventive Medicine, Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, Fourth Military Medical University, Xi'an, 710032, Shaanxi Province, China
| | - Manli Huang
- Department of Health Statistics, School of Military Preventive Medicine, Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, Fourth Military Medical University, Xi'an, 710032, Shaanxi Province, China
| | - Chen Li
- Department of Health Statistics, School of Military Preventive Medicine, Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, Fourth Military Medical University, Xi'an, 710032, Shaanxi Province, China
| | - Ling Wang
- Department of Health Statistics, School of Military Preventive Medicine, Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, Fourth Military Medical University, Xi'an, 710032, Shaanxi Province, China
| | - Zifan Lu
- Shaanxi Provincial Key Laboratory of Infection and Immune Diseases, Shaanxi Provincial People's Hospital, Xi'an, 710068, Shaanxi Province, China
| | - Jielai Xia
- Department of Health Statistics, School of Military Preventive Medicine, Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, Fourth Military Medical University, Xi'an, 710032, Shaanxi Province, China
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15
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Ma X, Sun Y, Li C, Wang M, Zang Q, Zhang X, Wang F, Niu Y, Hua J. Novel Insights Into DLAT's Role in Alzheimer's Disease-Related Copper Toxicity Through Microglial Exosome Dynamics. CNS Neurosci Ther 2024; 30:e70064. [PMID: 39428563 PMCID: PMC11491298 DOI: 10.1111/cns.70064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2024] [Revised: 08/10/2024] [Accepted: 09/03/2024] [Indexed: 10/22/2024] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is a complex neurodegenerative disorder, with recent research emphasizing the roles of microglia and their secreted extracellular vesicles in AD pathology. However, the involvement of specific molecular pathways contributing to neuronal death in the context of copper toxicity remains largely unexplored. OBJECTIVE This study investigates the interaction between pyruvate kinase M2 (PKM2) and dihydrolipoamide S-acetyltransferase (DLAT), particularly focusing on copper-induced neuronal death in Alzheimer's disease. METHODS Gene expression datasets were analyzed to identify key factors involved in AD-related copper toxicity. The role of DLAT was validated using 5xFAD transgenic mice, while in vitro experiments were conducted to assess the impact of microglial exosomes on neuronal PKM2 transfer and DLAT expression. The effects of inhibiting the PKM2 transfer via microglial exosomes on DLAT expression and copper-induced neuronal death were also evaluated. RESULTS DLAT was identified as a critical factor in the pathology of AD, particularly in copper toxicity. In 5xFAD mice, increased DLAT expression was linked to hippocampal damage and cognitive decline. In vitro, microglial exosomes were shown to facilitate the transfer of PKM2 to neurons, leading to upregulation of DLAT expression and increased copper-induced neuronal death. Inhibition of PKM2 transfer via exosomes resulted in a significant reduction in DLAT expression, mitigating neuronal death and slowing AD progression. CONCLUSION This study uncovers a novel pathway involving microglial exosomes and the PKM2-DLAT interaction in copper-induced neuronal death, providing potential therapeutic targets for Alzheimer's disease. Blocking PKM2 transfer could offer new strategies for reducing neuronal damage and slowing disease progression in AD.
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Affiliation(s)
- Xiang Ma
- Laboratory of Biochemistry and PharmacyTaiyuan Institute of TechnologyTaiyuanP. R. China
| | - Yusheng Sun
- Laboratory of Biochemistry and PharmacyTaiyuan Institute of TechnologyTaiyuanP. R. China
| | - Changchun Li
- Department of Chemistry and Chemical EngineeringTaiyuan Institute of TechnologyTaiyuanP. R. China
| | - Man Wang
- Laboratory of Biochemistry and PharmacyTaiyuan Institute of TechnologyTaiyuanP. R. China
| | - Qijiao Zang
- Laboratory of Biochemistry and PharmacyTaiyuan Institute of TechnologyTaiyuanP. R. China
| | - Xuxia Zhang
- Laboratory of Biochemistry and PharmacyTaiyuan Institute of TechnologyTaiyuanP. R. China
| | - Feng Wang
- Department of Chemistry and Chemical EngineeringTaiyuan Institute of TechnologyTaiyuanP. R. China
| | - Yulan Niu
- Department of Chemistry and Chemical EngineeringTaiyuan Institute of TechnologyTaiyuanP. R. China
| | - Jiai Hua
- Laboratory of Biochemistry and PharmacyTaiyuan Institute of TechnologyTaiyuanP. R. China
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16
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Wang H, Zhang Z, Cheng X, Hou Z, Wang Y, Liu Z, Gao Y. Machine learning algorithm-based biomarker exploration and validation of mitochondria-related diagnostic genes in osteoarthritis. PeerJ 2024; 12:e17963. [PMID: 39282111 PMCID: PMC11397131 DOI: 10.7717/peerj.17963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 08/01/2024] [Indexed: 09/18/2024] Open
Abstract
The role of mitochondria in the pathogenesis of osteoarthritis (OA) is significant. In this study, we aimed to identify diagnostic signature genes associated with OA from a set of mitochondria-related genes (MRGs). First, the gene expression profiles of OA cartilage GSE114007 and GSE57218 were obtained from the Gene Expression Omnibus. And the limma method was used to detect differentially expressed genes (DEGs). Second, the biological functions of the DEGs in OA were investigated using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Wayne plots were employed to visualize the differentially expressed mitochondrial genes (MDEGs) in OA. Subsequently, the LASSO and SVM-RFE algorithms were employed to elucidate potential OA signature genes within the set of MDEGs. As a result, GRPEL and MTFP1 were identified as signature genes. Notably, GRPEL1 exhibited low expression levels in OA samples from both experimental and test group datasets, demonstrating high diagnostic efficacy. Furthermore, RT-qPCR analysis confirmed the reduced expression of Grpel1 in an in vitro OA model. Lastly, ssGSEA analysis revealed alterations in the infiltration abundance of several immune cells in OA cartilage tissue, which exhibited correlation with GRPEL1 expression. Altogether, this study has revealed that GRPEL1 functions as a novel and significant diagnostic indicator for OA by employing two machine learning methodologies. Furthermore, these findings provide fresh perspectives on potential targeted therapeutic interventions in the future.
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Affiliation(s)
- Hongbo Wang
- Department of Urology Surgery, Lanzhou University Second Hospital, Lanzhou, Gansu, China
- Department of Surgery of Spine and Spinal Cord, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Zongye Zhang
- Department of Surgery of Spine and Spinal Cord, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Xingbo Cheng
- Department of Surgery of Spine and Spinal Cord, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Zhenxing Hou
- Department of Surgery of Spine and Spinal Cord, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Yubo Wang
- School of Basic Medicine and Forensic Medicine, Henan University of Science & Technology, Luoyang, Henan, China
| | - Zhendong Liu
- Department of Surgery of Spine and Spinal Cord, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Yanzheng Gao
- Department of Surgery of Spine and Spinal Cord, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Zhengzhou, Henan, China
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Wang M, Peng J, Yang M, Chen J, Shen Y, Liu L, Chen L. Elevated expression of NLRP3 promotes cigarette smoke-induced airway inflammation in chronic obstructive pulmonary disease. Arch Med Sci 2024; 20:1281-1293. [PMID: 39439673 PMCID: PMC11493075 DOI: 10.5114/aoms/176805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 12/11/2023] [Indexed: 10/25/2024] Open
Abstract
Introduction NOD-like receptor protein 3 (NLRP3) is implicated in chronic obstructive pulmonary disease (COPD) pathogenesis. Here, we explored the role of NLRP3 in cigarette smoke (CS)-induced airway inflammation in COPD. Material and methods NLRP3 expression level was assessed with the microarray data in GEO datasets and validated in serum by ELISA from a case-control cohort. Male C57BL/6J mice were randomly divided into: saline, CS, MCC950 (a specific NLRP3 inhibitor, 10 mg/kg) and CS + MCC950 (5 mg/kg and 10 mg/kg) groups (n = 5 per group). All mice were exposed to CS or air for 4 weeks. Then, broncho-alveolar lavage (BAL) fluid and lung tissues were collected for cell counting, ELISA, HE staining and RNA sequencing with validation by real-time qPCR. Results Compared to non-smokers, NLRP3 expression was significantly elevated in the lung tissues and sera of COPD smokers. CS remarkably induced airway inflammation in mice, characterized by an increase of inflammatory cells and proinflammatory cytokines in BAL fluid and HE inflammatory score, which were ameliorated by MCC950 treatment dose-dependently. Subsequently, 84 candidate genes were selected following RNA sequencing, and five hub genes (Mmp9, IL-1α, Cxcr2, Cxcl10, Ccr1) were then identified by PPI and MCODE analyses, which were confirmed by real-time qPCR. GO and KEGG analysis suggested that the five genes were enriched in a complicated network of inflammatory processes and signaling pathways. Conclusions NLRP3 expression is elevated in lungs and sera of COPD smokers. Inhibition of NLRP3 significantly attenuates CS-induced airway inflammation in mice via inactivation of multiple hub genes and their related inflammatory processes and signaling pathways.
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Affiliation(s)
- Min Wang
- Department of Pulmonary and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Junjie Peng
- Department of Pulmonary and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Mei Yang
- Department of Pulmonary and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Jun Chen
- Lab of Pulmonary Diseases, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yongchun Shen
- Department of Pulmonary and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Lin Liu
- Department of Pulmonary and Critical Care Medicine, 363 Hospital, Chengdu, Sichuan, China
| | - Lei Chen
- Department of Pulmonary and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
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18
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Shen HH, Zhang YY, Wang XY, Li MY, Liu ZX, Wang Y, Ye JF, Wu HH, Li MQ. Validation of mitochondrial biomarkers and immune dynamics in polycystic ovary syndrome. Am J Reprod Immunol 2024; 91:e13847. [PMID: 38661639 DOI: 10.1111/aji.13847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2023] [Revised: 03/26/2024] [Accepted: 04/09/2024] [Indexed: 04/26/2024] Open
Abstract
PROBLEM Polycystic ovary syndrome (PCOS), a prevalent endocrine-metabolic disorder, presents considerable therapeutic challenges due to its complex and elusive pathophysiology. METHOD OF STUDY We employed three machine learning algorithms to identify potential biomarkers within a training dataset, comprising GSE138518, GSE155489, and GSE193123. The diagnostic accuracy of these biomarkers was rigorously evaluated using a validation dataset using area under the curve (AUC) metrics. Further validation in clinical samples was conducted using PCR and immunofluorescence techniques. Additionally, we investigate the complex interplay among immune cells in PCOS using CIBERSORT to uncover the relationships between the identified biomarkers and various immune cell types. RESULTS Our analysis identified ACSS2, LPIN1, and NR4A1 as key mitochondria-related biomarkers associated with PCOS. A notable difference was observed in the immune microenvironment between PCOS patients and healthy controls. In particular, LPIN1 exhibited a positive correlation with resting mast cells, whereas NR4A1 demonstrated a negative correlation with monocytes in PCOS patients. CONCLUSION ACSS2, LPIN1, and NR4A1 emerge as PCOS-related diagnostic biomarkers and potential intervention targets, opening new avenues for the diagnosis and management of PCOS.
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Affiliation(s)
- Hui-Hui Shen
- Institute of Obstetrics and Gynecology, Hospital of Obstetrics and Gynecology, Fudan University, Shanghai, People's Republic of China
- Laboratory for Reproductive Immunology, Hospital of Obstetrics and Gynecology, Fudan University, Shanghai, People's Republic of China
| | - Yang-Yang Zhang
- Institute of Obstetrics and Gynecology, Hospital of Obstetrics and Gynecology, Fudan University, Shanghai, People's Republic of China
- Shanghai Medical College, Fudan University, Shanghai, People's Republic of China
| | - Xuan-Yu Wang
- College of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, People's Republic of China
| | - Meng-Ying Li
- Institute of Obstetrics and Gynecology, Hospital of Obstetrics and Gynecology, Fudan University, Shanghai, People's Republic of China
| | - Zhen-Xing Liu
- Center of Reproduction and Genetics, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, People's Republic of China
| | - Ying Wang
- Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Qilu Hospital, Shandong University, Ji'nan, Shandong, People's Republic of China
| | - Jiang-Feng Ye
- Institute for Molecular and Cell Biology, Agency for Science, Technology and Research, Singapore, Singapore
| | - Hui-Hua Wu
- Center of Reproduction and Genetics, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, People's Republic of China
| | - Ming-Qing Li
- Institute of Obstetrics and Gynecology, Hospital of Obstetrics and Gynecology, Fudan University, Shanghai, People's Republic of China
- Laboratory for Reproductive Immunology, Hospital of Obstetrics and Gynecology, Fudan University, Shanghai, People's Republic of China
- Shanghai Key Laboratory of Female Reproductive Endocrine Related Diseases, Hospital of Obstetrics and Gynecology, Fudan University, Shanghai, People's Republic of China
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Zhang Y, Li G. Predicting feature genes correlated with immune infiltration in patients with abdominal aortic aneurysm based on machine learning algorithms. Sci Rep 2024; 14:5157. [PMID: 38431726 PMCID: PMC10908806 DOI: 10.1038/s41598-024-55941-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 02/29/2024] [Indexed: 03/05/2024] Open
Abstract
Abdominal aortic aneurysm (AAA) is a condition characterized by a pathological and progressive dilatation of the infrarenal abdominal aorta. The exploration of AAA feature genes is crucial for enhancing the prognosis of AAA patients. Microarray datasets of AAA were downloaded from the Gene Expression Omnibus database. A total of 43 upregulated differentially expressed genes (DEGs) and 32 downregulated DEGs were obtained. Function, pathway, disease, and gene set enrichment analyses were performed, in which enrichments were related to inflammation and immune response. AHR, APLNR, ITGA10 and NR2F6 were defined as feature genes via machine learning algorithms and a validation cohort, which indicated high diagnostic abilities by the receiver operating characteristic curves. The cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) method was used to quantify the proportions of immune infiltration in samples of AAA and normal tissues. We have predicted AHR, APLNR, ITGA10 and NR2F6 as feature genes of AAA. CD8 + T cells and M2 macrophages correlated with these genes may be involved in the development of AAA, which have the potential to be developed as risk predictors and immune interventions.
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Affiliation(s)
- Yufeng Zhang
- Department of Vascular Surgery, The Second Affiliated Hospital of Shandong First Medical University, Tai'an, 271000, Shandong, China
- Postdoctoral Workstation, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, 250021, Shandong, China
- Department of Pulmonary and Critical Care Medicine, Jiangyin Hospital of Traditional Chinese Medicine, Jiangyin Hospital Affiliated to Nanjing University of Chinese Medicine, Jiangyin, 214400, Jiangsu, China
| | - Gang Li
- Department of Vascular Surgery, The Second Affiliated Hospital of Shandong First Medical University, Tai'an, 271000, Shandong, China.
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Li S, Zhang J, Hou X, Wang Y, Li T, Xu Z, Chen F, Zhou Y, Wang W, Liu M. Prediction Model for Unfavorable Outcome in Spontaneous Intracerebral Hemorrhage Based on Machine Learning. J Korean Neurosurg Soc 2024; 67:94-102. [PMID: 37661087 PMCID: PMC10788551 DOI: 10.3340/jkns.2023.0118] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 08/05/2023] [Accepted: 08/21/2023] [Indexed: 09/05/2023] Open
Abstract
OBJECTIVE The spontaneous intracerebral hemorrhage (ICH) remains a significant cause of mortality and morbidity throughout the world. The purpose of this retrospective study is to develop multiple models for predicting ICH outcomes using machine learning (ML). METHODS Between January 2014 and October 2021, we included ICH patients identified by computed tomography or magnetic resonance imaging and treated with surgery. At the 6-month check-up, outcomes were assessed using the modified Rankin Scale. In this study, four ML models, including Support Vector Machine (SVM), Decision Tree C5.0, Artificial Neural Network, Logistic Regression were used to build ICH prediction models. In order to evaluate the reliability and the ML models, we calculated the area under the receiver operating characteristic curve (AUC), specificity, sensitivity, accuracy, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR). RESULTS We identified 71 patients who had favorable outcomes and 156 who had unfavorable outcomes. The results showed that the SVM model achieved the best comprehensive prediction efficiency. For the SVM model, the AUC, accuracy, specificity, sensitivity, PLR, NLR, and DOR were 0.91, 0.92, 0.92, 0.93, 11.63, 0.076, and 153.03, respectively. For the SVM model, we found the importance value of time to operating room (TOR) was higher significantly than other variables. CONCLUSION The analysis of clinical reliability showed that the SVM model achieved the best comprehensive prediction efficiency and the importance value of TOR was higher significantly than other variables.
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Affiliation(s)
- Shengli Li
- Department of Neurosurgery, Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), Qingdao, China
| | - Jianan Zhang
- Department of Anesthesia Operating Room, Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), Qingdao, China
| | - Xiaoqun Hou
- Department of Neurosurgery, Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), Qingdao, China
| | - Yongyi Wang
- Department of Neurosurgery, Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), Qingdao, China
| | - Tong Li
- Department of Neurosurgery, Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), Qingdao, China
| | - Zhiming Xu
- Department of Neurosurgery, Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), Qingdao, China
| | - Feng Chen
- Department of Neurosurgery, Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), Qingdao, China
| | - Yong Zhou
- Department of Neurosurgery, Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), Qingdao, China
| | - Weimin Wang
- Department of Neurosurgery, Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), Qingdao, China
| | - Mingxing Liu
- Department of Neurosurgery, Qingdao Hospital, University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital), Qingdao, China
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Zhang W, Zhao Y, Tian Y, Liang X, Piao C. Early Diagnosis of High-Risk Chronic Obstructive Pulmonary Disease Based on Quantitative High-Resolution Computed Tomography Measurements. Int J Chron Obstruct Pulmon Dis 2023; 18:3099-3114. [PMID: 38162987 PMCID: PMC10757779 DOI: 10.2147/copd.s436803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 12/15/2023] [Indexed: 01/03/2024] Open
Abstract
Purpose Quantitative computed tomography (QCT) techniques, focusing on airway anatomy and emphysema, may help to detect early structural changes of COPD disease. This retrospective study aims to identify high-risk COPD participants by using QCT measurements. Patients and Methods We enrolled 140 participants from the Second Affiliated Hospital of Shenyang Medical College who completed inspiratory high-resolution CT scans, pulmonary function tests (PFTs), and clinical characteristics recorded. They were diagnosed Non-COPD by PFT value of FEV1/FVC >70% and divided into two groups according percentage predicted FEV1 (FEV1%), low-risk COPD group: FEV1% ≥ 95%, high-risk group: 80% < FEV1% < 95%. The QCT measurements were analyzed by the Student's t-test (or Mann-Whitney U-test) method. Then, feature candidates were identified using the LASSO method. Meanwhile, the correlation between QCT measurements and PFTs was assessed by the Spearman rank correlation test. Furthermore, support vector machine (SVM) was performed to identify high-risk COPD participants. The performance of the models was evaluated in terms of accuracy (ACC), sensitivity (SEN), specificity (SPE), F1-score, and area under the ROC curve (AUC), with p <0.05 considered statistically significant. Results The SVM based on QCT measurements achieved good performance in identifying high-risk COPD patients with 85.71% of ACC, 88.34% of SEN, 84.00% of SPE, 83.33% of F1-score, and 0.93 of AUC. Further, QCT measurements integration of clinical data improved the performance with an ACC of 90.48%. The emphysema index (%LAA-950) of left lower lung was negatively correlated with PFTs (P < 0.001). The airway anatomy indexes of lumen diameter (LD) were correlated with PFTs. Conclusion QCT measurements combined with clinical information could provide an effective tool for an early diagnosis of high-risk COPD. The QCT indexes can be used to assess the pulmonary function status of high-risk COPD.
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Affiliation(s)
- Wenxiu Zhang
- Institute of Research and Clinical Innovations, Neusoft Medical Systems Co, Ltd, Shanghai, People’s Republic of China
| | - Yu Zhao
- Radiology Department, Second Affiliated Hospital of Shenyang Medical College, Shenyang, Liaoning, People’s Republic of China
| | - Yuchi Tian
- Institute of Research and Clinical Innovations, Neusoft Medical Systems Co, Ltd, Shanghai, People’s Republic of China
| | - Xiaoyun Liang
- Institute of Research and Clinical Innovations, Neusoft Medical Systems Co, Ltd, Shanghai, People’s Republic of China
| | - Chenghao Piao
- Radiology Department, Second Affiliated Hospital of Shenyang Medical College, Shenyang, Liaoning, People’s Republic of China
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Zhang Z, Yu H, Wang Q, Ding Y, Wang Z, Zhao S, Bian T. A Macrophage-Related Gene Signature for Identifying COPD Based on Bioinformatics and ex vivo Experiments. J Inflamm Res 2023; 16:5647-5665. [PMID: 38050560 PMCID: PMC10693783 DOI: 10.2147/jir.s438308] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 11/21/2023] [Indexed: 12/06/2023] Open
Abstract
Background This study aims to investigate the association between immune cells and the development of COPD, while providing a new method for the diagnosis of COPD according to the changes in immune microenvironment. Methods In this study, the "CIBERSORT" algorithm was used to estimate the tissue infiltration of 22 types of immune cells in GSE20257 and GSE10006. The "limma" package was used for differentially expressed analysis. The key modules associated with vital immune cells were identified using WGCNA. GO and KEGG enrichment analysis revealed the biological functions of the candidate genes. Ultimately, a novel diagnostic prediction model was constructed via machine learning methods and multivariate logistic regression analysis based on GSE20257. Furthermore, we examined the stability of the model on one internal test set (GSE10006), three external test sets (GSE8545, GSE57148 and GSE76925), one single-cell transcriptome dataset (GSE167295), macrophages (THP-M cells) and lung tissue from COPD patients. Results M0 macrophages (AUC > 0.7 in GSE20257 and GSE10006) were considered as the most important immune cells through exploring the immune microenvironment landscapes in COPD patients and healthy controls. The differentially expressed genes from GSE20257 and GSE10006 were divided into six and five modules via WGCNA, respectively. The green module in GSE20257 (cor = 0.41, P < 0.001) and the brown module in GSE10006 (cor = 0.67, P < 0.001) were highly correlated with M0 macrophages and were selected as key modules. Forty-one intersected genes obtained from two modules were primarily involved in regulation of cytokine production, regulation of innate immune response, specific granule, phagosome, lysosome, ferroptosis, and other biological processes. On the basis of the candidate genetic markers further characterized via the "Boruta" and "LASSO" algorithm for COPD, a diagnostic model comprising CLEC5A, FTL and SLC2A3 was constructed, which could accurately distinguish COPD patients from healthy controls in multiple datasets. GSE20257 as the training set has an AUC of 0.916. The AUCs of the internal test set and three external test sets were 0.873, 0.932, 0.675 and 0.688, respectively. Single-cell sequencing analysis suggested that CLEC5A, FTL and SLC2A3 were expressed in macrophages from COPD patients. The expressions of CLEC5A, FTL and SLC2A3 were up-regulated in THP-M cells and lung tissue from COPD patients. Conclusion According to the variations of immune microenvironment in COPD patients, we constructed and validated a novel macrophage M0-associated diagnostic model with satisfactory predictive value. CLEC5A, FTL and SLC2A3 are expected to be promising targets of immunotherapy in COPD.
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Affiliation(s)
- Zheming Zhang
- Wuxi Medical Center of Nanjing Medical University, Wuxi, People’s Republic of China
- Department of Respiratory Medicine, Wuxi People’s Hospital, Nanjing Medical University, Wuxi, People’s Republic of China
| | - Haoda Yu
- Wuxi Medical Center of Nanjing Medical University, Wuxi, People’s Republic of China
- Department of Respiratory Medicine, Wuxi People’s Hospital, Nanjing Medical University, Wuxi, People’s Republic of China
| | - Qi Wang
- Department of Gastroenterology, Affiliated Hospital of Jiangsu University, Zhenjiang, People’s Republic of China
| | - Yu Ding
- Wuxi Medical Center of Nanjing Medical University, Wuxi, People’s Republic of China
- Department of Respiratory Medicine, Wuxi People’s Hospital, Nanjing Medical University, Wuxi, People’s Republic of China
| | - Ziteng Wang
- Wuxi Medical Center of Nanjing Medical University, Wuxi, People’s Republic of China
- Department of Respiratory Medicine, Wuxi People’s Hospital, Nanjing Medical University, Wuxi, People’s Republic of China
| | - Songyun Zhao
- Wuxi Medical Center of Nanjing Medical University, Wuxi, People’s Republic of China
| | - Tao Bian
- Wuxi Medical Center of Nanjing Medical University, Wuxi, People’s Republic of China
- Department of Respiratory Medicine, Wuxi People’s Hospital, Nanjing Medical University, Wuxi, People’s Republic of China
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Zhou X, Tan F, Zhang S, Zhang T. Combining single-cell RNA sequencing data and transcriptomic data to unravel potential mechanisms and signature genes of the progression of idiopathic pulmonary fibrosis to lung adenocarcinoma and predict therapeutic agents. Funct Integr Genomics 2023; 23:346. [PMID: 37996625 DOI: 10.1007/s10142-023-01274-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 10/29/2023] [Accepted: 11/17/2023] [Indexed: 11/25/2023]
Abstract
Patients with idiopathic pulmonary fibrosis (IPF) have a significantly higher prevalence of lung adenocarcinoma (LUAD) than normal subjects, although the underlying association is unclear. The raw data involved were obtained from the Gene Expression Omnibus (GEO) database. Differential expression analysis and weighted gene co-expression network analysis were used to screen for differentially expressed genes (DEGs) and modular signature genes (MSGs). Genes intersecting DEGs and MSGs were considered hub genes for IPF and LUAD. Machine learning algorithms were applied to capture epithelial cell-derived signature genes (EDSGs) shared. External cohort data were exploited to validate the robustness of EDSGs. Immunohistochemical staining and K-M plots were used to denote the prognostic value of EDSGs in LUAD. Based on EDSGs, we constructed a TF-gene-miRNA regulatory network. Molecular docking can validate the strength of action between candidate drugs and EDSGs. Epithelial cells, 650 DEGs, and 1773 MSGs were shared by IPF and LUAD. As for 379 hub genes, we performed pathway and functional enrichment analysis. By analyzing sc-RNA seq data, we identified 1234 marker genes of IPF epithelial cell-derived and 1481 of LUAD. And these genes shared 8 items with 379 hub genes. Through the machine learning algorithms, we further fished TRIM2, S100A14, CYP4B1, LMO7, and SFN. The ROC curves emphasized the significance of EDSGs in predicting the onset of LUAD and IPF. The TF-gene-miRNA network revealed regulatory relationships behind EDSGs. Finally, we predicted appropriate therapeutic agents. Our study preliminarily identified potential mechanisms between IPF and LUAD, which will inform subsequent studies.
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Affiliation(s)
- Xianqiang Zhou
- Department of Traditional Chinese Medicine, Jing'an District Central Hospital Affiliated to Fudan University, Shanghai, 200040, China
- Department of Pulmonary Diseases, Jing'an District Hospital of Traditional Chinese Medicine, Shanghai, 200072, China
| | - Fang Tan
- Department of Neurology, The First Affiliated Hospital of Anhui University of Traditional Chinese Medicine, Hefei, 230031, Anhui Province, China
| | - Suxian Zhang
- Department of Traditional Chinese Medicine, Jing'an District Central Hospital Affiliated to Fudan University, Shanghai, 200040, China
| | - Tiansong Zhang
- Department of Traditional Chinese Medicine, Jing'an District Central Hospital Affiliated to Fudan University, Shanghai, 200040, China.
- Department of Pulmonary Diseases, Jing'an District Hospital of Traditional Chinese Medicine, Shanghai, 200072, China.
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Nazari E, Khalili-Tanha G, Asadnia A, Pourali G, Maftooh M, Khazaei M, Nasiri M, Hassanian SM, Ghayour-Mobarhan M, Ferns GA, Kiani MA, Avan A. Bioinformatics analysis and machine learning approach applied to the identification of novel key genes involved in non-alcoholic fatty liver disease. Sci Rep 2023; 13:20489. [PMID: 37993474 PMCID: PMC10665370 DOI: 10.1038/s41598-023-46711-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 11/03/2023] [Indexed: 11/24/2023] Open
Abstract
Non-alcoholic fatty liver disease (NAFLD) comprises a range of chronic liver diseases that result from the accumulation of excess triglycerides in the liver, and which, in its early phases, is categorized NAFLD, or hepato-steatosis with pure fatty liver. The mortality rate of non-alcoholic steatohepatitis (NASH) is more than NAFLD; therefore, diagnosing the disease in its early stages may decrease liver damage and increase the survival rate. In the current study, we screened the gene expression data of NAFLD patients and control samples from the public dataset GEO to detect DEGs. Then, the correlation betweenbetween the top selected DEGs and clinical data was evaluated. In the present study, two GEO datasets (GSE48452, GSE126848) were downloaded. The dysregulated expressed genes (DEGs) were identified by machine learning methods (Penalize regression models). Then, the shared DEGs between the two training datasets were validated using validation datasets. ROC-curve analysis was used to identify diagnostic markers. R software analyzed the interactions between DEGs, clinical data, and fatty liver. Ten novel genes, including ABCF1, SART3, APC5, NONO, KAT7, ZPR1, RABGAP1, SLC7A8, SPAG9, and KAT6A were found to have a differential expression between NAFLD and healthy individuals. Based on validation results and ROC analysis, NR4A2 and IGFBP1b were identified as diagnostic markers. These key genes may be predictive markers for the development of fatty liver. It is recommended that these key genes are assessed further as possible predictive markers during the development of fatty liver.
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Affiliation(s)
- Elham Nazari
- Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Ghazaleh Khalili-Tanha
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
- Medical Genetics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Alireza Asadnia
- Medical Genetics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Ghazaleh Pourali
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mina Maftooh
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Majid Khazaei
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mohammadreza Nasiri
- Recombinant Proteins Research Group, The Research Institute of Biotechnology, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Seyed Mahdi Hassanian
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
- Basic Sciences Research Institute, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Majid Ghayour-Mobarhan
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Gordon A Ferns
- Division of Medical Education, Brighton & Sussex Medical School, Falmer, Brighton, BN1 9PH, Sussex, UK
| | - Mohammad Ali Kiani
- Department of Pediatrics, Ghaem Hospital, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Amir Avan
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
- College of Medicine, University of Warith Al-Anbiyaa, Karbala, Iraq.
- Faculty of Health, School of Biomedical Sciences, Queensland University of Technology (QUT), Brisbane, 4000, Australia.
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Liu S, Zhao X, Meng Q, Li B. Screening of potential biomarkers for polycystic ovary syndrome and identification of expression and immune characteristics. PLoS One 2023; 18:e0293447. [PMID: 37883387 PMCID: PMC10602247 DOI: 10.1371/journal.pone.0293447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 10/12/2023] [Indexed: 10/28/2023] Open
Abstract
BACKGROUND Polycystic ovary syndrome (PCOS) seriously affects the fertility and health of women of childbearing age. We look forward to finding potential biomarkers for PCOS that can aid clinical diagnosis. METHODS We acquired PCOS and normal granulosa cell (GC) expression profiles from the Gene Expression Omnibus (GEO) database. After data preprocessing, differentially expressed genes (DEGs) were screened by limma package, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and Gene Set Enrichment Analysis (GSEA) were performed. Recursive feature elimination (RFE) algorithm and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis were used to acquire feature genes as potential biomarkers. Time-dependent receiver operator characteristic curve (ROC curve) and Confusion matrix were used to verify the classification performance of biomarkers. Then, the expression characteristics of biomarkers in PCOS and normal cells were analyzed, and the insulin resistance (IR) score of samples was computed by ssGSEA. Immune characterization of biomarkers was evaluated using MCP counter and single sample gene set enrichment analysis (ssGSEA). Finally, the correlation between biomarkers and the scores of each pathway was assessed. RESULTS We acquired 93 DEGs, and the enrichment results indicated that most of DEGs in PCOS group were significantly enriched in immune-related biological pathways. Further screening results indicated that JDP2 and HMOX1 were potential biomarkers. The area under ROC curve (AUC) value and Confusion matrix of the two biomarkers were ideal when separated and combined. In the combination, the training set AUC = 0.929 and the test set AUC = 0.917 indicated good diagnostic performance of the two biomarkers. Both biomarkers were highly expressed in the PCOS group, and both biomarkers, which should be suppressed in the preovulation phase, were elevated in PCOS tissues. The IR score of PCOS group was higher, and the expression of JDP2 and HMOX1 showed a significant positive correlation with IR score. Most immune cell scores and immune infiltration results were significantly higher in PCOS. Comprehensive analysis indicated that the two biomarkers had strong correlation with immune-related pathways. CONCLUSION We acquired two potential biomarkers, JDP2 and HMOX1. We found that they were highly expressed in the PCOS and had a strong positive correlation with immune-related pathways.
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Affiliation(s)
- Shuang Liu
- The Reproductive Laboratory, Shenyang Jinghua Hospital, Shenyang, China
| | - Xuanpeng Zhao
- The Reproductive Laboratory, Shenyang Jinghua Hospital, Shenyang, China
| | - Qingyan Meng
- The Reproductive Laboratory, Shenyang Jinghua Hospital, Shenyang, China
| | - Baoshan Li
- The Reproductive Laboratory, Shenyang Jinghua Hospital, Shenyang, China
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Wu J, Guo Y, Zuo ZF, Zhu ZW, Han L. MMP14 is a diagnostic gene of intrahepatic cholangiocarcinoma associated with immune cell infiltration. World J Gastroenterol 2023; 29:2961-2978. [PMID: 37274806 PMCID: PMC10237093 DOI: 10.3748/wjg.v29.i19.2961] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Revised: 03/24/2023] [Accepted: 04/23/2023] [Indexed: 05/16/2023] Open
Abstract
BACKGROUND Intrahepatic cholangiocarcinoma (ICC) is a malignant tumor of the hepatobiliary system with concealed onset, strong invasiveness and poor prognosis.
AIM To explore the disease characteristic genes that may be helpful in the diagnosis of ICC and affect immune cell infiltration.
METHODS We downloaded two ICC-related human gene expression profiles from GEO database as the training group (GSE26566 and GSE32958 datasets) for difference analysis, and performed enrichment analysis on differential genes. The least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF), three machine learning algorithms, were used to screen the characteristic genes. Double verification was carried out on GSE107943 and The Cancer Genome Atlas, two verification groups. Receiver operating characteristic curve and area under the curve (AUC) were used to evaluate the diagnostic efficacy of genes for ICC. CIBERSORT and ssGSEA algorithms were used to evaluate the effect of characteristic genes on immune infiltration pattern. Human Protein Atlas (HPA) was used to analyze the protein expression level of the target gene.
RESULTS A total of 1091 differential genes were obtained in the training group. Enrichment analysis showed that the above genes were mainly enriched in small molecular catabolism, complement and coagulation cascade, bile secretion and other functions and pathways. Twenty-five characteristic genes were screened by LASSO regression, 19 by SVM-RFE algorithm, and 30 by RF algorithm. Three algorithms were used in combination to determine the characteristic gene of ICC: MMP14. The verification group confirmed that the genes had a high diagnostic accuracy (AUC values of the training group and the verification group were 0.960, 0.999, and 0.977, respectively). Comprehensive analysis of immune infiltration showed that MMP14 could affect the infiltration of monocytes, activated memory CD4 T cells, resting memory CD4 T cells, and other immune cells, and was closely related to the expression of CD200, cytotoxic T-lymphocyte-associated antigen 4, CD14, CD44, and other immune checkpoints. The results of immunohistochemistry in HPA database showed was indeed overexpressed in ICC.
CONCLUSION MMP14 can be used as a disease characteristic gene of ICC, and may regulate the distribution of immune-infiltrating cells in the ICC tumor microenvironment, which provides a new method for the determination of ICC diagnostic markers and screening of therapeutic targets.
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Affiliation(s)
- Jun Wu
- China Medical University, The General Hospital of Northern Theater Command Training Base for Graduate, Shenyang 110016, Liaoning Province, China
| | - Yang Guo
- Department of Hepatobiliary Surgery, The General Hospital of Northern Theater Command, Shenyang 110016, Liaoning Province, China
| | - Zhi-Fan Zuo
- Gynecological Radiotherapy Ward, Liaoning Provincial Cancer Hospital, Shenyang 110801, Liaoning province, China
| | - Zi-Wei Zhu
- China Medical University, The General Hospital of Northern Theater Command Training Base for Graduate, Shenyang 110016, Liaoning Province, China
| | - Lei Han
- Department of Hepatobiliary Surgery, The General Hospital of Northern Theater Command, Shenyang 110016, Liaoning Province, China
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Wang W, Zhang TN, Yang N, Wen R, Wang YJ, Zhang BL, Yang YH, Liu CF. Transcriptome-wide identification of altered RNA m 6A profiles in cardiac tissue of rats with LPS-induced myocardial injury. Front Immunol 2023; 14:1122317. [PMID: 37275860 PMCID: PMC10237353 DOI: 10.3389/fimmu.2023.1122317] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 05/05/2023] [Indexed: 06/07/2023] Open
Abstract
Purpose Myocardial injury is a common complication in patients with endotoxaemia/sepsis, especially in children. Moreover, it develops through an unclear pathophysiological mechanism, and effective therapies are lacking. Recently, RNA modification, particularly N 6-methyladenosine (m6A) modification, has been found to be involved in various physiological processes and to play important roles in many diseases. However, the role of m6A modification in endotoxaemia/sepsis-induced myocardial injury is still in its infancy. Therefore, we attempted to construct the m6A modification map of myocardial injury in a rat model treated by lipopolysaccharide (LPS) and explore the role of m6A modification in LPS-induced myocardial injury. Method Myocardial injury adolescent rat model was constructed by intraperitoneal injection of LPS. m6A RNA Methylation Quantification Kit was used to detect overall level of m6A modification in rat cardiac tissue. m6A-specific methylated RNA immunoprecipitation followed by high-throughput sequencing (MeRIP-seq) and RNA sequencing (RNA-seq) were conducted to identify the altered m6A-modified genes and differentially expressed genes in cardiac tissue of rats treated by LPS and control rats (6 versus. 6). Bioinformatics was used to analyze the functions of differentially m6A modified genes, differentially expressed genes, and genes with both differential m6A modification and differential expression. qPCR was used to detect expression of m6A modification related enzymes. Result We found that the overall level of m6A modification in cardiac tissue of the LPS group was up-regulated compared with that of the control group. MeRIP-seq and RNA-seq results showed that genes with differential m6A modification, genes with differential expression and genes with both differential m6A modification and differential expression were closely associated with inflammatory responses and apoptosis. In addition, we found that m6A-related enzymes (Mettl16, Rbm15, Fto, Ythdc2 and Hnrnpg) were differentially expressed in the LPS group versus. the control group. Conclusion m6A modification is involved in the pathogenesis process of LPS-induced myocardial injury, possibly through the regulation of inflammatory response and apoptosis-related pathways. These results provide valuable information regarding the potential pathogenic mechanisms underlying LPS-induced myocardial injury.
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Affiliation(s)
- Wei Wang
- Department of Pediatrics, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Tie-Ning Zhang
- Department of Pediatrics, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Ni Yang
- Department of Pediatrics, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Ri Wen
- Department of Pediatrics, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Yu-Jing Wang
- Department of Pediatrics, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Bing-Lun Zhang
- Department of Pediatrics, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Yu-Hang Yang
- Department of Pediatrics, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - Chun-Feng Liu
- Department of Pediatrics, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
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Zhang Y, Wang C, Xia Q, Jiang W, Zhang H, Amiri-Ardekani E, Hua H, Cheng Y. Machine learning-based prediction of candidate gene biomarkers correlated with immune infiltration in patients with idiopathic pulmonary fibrosis. Front Med (Lausanne) 2023; 10:1001813. [PMID: 36860337 PMCID: PMC9968813 DOI: 10.3389/fmed.2023.1001813] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 01/26/2023] [Indexed: 02/15/2023] Open
Abstract
Objective This study aimed to identify candidate gene biomarkers associated with immune infiltration in idiopathic pulmonary fibrosis (IPF) based on machine learning algorithms. Methods Microarray datasets of IPF were extracted from the Gene Expression Omnibus (GEO) database to screen for differentially expressed genes (DEGs). The DEGs were subjected to enrichment analysis, and two machine learning algorithms were used to identify candidate genes associated with IPF. These genes were verified in a validation cohort from the GEO database. Receiver operating characteristic (ROC) curves were plotted to assess the predictive value of the IPF-associated genes. The cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm was used to evaluate the proportion of immune cells in IPF and normal tissues. Additionally, the correlation between the expression of IPF-associated genes and the infiltration levels of immune cells was examined. Results A total of 302 upregulated and 192 downregulated genes were identified. Functional annotation, pathway enrichment, Disease Ontology and gene set enrichment analyses revealed that the DEGs were related to the extracellular matrix and immune responses. COL3A1, CDH3, CEBPD, and GPIHBP1 were identified as candidate biomarkers using machine learning algorithms, and their predictive value was verified in a validation cohort. Additionally, ROC analysis revealed that the four genes had high predictive accuracy. The infiltration levels of plasma cells, M0 macrophages and resting dendritic cells were higher and those of resting natural killer (NK) cells, M1 macrophages and eosinophils were lower in the lung tissues of patients with IPF than in those of healthy individuals. The expression of the abovementioned genes was correlated with the infiltration levels of plasma cells, M0 macrophages and eosinophils. Conclusion COL3A1, CDH3, CEBPD, and GPIHBP1 are candidate biomarkers of IPF. Plasma cells, M0 macrophages and eosinophils may be involved in the development of IPF and may serve as immunotherapeutic targets in IPF.
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Affiliation(s)
- Yufeng Zhang
- Department of Pulmonary and Critical Care Medicine, Jiangyin Hospital of Traditional Chinese Medicine, Jiangyin Hospital Affiliated to Nanjing University of Chinese Medicine, Jiangyin, Jiangsu, China
| | - Cong Wang
- Department of Pulmonary and Critical Care Medicine, Jiangyin Hospital of Traditional Chinese Medicine, Jiangyin Hospital Affiliated to Nanjing University of Chinese Medicine, Jiangyin, Jiangsu, China
| | - Qingqing Xia
- Department of Pulmonary and Critical Care Medicine, Jiangyin Hospital of Traditional Chinese Medicine, Jiangyin Hospital Affiliated to Nanjing University of Chinese Medicine, Jiangyin, Jiangsu, China
| | - Weilong Jiang
- Department of Pulmonary and Critical Care Medicine, Jiangyin Hospital of Traditional Chinese Medicine, Jiangyin Hospital Affiliated to Nanjing University of Chinese Medicine, Jiangyin, Jiangsu, China
| | - Huizhe Zhang
- Department of Respiratory Medicine, Yancheng Hospital of Traditional Chinese Medicine, Yancheng Hospital Affiliated to Nanjing University of Chinese Medicine, Yancheng, Jiangsu, China
| | - Ehsan Amiri-Ardekani
- Department of Phytopharmaceuticals (Traditional Pharmacy), Faculty of Pharmacy, Shiraz University of Medical Sciences, Shiraz, Iran,*Correspondence: Ehsan Amiri-Ardekani,
| | - Haibing Hua
- Department of Gastroenterology, Jiangyin Hospital of Traditional Chinese Medicine, Jiangyin Hospital Affiliated to Nanjing University of Chinese Medicine, Jiangyin, Jiangsu, China,Haibing Hua,
| | - Yi Cheng
- Department of Respiratory Medicine, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China,Yi Cheng,
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Zhang Q, Guo Y, Zhang B, Liu H, Peng Y, Wang D, Zhang D. Identification of hub biomarkers of myocardial infarction by single-cell sequencing, bioinformatics, and machine learning. Front Cardiovasc Med 2022; 9:939972. [PMID: 35958412 PMCID: PMC9357907 DOI: 10.3389/fcvm.2022.939972] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 07/05/2022] [Indexed: 12/11/2022] Open
Abstract
Background Myocardial infarction (MI) is one of the first cardiovascular diseases endangering human health. Inflammatory response plays a significant role in the pathophysiological process of MI. Messenger RNA (mRNA) has been proven to play a key role in cardiovascular diseases. Single-cell sequencing (SCS) technology is a new technology for high-throughput sequencing analysis of genome, transcriptome, and epigenome at the single-cell level, and it also plays an important role in the diagnosis and treatment of cardiovascular diseases. Machine learning algorithms have a wide scope of utilization in biomedicine and have demonstrated superior efficiency in clinical trials. However, few studies integrate these three methods to investigate the role of mRNA in MI. The aim of this study was to screen the expression of mRNA, investigate the function of mRNA, and provide an underlying scientific basis for the diagnosis of MI. Methods In total, four RNA microarray datasets of MI, namely, GSE66360, GSE97320, GSE60993, and GSE48060, were downloaded from the Gene Expression Omnibus database. The function analysis was carried out by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Disease Ontology (DO) enrichment analysis. At the same time, inflammation-related genes (IRGs) were acquired from the GeneCards database. Then, 52 co-DEGs were acquired from differentially expressed genes (DEGs) in differential analysis, IRGs, and genes from SCS, and they were used to construct a protein-protein interaction (PPI) network. Two machine learning algorithms, namely, (1) least absolute shrinkage and selection operator and (2) support vector machine recursive feature elimination, were used to filter the co-DEGs. Gene set enrichment analysis (GSEA) was performed to screen the hub-modulating signaling pathways associated with the hub genes. The results were validated in GSE97320, GSE60993, and GSE48060 datasets. The CIBERSORT algorithm was used to analyze 22 infiltrating immune cells in the MI and healthy control (CON) groups and to analyze the correlation between these immune cells. The Pymol software was used for molecular docking of hub DEGs and for potential treatment of MI drugs acquired from the COREMINE. Results A total of 126 DEGs were in the MI and CON groups. After screening two machine learning algorithms and key co-DEGs from a PPI network, two hub DEGs (i.e., IL1B and TLR2) were obtained. The diagnostic efficiency of IL1B, TLR2, and IL1B + TLR2 showed good discrimination in the four cohorts. GSEA showed that KEGG enriched by DEGs were mainly related to inflammation-mediated signaling pathways, and GO biological processes enriched by DEGs were linked to biological effects of various inflammatory cells. Immune analysis indicated that IL1B and TLR2 were correlated with various immune cells. Dan shen, san qi, feng mi, yuan can e, can sha, san qi ye, san qi hua, and cha shu gen were identified as the potential traditional Chinese medicine (TCM) for the treatment of MI. 7-hydroxyflavone (HF) had stable combinations with IL1B and TLR2, respectively. Conclusion This study identified two hub DEGs (IL1B and TLR2) and illustrated its potential role in the diagnosis of MI to enhance our knowledge of the underlying molecular mechanism. Infiltrating immune cells played an important role in MI. TCM, especially HF, was a potential drug for the treatment of MI.
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Affiliation(s)
- Qunhui Zhang
- Research Center for High Altitude Medicine, Key Laboratory of High-Altitude Medicine (Ministry of Education), Key Laboratory of Application and Foundation for High Altitude Medicine Research in Qinghai Province (Qinghai-Utah Joint Research Key Lab for High Altitude Medicine), Qinghai University, Xining, China.,College of Eco-Environmental Engineering, Qinghai University, Xining, China
| | - Yang Guo
- Research Center for High Altitude Medicine, Key Laboratory of High-Altitude Medicine (Ministry of Education), Key Laboratory of Application and Foundation for High Altitude Medicine Research in Qinghai Province (Qinghai-Utah Joint Research Key Lab for High Altitude Medicine), Qinghai University, Xining, China.,College of Eco-Environmental Engineering, Qinghai University, Xining, China
| | - Benyin Zhang
- College of Eco-Environmental Engineering, Qinghai University, Xining, China
| | - Hairui Liu
- College of Eco-Environmental Engineering, Qinghai University, Xining, China
| | - Yanfeng Peng
- College of Eco-Environmental Engineering, Qinghai University, Xining, China
| | - Di Wang
- College of Eco-Environmental Engineering, Qinghai University, Xining, China
| | - Dejun Zhang
- Research Center for High Altitude Medicine, Key Laboratory of High-Altitude Medicine (Ministry of Education), Key Laboratory of Application and Foundation for High Altitude Medicine Research in Qinghai Province (Qinghai-Utah Joint Research Key Lab for High Altitude Medicine), Qinghai University, Xining, China.,College of Eco-Environmental Engineering, Qinghai University, Xining, China
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