Ageing significantly contributes to osteoarthritis (OA) and metabolic syndrome (MetS) pathogenesis, yet the underlying mechanisms remain unknown. This study aimed to identify ageing-related biomarkers in OA patients with MetS. OA and MetS datasets and ageing-related genes (ARGs) were retrieved from public databases. The limma package was used to identify differentially expressed genes (DEGs), and weighted gene coexpression network analysis (WGCNA) screened gene modules, and machine learning algorithms, such as random forest (RF), support vector machine (SVM), generalised linear model (GLM), and extreme gradient boosting (XGB), were employed. The nomogram and receiver operating characteristic (ROC) curve assess the diagnostic value, and CIBERSORT analysed immune cell infiltration. We identified 20 intersecting genes among DEGs of OA, key module genes of MetS, and ARGs. By comparing the accuracy of the four machine learning models for disease prediction, the SVM model, which includes CEBPB, PTEN, ARPC1B, PIK3R1, and CDC42, was selected. These hub ARGs not only demonstrated strong diagnostic values based on nomogram data but also exhibited a significant correlation with immune cell infiltration. Building on these findings, we have identified five hub ARGs that are associated with immune cell infiltration and have constructed a nomogram aimed at early diagnosing OA patients with MetS.

The molecular and genetic mechanisms underlying vascular calcification remain unclear. This study aimed to determine the differences in calcification marker-related gene expression in macrophages.

The expression profiling datasets GSE104140 and GSE235995 were analysed to identify differentially expressed genes (DEGs) between fibroatheroma with calcification and diffuse intimal thickening. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, Weighted Gene Co-expression Network Analysis (WGCNA), and Gene Set Enrichment Analysis (GSEA) were performed to assess functional characteristics. Hub genes were identified through a protein-protein interaction (PPI) network and machine learning approaches. Single-cell RNA sequencing data (GSE159677) validated the expression of calcification-related genes in macrophages, while Mendelian randomization analysis explored their potential causal relationship with coronary calcification. Further validation was conducted using enzyme-linked immunosorbent assay (ELISA) on coronary calcification samples and immunohistochemistry in ApoE-/- mice. Intravascular ultrasound was performed to assess coronary calcification severity.

Two key biomarkers, ITGAX and MYD88, were identified as diagnostic indicators of cardiovascular calcification. Both biomarkers were significantly upregulated in calcified samples and were strongly associated with immune processes. Single-cell RNA sequencing confirmed their high expression in multiple immune cell types. Additionally, molecular docking analysis revealed that retinoic acid interacted with both biomarkers, suggesting potential therapeutic relevance. Immunohistochemical and ELISA analyses further validated their elevated expression in calcified samples. These findings provide novel insights into the molecular mechanisms of vascular calcification and highlight potential diagnostic and therapeutic targets.

Major Depressive Disorder (MDD) and Bipolar Disorder (BD) exhibit overlapping depressive symptoms, complicating their differentiation in clinical practice. Traditional neuroimaging studies have focused on specific regions of interest, but few have employed whole-brain analyses like regional homogeneity (ReHo). This study aims to differentiate MDD from BD by identifying key brain regions with abnormal ReHo and using advanced machine learning techniques to improve diagnostic accuracy.

A total of 63 BD patients, 65 MDD patients, and 70 healthy controls were recruited from the Shanghai Mental Health Center. Resting-state functional MRI (rs-fMRI) was used to analyze ReHo across the brain. We applied Support Vector Machine (SVM) and SVM-Recursive Feature Elimination (SVM-RFE), a robust machine learning model known for its high precision in feature selection and classification, to identify critical brain regions that could serve as biomarkers for distinguishing BD from MDD. SVM-RFE allows for the recursive removal of non-informative features, enhancing the model's ability to accurately classify patients. Correlations between ReHo values and clinical scores were also evaluated.

ReHo analysis revealed significant differences in several brain regions. The study results revealed that, compared to healthy controls, both BD and MDD patients exhibited reduced ReHo in the superior parietal gyrus. Additionally, MDD patients showed decreased ReHo values in the Right Lenticular nucleus, putamen (PUT.R), Right Angular gyrus (ANG.R), and Left Superior occipital gyrus (SOG.L). Compared to the MDD group, BD patients exhibited increased ReHo values in the Left Inferior occipital gyrus (IOG.L). In BD patients only, the reduction in ReHo values in the right superior parietal gyrus and the right angular gyrus was positively correlated with Hamilton Depression Scale (HAMD) scores. SVM-RFE identified the IOG.L, SOG.L, and PUT.R as the most critical features, achieving an area under the curve (AUC) of 0.872, with high sensitivity and specificity in distinguishing BD from MDD.

This study demonstrates that BD and MDD patients exhibit distinct patterns of regional brain activity, particularly in the occipital and parietal regions. The combination of ReHo analysis and SVM-RFE provides a powerful approach for identifying potential biomarkers, with the left inferior occipital gyrus, left superior occipital gyrus, and right putamen emerging as key differentiating regions. These findings offer valuable insights for improving the diagnostic accuracy between BD and MDD, contributing to more targeted treatment strategies.

This study aimed to identify key candidate genes associated with the sexes of patients with SSc.

Skin gene expression datasets from patients with SSc and healthy controls (GSE181549 and GSE130955) were retrieved from the GEO database. GSE181549 served as the testing set, while the GSE130955 was used for validation. Differentially expressed genes (DEGs) between SSc and normal skin samples were identified using limma, stratified by sex in the GSE181549. Bioinformatics analyses were performed to evaluate the DEGs, and machine learning techniques were applied to identify sex-specific.

In male samples from the testing set, 80 DEGs were upregulated and 20 were downregulated, while in female samples, 94 DEGs were upregulated and 12 were downregulated. Functional enrichment analysis indicated that these DEGs are potentially implicated in sex-specific SSc pathogenesis. Machine learning identified 10 marker genes in males samples and 12 in females. Immune infiltration analysis revealed a significant increase in M0 and M1 macrophages and a decrease in M2 macrophages and resting dendritic cells in male SSc samples. In female SSc samples, memory B cells, plasma cells and M1 macrophages were significantly elevated, whereas resting CD4 memory T cells were notably reduced.

Patients with SSc exhibit distinct sex-specific differences in DEGs, marker genes and immune infiltration profiles.

Acute mountain sickness (AMS) is a self-limiting illness, involving a complex series of physiological responses to rapid ascent to high altitudes, where the body is exposed to lower oxygen levels (hypoxia) and changes in atmospheric pressure. AMS is the mildest and most common form of altitude sickness; however, without adequate preparation and adherence to ascent guidelines, it can progress to life-threatening conditions.

Due to the multi-factorial predisposition of AMS among individuals, identifying AMS biomarkers before high altitude exposure from multiple dimensions (e.g., clinical, metabolic, and proteomic markers) and integrating them to build an AMS predictive model enables early diagnosis and personalized interventions, which allows targeted allocation of medical resources, such as prophylactic medications (e.g., acetazolamide) and supplemental oxygen, to those who need them most and prevention of unnecessary complications. Consequently, predicting AMS utilizing biomarkers from multidimensional phenotypic data before high-altitude exposure is essential for the paradigm change in high-altitude medical research from currently applied reactive services to the cost-effective predictive, preventive, and personalized medicine (PPPM/3PM) in primary (reversible damage to health and targeted protection against health-to-disease transition) and secondary (personalized protection against disease progression) care.

To this end, this study recruited 83 Han Chinese male volunteers and obtained clinical, proteomic, and metabolomic profiles for analysis before they ascended to high altitudes. The Mann-Whitney U test was used to identify clinical features distinguishing AMS from non-AMS. The proteomic and metabolomic features were concatenated and clustered to find co-expression modules associated with AMS. A machine learning model, Mutual Information-radial kernel-based Support Vector Machine-Recursive Feature Elimination (MI-radialSVM-RFE) was employed for biomarkers selection and AMS prediction. A molecular docking technique was used to select molecular biomarkers that can bind with Traditional Chinese Medicine (TCM) ingredients.

Among 83 participants, 66 were selected for detailed analysis after quality control steps. Six protein-metabolite co-expression modules were identified as significantly associated with AMS. The MI-radialSVM-RFE model selected 12 biomarkers (two clinical features: systolic blood pressure (SBP) and peak expiratory flow (PEF); six proteins: Acyl-CoA synthetase long-chain family member 4 (ACSL4), immunoglobulin kappa variable 1D-16 (IGKV1D-16), coagulation factor XIII B subunit (F13B), prosaposin (PSAP), poliovirus receptor (PVR), and multimerin-2 (MMRN2); and four metabolites: 2-Methyl-1,3-cyclohexadiene, calcitriol, 4-Acetamido-2-amino-6-nitrotoluene, and 20-Hydroxy-PGE2) for the AMS prediction model. The model exhibited excellent predictive performance in both training (n = 66) and validating cohorts (n = 24) with AUCs of 0.97 and 0.94, respectively. Additionally, molecular docking analysis suggested PSAP and ACSL4 proteins as potential molecular targets for AMS prevention.

This study advances high-altitude medicine by developing a predictive model for AMS using clinical, proteomic, and metabolomic data. The identified biomarkers linked to energy metabolism, immune response, and vascular regulation offer insights into AMS mechanisms. High-altitude predictive approaches should focus on implementing biomarker-driven risk screening using clinical, proteomic, and metabolomic data to identify high-risk individuals before high-altitude exposure. Preventive measures should prioritize pre-acclimatization protocols, tailored nutritional strategies and interventions guided by biomarker profiles, and lifestyle adjustments, such as maintaining mitochondrial health through proper nutritional strategies.

The online version contains supplementary material available at 10.1007/s13167-025-00404-9.

We aimed to identify risk factors for initial seizure episodes in ICU patients using various machine learning algorithms.

Using the extensive eICU database, we curated a dataset of 200,859 patient records, with 15,890 patients meeting inclusion and exclusion criteria. Among them, 497 experienced initial seizure episodes during hospitalization. We developed models to identify risk factors associated with these episodes using Logistic Regression, Random Forest, Gradient Boosting, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Decision Tree. After developing and evaluating these individual models, we selected the two best-performing models and combined them using a stacking ensemble learning technique. Additionally, Recursive Feature Elimination (RFE) was used to select the most relevant features. Model performance was evaluated using metrics such as Area Under the Receiver Operating Characteristic Curve (AUC-ROC), accuracy, precision, recall, and F1 score, alongside calibration plots and Decision Curve Analysis (DCA).

The incidence rate of initial seizure episodes was 3.10% (497/15,890), with no significant difference between the training and validation sets. The best-performing individual models were Gradient Boosting (AUC-ROC: 0.78) and Logistic Regression (AUC-ROC: 0.79). The ensemble model achieved an AUC-ROC of 0.80 (95%CI: 0.78-0.82), accuracy of 0.78, precision of 0.80, recall of 0.75, and F1 score of 0.77. Calibration plots demonstrated that the ensemble model's predicted probabilities were well-aligned with observed outcomes. DCA indicated significant net benefit across a range of threshold probabilities, underscoring the model's clinical utility.

The ensemble learning model, combining Gradient Boosting and Logistic Regression via a stacking technique, demonstrated superior performance for identifying risk factors for initial seizure episodes in ICU patients. This model was evaluated using a range of performance metrics, including accuracy, sensitivity, specificity, and the AUC-ROC curve, and was validated through 10-fold cross-validation to ensure its robustness and generalizability. These results offer clinically relevant risk factor identification. Key risk factors identified include age, GCS score, glucose levels, hematocrit levels, hyponatremia, stroke history, prothrombin time, potassium levels, and hypertension. The risk estimation table simplifies these complex interactions into a practical tool for clinical use.

Obstructive sleep apnea syndrome (OSAS) is a common sleep disorder, which is closely related to abnormal mitochondrial energy metabolism in recent years. By analyzing gene expression data of OSAS, we identified differentially expressed genes (DEGs) related to mitochondrial energy metabolism, and further explored the function of NDUFA10 in OSAS and its potential diagnostic value. In this paper, we download OSAS related expression data from a public database and preprocess the data set using a standardized process. Gene set enrichment analysis (GSEA) was used to assess pathways associated with mitochondrial metabolism, and diagnostic models were constructed to assess the expression of key genes. ROC curve analysis was performed for common mitochondrial energy metabolism-related differentially expressed genes (Co-MEMRDEGs) and gene interaction networks were constructed. After data standardization, significantly differentially expressed genes were identified, among which NDUFA10 was identified as one of the genes most associated with OSAS. The constructed diagnostic model showed good prediction accuracy, and ROC curve analysis further verified its clinical diagnostic potential.

Fibrolamellar carcinoma (FLC) is a rare form of liver carcinoma with limited diagnostic and therapeutic options. In this study, we utilized the GSE57727 and E-MTAB-1503 datasets, downloaded from GEO and ArrayExpress, respectively, to explore hub genes for FLC diagnosis and potential therapeutic agents. Through the integration of multiple machine learning approaches and drug sensitivity databases, we identified AKTIP as a potential diagnostic biomarker for FLC. AKTIP exhibited markedly elevated expression in FLC compared to non-FLC, demonstrating superior diagnostic and prognostic performance over other FLC-specific biomarkers. Four compounds (PI-103, BVT-948, Digitoxigenin, and SB-218078) were identified as potential therapeutic agents targeting AKTIP. Molecular docking analysis revealed strong binding affinities of these compounds to AKTIP, and molecular dynamics simulations further validated the reliability and rationality of the molecular docking results. Pan-cancer analysis indicated that AKTIP expression varies across different tissues and is significantly associated with patient prognosis. qRT-PCR analysis confirmed that AKTIP mRNA levels were markedly overexpressed in normal liver epithelial cells compared to human hepatocellular carcinoma cell lines. In conclusion, AKTIP was successfully identified as a diagnostic and prognostic biomarker for FLC, and four compounds were proposed as potential therapeutic agents. This study uncovers new perspectives on diagnosing and managing of this rare type of liver carcinoma, offering promising avenues for future research and clinical applications.

The online version contains supplementary material available at 10.1007/s13205-025-04323-4.

Gastric cancer (GC), a common and deadly malignancy worldwide, is a serious burden on society and individuals. However, available diagnostic biomarkers for GC are very limited. The current study aimed to identify potential diagnostic biomarkers for GC and analyze the activity of infiltrating immune cells in this pathology.

Microarray data for GC were acquired from the Gene Expression Omnibus (GEO) database. The limma package was utilized to normalize these data, thus identifying differentially expressed genes (DEGs). For normalized data of samples, we established a weighted gene co-expression network (WGCNA) to reveal key genes in the significant module. Afterward, we obtained overlapping genes by intersecting the DEGs and the key genes from the WGCNA module. Next, after applying the three algorithms (LASSO, RandomForest, and SVM-RFE) to analyze these overlapping genes and take the intersection, we established a GC diagnosis. The diagnostic significances of these identified genes were evaluated with receiver operating characteristic (ROC) curves and validated in the external dataset. Furthermore, ssGSEA and CIBERSORT were employed for evaluating the infiltrating immune cells and the association of the immune cells and diagnostic biomarkers.

Herein, we identified 49 overlapping genes, and the results of enrichment analysis demonstrated that these genes may be involved in the signaling transduction-related process. Finally, BANF1, DUSP14, and VMP1 were regarded as key biomarkers in GC patients based on the overlapping genes that we found, and these three biomarkers demonstrated great diagnostic significance. Additionally, the hub biomarkers had different levels of association with macrophages, neutrophils, memory B cells, and plasma cells.

BANF1, DUSP14, and VMP1 are promising diagnostic biomarkers for GC, and infiltrating immune cells may dramatically affect gastric carcinogenesis and progression.

Decreased glucose tolerance is recognized as a factor associated with Parkinson's disease (PD) progression, yet the relationship between HbA1c and PD prognosis remains insufficiently explored. Using data from the Integrated Epidemiological Unit (IEU) open Genome-Wide Association Study (GWAS), PD's IEU-b-7 and HbA1c's IEU-b-104 were extracted. RNA-seq data from GSE20292 and single-cell RNA-seq data from GSE157783 were retrieved from Gene Expression Omnibus (GEO). Mendelian Randomization (MR) analysis, with HbA1c as the exposure and PD as the outcome, was performed using the inverse variance weighted (IVW) method. Differentially expressed genes (DEGs) between PD and controls in GSE20292 were identified, and overlapping instrumental variables (IVs) and DEGs pinpointed a set of candidate genes. Machine learning refinement selected biomarkers, leading to the development of a PD biomarker-based nomogram. Key cell lineages in GSE140231 were characterized, and communication and pseudotime analyses explored cell crosstalk and evolution. Using 223 independent single nucleotide polymorphisms (SNPs)as IVs, HbA1c was found causally [IVW: Odds Ratio (OR) = 1.438, P = 0.026, 95% Confidence Interval (CI) = 1.043-1.981].. Among 625 genes associated with these SNPs, 842 DEGs were identified by comparing PD vs. controls, intersecting with 27 candidate genes. Notably, five biomarkers-FASN, MICAL3, TCIRG1, CDK10, and MFSD1-emerged as potential diagnostic targets for PD. The receiver operating characteristic (ROC) curve demonstrated the high diagnostic accuracy of these biomarkers. Analysis of key cell lineages revealed strong interactions between excitatory and inhibitory cells and oligodendrocyte precursor cells and Astrocytes cells. In conclusion, HbA1c is identified as a risk factor for PD, with FASN, MICAL3, TCIRG1, CDK10, and MFSD1 representing promising targets for PD diagnosis and treatment.

Sepsis, a life-threatening condition driven by dysregulated host responses to infection, is associated with long-term cognitive impairments resembling Alzheimer's disease (AD). However, the molecular mechanisms linking sepsis-induced cognitive dysfunction and AD remain unclear. We hypothesized that shared genetic pathways underlie cognitive deficits in both conditions.

Cecal ligation and puncture (CLP) in C57BL/6 J mice modeled sepsis-induced cognitive decline and amyloid pathology. Brain tissue datasets (GSE33000 for AD; GSE135838 for sepsis) were analyzed via Weighted Gene Co-expression Network Analysis (WGCNA), machine learning, and functional enrichment. Key genes were validated through ROC analysis, immune infiltration profiling, and in vivo/in vitro experiments.

Sepsis accelerated cognitive decline and AD-like pathology in mice. Bioinformatics identified CLIC1 and IFITM2 as co-diagnostic genes linked to immune dysregulation in both sepsis and AD. Immune infiltration revealed reduced neutrophils/NK cells, M1 macrophage polarization, and naïve-to-memory B cell shifts in sepsis versus AD. CLIC1 and IFITM2 were upregulated in CLP mice and cytokine-stimulated human cerebral endothelial cells, aligning with bioinformatics predictions.

CLIC1 and IFITM2, pivotal in immune cell activation, emerged as shared biomarkers of sepsis-related cognitive impairment and AD. These findings highlight immune-driven molecular intersections in cognitive deficits, offering novel targets for mechanistic research and therapeutic development.

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that involves multiple systems. SLE is characterized by the production of autoantibodies and inflammatory tissue damage. This study further explored the role of immune-related genes in SLE. We downloaded the expression profiles of GSE50772 using the Gene Expression Omnibus (GEO) database for differentially expressed genes (DEGs) in SLE. The DEGs were also analyzed for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. The gene modules most closely associated with SLE were then derived by Weighted Gene Co-expression Network Analysis (WGCNA). Differentially expressed immune-related genes (DE-IRGs) in SLE were obtained by DEGs, key gene modules and IRGs. The protein-protein interaction (PPI) network was constructed through the STRING database. Three machine learning algorithms were applied to DE-IRGs to screen for hub DE-IRGs. Then, we constructed a diagnostic model. The model was validated by external cohort GSE61635 and peripheral blood mononuclear cells (PBMC) from SLE patients. Immune cell abundance assessment was achieved by CIBERSORT. The hub DE-IRGs and miRNA networks were made accessible through the NetworkAnalyst database. We screened 945 DEGs, which are closely related to the type I interferon pathway and NOD-like receptor signaling pathway. Machine learning identified a total of five hub DE-IRGs (CXCL2, CXCL8, FOS, NFKBIA, CXCR2), and validated in GSE61635 and PBMC from SLE patients. Immune cell abundance analysis showed that the hub genes may be involved in the development of SLE by regulating immune cells (especially neutrophils). In this study, we identified five hub DE-IRGs in SLE and constructed an effective predictive model. These hub genes are closely associated with immune cell in SLE. These may provide new insights into the immune-related pathogenesis of SLE.

To develop and validate an interpretable machine learning (ML) model for the preoperative prediction of central lymph node metastasis (CLNM) in papillary thyroid microcarcinoma (PTMC).

From December 2016 to December 2023, we retrospectively analyzed 710 PTMC patients who underwent thyroidectomies. Feature selection was conducted using the least absolute shrinkage and selection operator (LASSO) regression method, alongside the Support Vector Machine-Recursive Feature Elimination (SVM-RFE) algorithm in conjunction with multivariate logistic regression. Eight ML algorithms, namely Decision Tree, Random Forest (RF), K-nearest neighbors, Support vector machine, Extreme Gradient Boosting, Naive Bayes, Logistic regression, and Light Gradient Boosting machine, were developed for the prediction of CLNM. The performance of these models was evaluated using area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA), sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and F1 scores. Additionally, the Shapley Additive Explanation (SHAP) algorithm was utilized to clarify the results of the optimal ML model.

The results indicated that 32.95% of the patients (234/710) presented with CLNM. Tumor diameter, multifocality, lymph nodes identified via ultrasound (US-LN), and extrathyroidal extension (ETE) were identified as independent predictors of CLNM. The RF model achieved the highest performance in the validation set with an AUC of 0.893(95%CI: 0.846-0.940), accuracy of 0.832, sensitivity of 0.764, specificity of 0.866, PPV of 0.743, NPV of 0.879, and F1-score of 0.753. Furthermore, the DCA demonstrated that the RF model exhibited a superior clinical net benefit.

Our model predicted the risk of CLNM in PTMC patients with high accuracy preoperatively.

The efficacy of clinical drugs for acute lung injury/acute respiratory distress syndrome (ALI/ARDS) remains suboptimal. Phillyrin (PHN), a compound derived from Forsythia, is believed to alleviate sepsis-related ALI/ARDS; however, its mechanisms are not fully elucidated. In this study, we screened 8331 target genes associated with ALI/ARDS from public databases and identified six hub genes relevant to PHN treatment: AKT1, GSK-3β, PPP2CA, PPP2CB, PPP2R1A, and AR. Receiver operating characteristic analysis and single-cell sequencing analysis revealed the expression of AKT1, GSK-3β, PPP2CA, PPP2CB, and PPP2R1A were markedly elevated. Molecular docking and dynamics simulations indicated that PHN forms a structurally stable complex with glycogen synthase kinase-3β (GSK-3β). Mendelian randomization analyses suggested that PHN, as a potent GSK-3β inhibitor, may promote M2 macrophage polarization and reduce neutrophil recruitment. We validated these findings through in vivo and in vitro experiments, demonstrating that PHN lowers iNOS levels and raises MMR levels by downregulating GSK-3β mRNA expression and protein activity during lipopolysaccharide (LPS)-induced macrophage inflammation. Additionally, PHN inhibited GSK-3β mRNA expression and protein activity, reducing NF-κB-p65 nuclear translocation in LPS-induced zebrafish inflammation and mice ALI. This inhibition decreased levels of TNF-α and IL-6, increased IL-10 levels, promoted M2 macrophage polarization, suppressed neutrophil recruitment, and ultimately ameliorated ALI/ARDS. In conclusion, our results indicate that PHN effectively alleviates LPS-induced ALI/ARDS by suppressing GSK-3β signaling.

Hypertrophic cardiomyopathy (HCM), characterised by abnormal ventricular thickening, involves complex mechanisms including gene mutations, calcium dysregulation, mitochondrial dysfunction, and oxidative stress. Oxidative stress plays a pivotal role in the progression of HCM by mediating cardiomyocyte injury and remodelling. This study systematically analysed HCM transcriptomic data using differential gene expression, weighted gene co-expression network analysis (WGCNA), and unsupervised consensus clustering to identify key genes and classify HCM subtypes. Four oxidative stress-related characteristic genes (DUSP1, CCND1, STAT3, and THBS1) were identified using LASSO regression, SVM-RFE, and Random Forest algorithms. Their functional significance was validated by immune infiltration analysis, drug prediction using the cMAP database, and molecular docking. Single-cell RNA sequencing revealed their cell-type-specific expression, and in vitro experiments confirmed their role in HCM. These findings provide insights into oxidative stress mechanisms and potential therapeutic targets for HCM.

Chemotherapy is one of the most successful strategies for treating cancer. Unfortunately, up to 70 % of cancer survivors develop cognitive impairment during or after chemotherapy, which severely affects their quality of life. We first established a mouse model of CICI and combined bioinformatics, machine learning, and transcriptome sequencing to screen diagnostic genes associated with CICI. Relevant DEGs were screened by differential analysis, and potential biological functions of DEGs were explored by GO and KEGG analysis. WGCNA analysis was then used to find the most relevant modules for CICI. The diagnostic gene Npas4 was screened by combining the three machine learning methods; its diagnostic value was proved by ROC analysis, GSEA analyzed its potential biological function, and then we preliminarily explored the chemicals associated with Npas4. Our study found that Npas4 can be used as an early diagnostic gene for CICI, which provides a theoretical basis for further research.

The expression of human epidermal growth factor receptor 2 (HER2) in gastric cancer is closely associated with its treatment outcomes and prognosis. This study aims to develop and validate a HER2 prediction model based on computed tomography (CT). Additionally, the study evaluates the robustness of the proposed model.

This retrospective study included 1059 patients from three hospitals (A, B, and C), where patients from hospitals A and B formed the training set (720 cases), and patients from hospital C served as the external test set (339 cases). Venous-phase CT radiomic features were extracted, normalized using the Z-score method, and simplified via principal component analysis. Feature selection was performed using recursive feature elimination (RFE), analysis of variance, Relief, and the Kruskal-Wallis (KW) test, followed by modeling using Lasso-regularized logistic regression and Support Vector Machine (SVM) methods. The models were evaluated and validated using the area under the curve (AUC) and decision curve analysis to determine the best-performing model.

The positive proportions of HER2 expression were 8.60% (52/658) in the training set and 5.60% (19/320) in the test set. Eight distinct models were developed to predict HER2 expression. Among these, the model utilizing RFE and Lasso-regularized logistic regression (LR-Lasso) exhibited the highest predictive performance, with AUC values of 0.7874 (95% CI: 0.7346-0.8402) in the training set and 0.8033 (95% CI: 0.7288-0.8788) in the test set. Compared to other models, this model provided a greater net benefit on the decision curve analysis. These results suggest that the proposed model can be effectively applied to predict HER2 expression in patients.

The HER2 prediction model demonstrated promising performance in predicting HER2 expression in gastric cancer patients.

Stroke is the second-leading global cause of death. The damage attributed to the immune storm triggered by ischemia-reperfusion injury (IRI) post-stroke is substantial. However, data on the transcriptomic dynamics of pyroptosis in IRI are limited. This study aimed to analyze the expression of key pyroptosis genes in stroke and their correlation with immune infiltration. Pyroptosis-related genes were identified from the obtained middle cerebral artery occlusion (MCAO) datasets. Differential expression and functional analyses of pyroptosis-related genes were performed, and differences in functional enrichment between high-risk and low-risk groups were determined. An MCAO diagnostic model was constructed and validated using selected pyroptosis-related genes with differential expression. High- and low-risk MCAO groups were constructed for expression and immune cell correlation analysis with pyroptosis-related hub genes. A regulatory network between pyroptosis-related hub genes and miRNA was also constructed, and protein domains were predicted. The expression of key pyroptosis genes was validated using an MCAO rat model. Twenty-five pyroptosis genes showed differential expression, including four hub genes, namely WISP2, MELK, SDF2L1, and AURKB. Characteristic genes were verified using real-time quantitative PCR analyses. The high- and low-risk groups showed significant expression differences for WISP2, MELK, and SDF2L1. In immune infiltration analysis, 12 immune cells showed differences in expression in MCAO samples. Further analysis demonstrated significant positive correlations between the pyroptosis-related hub gene SDF2L1 and immune cell-activated dendritic cells in the high-risk group and immune cell natural killer cells in the low-risk group. This study identified four pyroptosis-related hub genes, with elevated WISP2, MELK, and SDF2L1 expression closely associated with the high-risk group. The analysis of inflammatory cell types in immune infiltration can predict ischemic stroke risk levels and help to facilitate treatment.

Increasing evidence demonstrates that tryptophan metabolism is closely related to the development of nonalcoholic fatty liver disease (NAFLD). This study aimed to identify specific biomarkers of NAFLD associated with tryptophan metabolism and research its functional mechanism.

We downloaded NAFLD RNA-sequencing data from GSE89632 and GSE24807, and obtained tryptophan metabolism-related genes (TMRGs) from the MsigDB database. The R package limma and WGCNA were used to identify TMRGs-DEGs, and GO, KEGG and Cytoscape were used to analyze and visualize the data. Immune cell infiltration analysis was used to explore the immune mechanism of NAFLD and the biomarkers. We also validated extended levels of biomarkers.

We identified 375 NAFLD differentially expressed genes (DEGs) and 85 TMRGs-DEGs. GO/KEGG analysis revealed that TMRGs-DEGs were mainly enriched in triglyceride and cholesterol metabolism. ROC curves identified CCL20 (AUC = 0.917), CD160 (AUC = 0.933) and CYP7A1 (AUC = 1) as biomarkers of NAFLD. Immune infiltration analysis showed significant differences in ten immune cells, and the activation of dendritic cells and mast cells were highly positively correlated with NAFLD. CCL20, CD160 and CYP7A1 were highly correlated with M2 macrophage, neutrophil and mast cells activation, respectively. Twenty-seven TMRGs correlated with hub genes, and gene set enrichment analysis demonstrated their function in tryptophan- and lysine-containing metabolic process. We identified 41 therapeutic drug matches which corresponded to two hub genes and four drugs which co-targeted CCL20 and CYP7A1. Finally, three hub genes were validated in our mouse model.

CCL20, CD160 and CYP7A1 are tryptophan metabolism-related biomarkers of NAFLD, related to glycerol ester and cholesterol metabolism. We screened four compounds which co-target CCL29 and CYP7A1 to provide potential experimental drugs for NAFLD.

The Ubiquitin A (UBA) protein family contains seven members that protect themselves or their interacting proteins from proteasome degradation. The UBA protein family regulates cell proliferation, cell cycle, invasion, migration, apoptosis, autophagy, tissue differentiation, and immune response. With the deepening of research, the UBA protein family has been found to be abnormally expressed in a variety of tumor diseases, and the clarification of its relationship with tumor diseases can be used as a molecular therapeutic target and have an important role in the prognosis of tumors. In this paper, we review the structure, biological process, target therapy, and biomarkers of the UBA protein family to provide new ideas for the diagnosis and treatment of tumors.

This study looked at possible targets for hypertrophic cardiomyopathy (HCM), a condition marked by thickening of the ventricular wall, primarily in the left ventricle. We employed differential gene analysis and weighted gene co-expression network analysis (WGCNA) on samples. We then carried out an enrichment analysis. We also investigated the process of immunological infiltration. We employed six machine learning techniques and two protein-protein interaction (PPI) network gene selection approaches to search for the most characteristic gene (MCG). In the validation ladder, we verified the expression of MCG. Furthermore, we examined the MCG expression levels in HCM animal and cell models. Finally, we performed molecular docking and predicted potential medications for HCM treatment. 7975 differentially expressed genes (DEGs) were found in our study. We also identified 236 genes in the blue module using WGCNA. Screening at the transcriptome and protein levels was used to mine MCG. The final result screened CCAAT/Enhancer Binding Protein Delta (CEBPD) as MCG. We confirmed that MCG expression matched the outcomes of the experimental ladder. The level of CEBPD mRNA and protein was lowered in HCM animal and cellular models. Given that Abt-751 had the highest binding affinity to CEBPD, it might be a projected targeted medication. We found a new target gene for HCM called CEBPD, which is probably going to function by mitochondrial dysfunction. An innovative aim for the management or avoidance of HCM is offered by this analysis. Abt-751 may be a predicted targeted drug for HCM that had the greatest binding affinity with CEBPD.

While the FRAIL scale has been used in primary care, cluster analysis on frail patients in a hospital setting has not been performed.

To identify potential subtypes of frail patients, and develop a simple, clinically applicable model for improved patient management.

The study included 214 frail patients aged 65 and above who were hospitalized in a hospital in Beijing from September 2018 to April 2019. This study applied the K-means clustering algorithm to analyze 27 variables, determining the optimal cluster number using the Elbow method and Silhouette coefficient. Key variables for predictive modeling were identified through LASSO (least absolute shrinkage and selection operator) regression, SVM-RFE (support vector machine-recursive feature elimination), and random forest techniques. A logistic regression model was then developed to predict patient subtypes, aimed at enhancing clinical identification and management of frailty subtypes.

Clustering analysis distinguished two unique subgroups among the frail patients, revealing significant disparities in clinical characteristics and survival outcomes. One-year survival rates for Class 1 and Class 2 were 62.51% and 47.51%, respectively. The logistic regression model exhibited robust predictive capability, with an AUC (Area under curve) of 0.88. Validation through 1000 bootstrap resamples confirmed the model's reliability, with an average AUC of 0.8707 and a 95% CI (Confidence intervals) of 0.8572 to 0.8792.

This study identifies two frailty subtypes in a hospital setting using unsupervised machine learning, demonstrating significant differences in survival outcomes. Clinical Trial registration ChiCTR1800017204; date of reqistration: 07/18/2018.

Osteoporosis (OP), marked by reduced bone density and structural decay, poses a heightened risk of fractures. Our study formulates a predictive diagnostic model for OP by analyzing differential gene expression, thereby improving early diagnosis and therapeutic approaches.

Using GSE62402, GSE56815, and GSE35958 datasets from the Gene Expression Omnibus (GEO) database, we identified differentially expressed genes (DEGs) via R packages, and evaluated the underlying molecular mechanisms by network analysis. Immune checkpoint and drug sensitivity were analyzed to construct and validate diagnostic models. The single-sample gene-set enrichment analysis (ssGSEA) was used to assess immune cell infiltration; the CIBERSORT algorithm was used to evaluate immune cells within the different subtypes of OP.

The study identified 1,297 DEGs, with 14 DEGs related to autophagy, osteogenesis, and adipogenesis (AP&OG&AGRDEGs) showing significant expression differences between OP and control groups, including seven upregulated and seven downregulated genes (p-value < 0.05). The analysis results from gene ontology (GO), gene set enrichment analysis (GSEA), and the Kyoto encyclopedia of genes and genomes (KEGG) indicated that oxidative stress and inflammation-related signaling pathways are closely connected to OP. Immune checkpoint analysis identified differential expression of eight genes between OP patients and controls (p-value < 0.05). The ssGSEA findings showed significant variations in immune cell infiltration levels, particularly of natural killer cells, Th2 cells, mast cells, and plasmacytoid dendritic cells (p-value < 0.05). The diagnostic model, developed utilizing logistic regression, support vector machine (SVM), and the least absolute shrinkage and selection operator (LASSO), pinpointed nine pivotal genes-AKT1, NFKB1, TNF, CTNNB1, LMNA, BHLHE40, BMP4, WNT1, and COPS3-and confirmed their diagnostic efficacy through validation. In further subgroup analysis, eight types of immune cells were found to be differentially expressed across various risk groups. Subtype analysis based on ConsensusClusterPlus revealed differential expression of six key genes in distinct subtypes of OP.

This comprehensive study established a network of OP-associated genes, and provides insights into the molecular mechanisms involving immune responses in OP. It identified key diagnostic genes and analyzed immune cell infiltration to better understand OP pathogenesis. The study underscores the importance of personalized treatment and the potential role of immune modulation in managing OP.

Lupus nephritis (LN) is one of the most common and severe complications of systemic lupus erythematosus with unclear pathogenesis. The most accurate diagnosis criterion of LN is still renal biopsy and nowadays treatment strategies of LN are far from satisfactory. Cellular senescence is defined as the permanent cell cycle arrest marked by senescence-associated secretory phenotype (SASP), which has been proved to accelerate the mobility and mortality of patients with LN. The study is aimed to identify cellular senescence-related genes for LN.

Genes related to cellular senescence and LN were obtained from the MSigDB genetic database and GEO database respectively. Through differential gene analysis, Weighted Gene Go-expression Network Analysis (WGCNA) and machine learning algorithms, hub cellular senescence-related differentially expressed genes (CS-DEGs) were identified. By external validation, hub CS-DEGs were further filtered and the remaining genes were identified as biomarkers. We explored their potential physiopathologic function through GSEA.

We obtained 432 genes related to cellular senescence, 1,208 differentially expressed genes (DEGs) and 840 genes in the key gene module related to LN, which were intersected with each other for CS-DEGs. Subsequent Machine learning algorithms screened out six hub CS-DEGs and finally three hub CS-DEGs, ALOX5, PTGER2 and PRKCB passed through external validation, which were identified as biomarkers. The three biomarkers were enriched in "B Cell receptor signaling pathway" and "NF-kappa B signaling pathway" based on GESA results.

This study explored the potential relationship between cellular senescence and LN, and identified three biomarkers ALOX5, PTGER2, and PRKCB playing key roles in LN, which will provide new insights for the diagnosis and treatment of LN.

PE is a serious form of pregnancy-related hypertension. Hypoxia can induce cellular dysfunction, adversely affecting both the infant and the mother. This study aims to investigate the relationship between HRGs and the diagnosis of PE, seeking to enhance our understanding of potential molecular mechanisms and offer new perspectives for the detection and treatment of the condition. A WGCNA network was established to identify key genes significantly associated with traits of PE. LASSO, SVM-RFE, and RF were utilized to identify feature genes. Calibration curves and DCA were employed to assess the diagnostic performance of the comprehensive nomogram. Consensus clustering was applied to identify subtypes of PE. GSEA and the construction of a ceRNA network were used to explore the potential biological functions and regulatory mechanisms of the identified feature genes. Furthermore, ssGSEA was conducted to investigate the immune landscape associated with PE. We successfully identified three potential diagnostic biomarkers for PE: P4HA1, NDRG1, and BHLHE40. Furthermore, the nomogram exhibited strong diagnostic performance. In patients with PE, the abundance of pro-inflammatory immune cells was significantly elevated, reflecting characteristics of high infiltration. The levels of immune cells infiltration were significantly correlated with the expression of the identified feature genes. Notably, these feature genes may be closely linked to mitochondrial-related biological functions. In conclusion, our findings enhance the understanding of the pathological mechanisms underlying PE and open innovative avenues for the diagnosis and treatment of PE.

Fourier transform infrared (FT-IR) spectroscopy is an innovative diagnostic technique for improving early detection and personalized care for breast cancer patients. It allows rapid and accurate analysis of biological samples. Therefore, the purpose of this study was to assess the diagnostic accuracy of FT-IR spectroscopy for breast cancer, based on a comprehensive literature review.

An online electronic database systematic search was conducted using PubMed/Medline, Cochrane Library, and hand databases from March 28, 2024, to April 10, 2024. We included peer-reviewed journal articles in which FT-IR spectroscopy was used to acquire data on breast cancers and manuscripts published in English. All eligible studies were assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS) tool.

Serum, breast biopsies, blood plasma, specimen, and saliva samples were included in this study. This study revealed that breast cancer diagnosis using FT-IR spectroscopy with diagnostic algorithms had a sensitivity and specificity of approximately 98 % and 100 %, respectively. Almost all studies have used more than one algorithm to analyze spectral data. This finding showed that the sensitivity of FT-IR spectroscopy reported in six included studies was greater than 90 %.

Employing multivariate analysis coupled with FT-IR spectroscopy has shown promise in differentiating between healthy and cancerous breast tissue. This review revealed that FT-IR spectroscopy will be the next gold standard for breast cancer diagnosis. However, to draw definitive conclusions, larger-scale studies, external validation, real-world clinical trials, legislative considerations, and alternative methods such as Raman spectroscopy should be considered.

This study aims to elucidate the underlying mechanisms of pyroptosis in hypertension through bioinformatics and machine learning approaches. R language was utilized to integrate differentially expressed genes (DEGs) between hypertension samples and healthy control samples in GSE24752 and GSE75360 datasets, followed by GO analysis, KEGG enrichment analysis, and GSEA. Key genes were screened based on the expression levels of DEGs using logistic regression, LASSO regression, and support vector machine (SVM). A visualized protein-protein interaction regulatory network was constructed, and immune cell infiltration analysis was performed on integrated GEO datasets of hypertensive samples. Collect serum samples from hypertensive subjects and healthy control subjects for RT-qPCR detection of key gene expression. A total of 1005 DEGs were obtained from peripheral blood samples of 13 hypertension cases and 14 control samples. GO analysis, KEGG enrichment analysis, and GSEA revealed that the DEGs function synergistically in various biological pathways. LASSO regression and SVM identified six key genes related to pyroptosis (CASP7 (caspase-7), CYBB, NEK7, NLRP2, RAB5A, VDR (vitamin D receptor)). Immune infiltration analysis showed that activated B cell, effector memory CD8 T cell, immature B cell, MDSC, and T follicular helper cell accounted for the largest proportion of immune cells. RT-qPCR results indicated significantly higher relative expression levels of caspase-7 and vitamin D receptor in hypertensive samples compared to controls. These findings suggest that CASP7 and the vitamin D receptor gene may offer new research targets for the diagnosis and treatment of hypertension, and they also provide fresh evidence for the involvement of pyroptosis in hypertension.

Oxidative stress (OS) is a pivotal mechanism driving the progression of cardiovascular diseases, particularly heart failure (HF). However, the comprehensive characterisation of OS-related genes in HF remains largely unexplored. In the present study, we analysed single-cell RNA sequencing datasets from the Gene Expression Omnibus and OS gene sets from GeneCards. We identified 167 OS-related genes potentially linked to HF by applying algorithms, such as AUCell, UCell, singscore, ssgsea, and AddModuleScore, combined with correlation analysis. Subsequently, we used feature selection algorithms, including least absolute shrinkage and selection operator, XGBoost, Boruta, random forest, gradient boosting machines, decision trees, and support vector machine recursive feature elimination, to identify lumican (LUM) and procollagen C-endopeptidase enhancer 2 (PCOLCE2) as key biomarker candidates with significant diagnostic potential. Bulk RNA-sequencing confirmed their elevated expression in patients with HF, highlighting their predictive utility. Single-cell analysis further revealed their upregulation primarily in fibroblasts, emphasising their cell-specific role in HF. To validate these findings, we developed a transverse aortic constriction-induced HF mouse model that showed enhanced cardiac OS activity and significant PCOLCE2 upregulation in the HF group. These results provide strong evidence of the involvement of OS-related mechanisms in HF. Herein, we propose a diagnostic strategy that provides novel insights into the molecular mechanisms underlying HF. However, further studies are required to validate its clinical utility and ensure its application in the diagnosis of HF.

This study aimed to establish and validate the efficacy of a nomogram model, synthesized through the integration of multi-parametric magnetic resonance radiomics and clinical risk factors, for forecasting perineural invasion in rectal cancer. We retrospectively collected data from 108 patients with pathologically confirmed rectal adenocarcinoma who underwent preoperative multiparametric MRI at the First Affiliated Hospital of Bengbu Medical College between April 2019 and August 2023. This dataset was subsequently divided into training and validation sets following a ratio of 7:3. Both univariate and multivariate logistic regression analyses were implemented to identify independent clinical risk factors associated with perineural invasion (PNI) in rectal cancer. We manually delineated the region of interest (ROI) layer-by-layer on T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) sequences and extracted the image features. Five machine learning algorithms were used to construct radiomics model with the features selected by least absolute shrinkage and selection operator (LASSO) method. The optimal radiomics model was then selected and combined with clinical features to formulate a nomogram model. The model performance was evaluated using receiver operating characteristic (ROC) curve analysis, and its clinical value was assessed via decision curve analysis (DCA). Our final selection comprised 10 optimal radiological features and the SVM model showcased superior predictive efficiency and robustness among the five classifiers. The area under the curve (AUC) values of the nomogram model were 0.945 (0.899, 0.991) and 0.846 (0.703, 0.99) for the training and validation sets, respectively. The nomogram model developed in this study exhibited excellent predictive performance in foretelling PNI of rectal cancer, thereby offering valuable guidance for clinical decision-making. The nomogram could predict the perineural invasion status of rectal cancer in early stage.

BackgroundKnee osteoarthritis is a debilitating disease with a complex pathogenesis. Synovitis, which refers to inflammation of the synovial membrane surrounding the joint, is believed to play an important role in the development and progression of knee osteoarthritis. To better understand the molecular mechanisms underlying knee osteoarthritis, we conducted a comprehensive analysis of gene expression in knee osteoarthritis synovium using machine learning.MethodsDifferentially expressed genes between knee osteoarthritis and control synovial tissues were analyzed using the GSE55235 dataset. We employed several machine learning algorithms, including least absolute shrinkage and selection operator and support vector machine-recursive feature elimination, to screen for key genes. Then, we validated the key genes using an external dataset (GSE51588) and an in vitro knee osteoarthritis animal model. CIBERSORT was used to compare immune cell infiltration levels between knee osteoarthritis and control synovial tissues and determine their relationship with the key genes. Finally, we performed a Connectivity Map analysis to screen for potential small-molecule compounds. Moreover, we conducted single-cell RNA sequencing analysis using knee joint tissues to annotate different subtypes of cells.ResultsA total of 930 differentially expressed genes were identified. Least absolute shrinkage and selection operator regression and support vector machine-recursive feature elimination identified FOSL2 and RHoBTB1 as key genes. The expression levels of both genes were further validated in the GSE51588 dataset as well as verified through an in vitro experiment involving a knee osteoarthritis mouse model. Multiple significant correlation pairs were found between the immune cell infiltration levels. We unveiled the genetic basis of knee osteoarthritis using genome-wide association study and specific signaling pathways through gene set enrichment analysis. The GeneCards database was used to obtain 3032 pathogenic genes associated with knee osteoarthritis, and we found that RHoBTB1 expression was significantly negatively correlated and FOSL2 expression was significantly positively correlated with interleukin-1β expression. We predicted several small-molecule compounds based on Connectivity Map analysis. Finally, single-cell RNA sequencing analysis revealed the expression levels of the two key genes in chondrocytes and tissue stem cells.ConclusionFOSL2 and RHoBTB1 may play key roles in the pathogenesis of knee osteoarthritis, exhibiting correlations with immune cell infiltration levels. These findings indicate that these genes have potential as therapeutic targets. However, further research and validation are necessary to confirm their exact roles and therapeutic potential in knee osteoarthritis.

Colorectal polyps (CRPs) are precursors to colorectal cancer (CRC), and early detection is crucial for prevention. Traditional diagnostic methods are invasive, prompting a need for noninvasive biomarkers. This study aimed to identify urinary metabolite biomarkers for diagnosing CRPs and construct a diagnostic nomogram based on noninvasive urinary metabolite screening.

A total of 192 participants, including 64 CRP patients and 128 healthy controls, were recruited. Urine samples were analyzed using untargeted gas chromatography-mass spectrometry (GC-MS) and ultra-performance liquid chromatography-mass spectrometry (UPLC-MS). Metabolite screening was performed using weighted gene coexpression network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO), and support vector machine-recursive feature elimination (SVM-RFE). A diagnostic nomogram was developed based on identified metabolites, and its performance was evaluated using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA).

A total of 350 metabolites were identified, with 7 key metabolites significantly associated with CRP. Multivariate logistic regression analysis identified Saccharin (OR = 48.3, 95% CI: 4.44-525.32) and N-omega-acetylhistamine (OR = 27.91, 95% CI: 2.31-337.06) as significant risk factors for CRP, while N-methyl-L-proline, trimethylsilyl ester (OR = 0.08, 95% CI: 0.01-0.8) was a protective factor. A nomogram incorporating these metabolites demonstrated strong discriminatory power, with AUC values of 0.974 and 0.960 in the training and validation sets, respectively. Calibration plots and DCA confirmed the model's accuracy and clinical utility.

This study successfully identified seven urinary metabolites as potential noninvasive biomarkers for CRP. The constructed diagnostic nomogram, based on these metabolites, offers high predictive accuracy and clinical applicability, providing a promising tool for the early detection of CRP.

Keloids are a common complication that occurs after injury. The pathogenesis of this disease remains unknown. Therefore, identifying immune-related biomarkers and macrophage polarization types in keloids can provide new insights into their treatment.

In this study, keloid-related bulk RNA-seq data (GSE83286, GSE212954, GSE92566, and GSE90051) were obtained from the Gene Expression Omnibus (GEO) database. The datasets GSE83286, GSE212964, and GSE92566 were combined to form a training set, while GSE90051 was utilized as an external validation set. Differentially expressed genes (DEGs) were detected by comparing keloid and normal samples within the training set. Differentially expressed immune-related genes (DIRGs) were then determined by intersecting the DEGs with immune-related genes (IRGs). Based on the protein-protein interaction (PPI) network, the top 40 DIRGs were selected for further analyses. Weighted Gene Co-expression Network Analysis (WGCNA), in conjunction with three machine learning techniques - least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE), and random forest (RF) - employed to identify biomarkers. Subsequently, a nomogram model was constructed and validated. Single-cell RNA (scRNA) analysis was used to examine the expression of biomarkers at the cell-type level. Furthermore, since keloid is a chronic inflammatory disease and the abnormal polarization of macrophages is essential for the occurrence of this kind of disease, in this study we also endeavor to elucidate the state of macrophage polarization dysregulation within keloid, with the anticipation of generating novel concepts for the treatment of keloid. Finally, western blot (WB) and immunofluorescence (IF) analyses were carried out to confirm the expression levels of the biomarkers.

A total of 740 DEGs were identified in the training set, comprising 331 up-regulated genes and 409 down-regulated genes. After intersecting with the IRGs, 73 DIRGs were obtained. Subsequently, the top 40 DIRGs were chosen for further analysis. Eventually, two biomarkers, namely BMP1 and IL1R1, were identified through WGCNA and the three machine learning methods. Their expression levels were then verified by single-cell analysis, WB, and IF analysis. Additionally, it was found that the number of M2 macrophages significantly increased, while the number of M1 macrophages decreased in keloids compared to normal samples.

BMP1 and IL1R1 might function as novel biomarkers and potential therapeutic targets for keloid treatment. Moreover, upregulating M1 macrophages and downregulating M2 macrophages could represent a promising approach for the treatment of keloids.

The microarray and single-cell RNA-sequencing (scRNA-seq) datasets of hepatocellular carcinoma (HCC) were downloaded from the Gene Expression Omnibus (GEO) database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were used to identify HCC-related biomarkers. Based on an analysis of scRNA-seq data, several marker genes expressed on tumor cells have been identified. Three machine-learning algorithms were used to identify shared diagnostic genes. Furthermore, logistic regression analysis was conducted to re-evaluate and identify essential biomarkers, which were then employed to develop a diagnostic prediction model. Additionally, AutoDockTools were used for molecular docking to investigate the association between the most sensitive drug and the core proteins. 44 genes were obtained by intersecting the WGCNA results, marker genes from scRNA-seq data, and up-regulated DEGs. Three machine-learning algorithms refined CDKN3, PPIA, PRC1, GMNN, and CENPW as hub biomarkers. GMNN and PRC1 were further selected by logistic regression analysis to build a nomogram. The molecular docking results showed that the drug NPK76-II-72-1 had a good binding ability with the GMNN and PRC1 proteins. The results highlighted CDKN3, PPIA, PRC1, GMNN, and CENPW as potential detection biomarkers for HCC patients. Our research offers novel insights into the diagnosis and treatment of HCC.

Macrophage inflammatory response is the key driver in atherosclerosis development. However, transcriptional remodeling of macrophage inflammatory response remains largely unknown. In this study, transcriptional regulatory networks were constructed from human plaque microarray datasets. Differential analysis and subsequent machine learning algorithms were used to identify key transcriptional regulons. Multiple immune cell inference methods (including CIBERSORT, ssGSEA, MCP-counter, and xCell), single-cell RNA-seq of human plaques and immunofluorescence of human and mouse plaque samples reveal that the macrophage-specific transcriptional regulator, KDM2A, is critical for inflammatory response. Diagnostic analyses validate KDM2A expression in peripheral monocytes/macrophages is an excellent predictor of atherosclerosis development and progression. RNA-seq of mouse bone marrow-derived macrophages under oxidized low-density lipoprotein stimulation reveal KDM2A knockdown significantly represses pro-inflammatory, oxidative, and lipid uptake pathways. In vitro experiments confirmed KDM2A activates inflammation, oxidative stress and lipid accumulation in macrophages. Mechanistically, FYN was identified as a direct target of KDM2A by chromatin immunoprecipitation followed by sequencing and qPCR analysis. Specific inhibition of FYN restored the inflammatory response, oxidative stress, and intracellular lipid accumulation after transfection with KDM2A overexpression plasmid. Importantly, macrophage-specific knockdown of KDM2A in ApoE-/- mice fed a high-fat diet apparently attenuated plaque progression. Furthermore, the genetic association of KDM2A with atherosclerosis was validated by Mendelian randomization and colocalization analysis. A group of small molecules with the potential to target KDM2A has been identified through virtual screening, offering promising strategies for atherosclerosis treatment. The current study provides the novel role of KDM2A in macrophage inflammatory response of atherosclerosis through transcriptional regulation of FYN.

Sepsis is a life-threatening condition characterized by a dysregulated host response to infection, resulting in high mortality rates and complex clinical management. This study leverages transcriptomics and machine learning (ML) to identify critical biomarkers and therapeutic targets in sepsis. Analyzing microarray data from the Gene Expression Omnibus (GEO) datasets GSE28750, GSE26440, GSE13205, and GSE9960, we discovered three pivotal biomarkers that BMX (bone marrow tyrosine kinase gene on chromosome X), GRB10 (growth factor receptor bound protein 10), and GADD45A (growth arrest and DNA damage inducible alpha), exhibiting exceptional diagnostic accuracy (AUC >0.9). Functional enrichment analyses revealed that these genes play key roles in reactive oxygen species metabolism and immune response regulation. Specifically, GADD45A was positively correlated with eosinophils and inversely associated with activated NK cells, CD8 T cells, and activated memory CD4 T cells. BMX showed positive correlations with eosinophils, mast cells, and neutrophils, while GRB10 was linked to eosinophils and M2 macrophages. Additionally, we constructed a comprehensive mRNA-miRNA-lncRNA regulatory network, identifying key interactions that may drive sepsis pathogenesis. Molecular docking and dynamics simulations validated Bendroflumethiazide, Cianidanol, and Hexamidine as promising therapeutic agents targeting these biomarkers. In conclusion, this integrated approach provides profound insights into the molecular mechanisms underlying sepsis, pinpointing BMX, GRB10, and GADD45A as pivotal biomarkers and therapeutic targets. These findings significantly enhance our understanding of sepsis pathophysiology and lay the groundwork for developing personalized diagnostic and therapeutic strategies aimed at improving patient outcomes.

Diabetes mellitus-related erectile dysfunction (DMED) is characterized by complicated pathogenesis and unsatisfactory therapeutic remedies. Glycolysis plays an essential role in diabetic complications and whether it is involved in the process of DMED has not been expounded. The aim of this study was to investigate the genetic profiling of glycolysis and explore potential mechanisms for DMED.

Glycolysis-related genes (GRGs) and gene expression matrix of DMED were obtained from the molecular signatures database and gene expression omnibus dataset. Differentially expressed analysis and support vector machine-recursive feature elimination (SVM-RFE) method were both used to obtain hub GRGs. Interactive network and functional enrichment analyses were performed to clarify the associated biological roles of these genes. The expression profile of hub GRGs was validated in cavernous endothelial cells, animals, and clinical patients. The subpopulation distribution of hub GRGs was further identified. In addition, a miRNA-GRGs network was constructed and expression patterns as well as molecular functions of relevant miRNAs were explored and validated. In addition, the relationship between hypoxia and DMED was also uncovered.

Based on the combined analysis, 48 differentially expressed GRGs were obtained. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses revealed that these genes were significantly enriched in carbon metabolism and oxidoreductase activities. Then hub GRGs including down-regulated as well as up-regulated genes in DMED were identified ultimately. Among them, ALDOC, ANGPTL4, and CITED2 were well-validated. In addition, 334 glycolysis-related miRNAs were verified and they were involved in endoplasmic reticulum membrane activity, smooth muscle cell differentiation and angiogenesis. After validation of miRNA signature in DMED patients, miR-222-5p, let-7e-5p, miR-184, and miR-122-3p were identified as the promising glycolysis-related miRNA biomarkers in DMED.

We clarified the expression signature of GRGs in DMED based on multi-omics analysis for the first time. It will be significantly important to reveal pathological mechanisms and promising treatments in DMED.