|
1
|
Li X, Wang H, Li Y, Zhu Y, Zhai Y, Xing N, Ye X, Yang F. DNA methylation expression patterns predict outcome of clear cell renal cell carcinoma. Discov Oncol 2025; 16:934. [PMID: 40419839 DOI: 10.1007/s12672-025-02764-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/23/2025] [Accepted: 05/20/2025] [Indexed: 05/28/2025] Open
Abstract
OBJECTIVE To identify DNA methylation markers related to clear cell renal cell carcinoma (ccRCC) prognosis and construct a prognostic model. METHODS Methylation data from TCGA and GSE113501 dataset were analyzed. Differential analysis, univariate Cox regression, and LASSO regression were used to find survival-related CpG sites and build a risk score model. The model was evaluated by the area under the curve, and multivariate analysis determined risk factors. RESULTS We determined 13 CpGs that are significantly associated with prognosis through a series of regression analyses and established a risk model based on them. Patients were divided into a high-risk group and a low-risk group according to the median risk score. The results showed that there was a significant difference in the overall survival rate between the two groups (p < 0.001), and the area under the curve (AUC) of the model was greater than 0.8. Verified by the GSE113501 dataset, the model performed well in distinguishing ccRCC with different progression states. In addition, by combining methylation data with gene expression analysis, five methylation-related differentially expressed genes (LINC02541, SLAMF8, LPXN, LGALS12, EGFR) were identified, and their expression levels were significantly upregulated in tumor tissues. Multivariate analysis indicated that age, clinical stage, and methylation risk score were independent prognostic factors. CONCLUSION This study confirmed that DNA methylation markers can effectively predict the progression and prognosis of clear cell renal cell carcinoma (ccRCC), providing a highly efficient and minimally invasive assessment tool for clinical practice.
Collapse
Affiliation(s)
- Xuwen Li
- Department of Urology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Haoxi Wang
- Department of Urology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yajian Li
- Department of Urology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yihao Zhu
- Department of Urology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yabo Zhai
- Department of Urology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Nianzeng Xing
- Department of Urology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiongjun Ye
- Department of Urology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
| | - Feiya Yang
- Department of Urology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
| |
Collapse
|
|
2
|
Holbrook KL, Lee WY. Volatile Organic Metabolites as Potential Biomarkers for Genitourinary Cancers: Review of the Applications and Detection Methods. Metabolites 2025; 15:37. [PMID: 39852380 PMCID: PMC11767221 DOI: 10.3390/metabo15010037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2024] [Revised: 01/07/2025] [Accepted: 01/08/2025] [Indexed: 01/26/2025] Open
Abstract
Cancer is one of the leading causes of death globally, and is ranked second in the United States. Early detection is crucial for more effective treatment and a higher chance of survival rates, reducing burdens on individuals and societies. Genitourinary cancers, in particular, face significant challenges in early detection. Finding new and cost-effective diagnostic methods is of clinical need. Metabolomic-based approaches, notably volatile organic compound (VOC) analysis, have shown promise in detecting cancer. VOCs are small organic metabolites involved in biological processes and disease development. They can be detected in urine, breath, and blood samples, making them potential candidates for sensitive and non-invasive alternatives for early cancer detection. However, developing robust VOC detection methods remains a hurdle. This review outlines the current landscape of major genitourinary cancers (kidney, prostate, bladder, and testicular), including epidemiology, risk factors, and current diagnostic tools. Furthermore, it explores the applications of using VOCs as cancer biomarkers, various analytical techniques, and comparisons of extraction and detection methods across different biospecimens. The potential use of VOCs in detection, monitoring disease progression, and treatment responses in the field of genitourinary oncology is examined.
Collapse
Affiliation(s)
| | - Wen-Yee Lee
- Department of Chemistry and Biochemistry, University of Texas at El Paso, El Paso, TX 79968, USA;
| |
Collapse
|
|
3
|
Gerke MB, Jansen CS, Bilen MA. Circulating Tumor DNA in Genitourinary Cancers: Detection, Prognostics, and Therapeutic Implications. Cancers (Basel) 2024; 16:2280. [PMID: 38927984 PMCID: PMC11201475 DOI: 10.3390/cancers16122280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2024] [Revised: 06/18/2024] [Accepted: 06/18/2024] [Indexed: 06/28/2024] Open
Abstract
CtDNA is emerging as a non-invasive clinical detection method for several cancers, including genitourinary (GU) cancers such as prostate cancer, bladder cancer, and renal cell carcinoma (RCC). CtDNA assays have shown promise in early detection of GU cancers, providing prognostic information, assessing real-time treatment response, and detecting residual disease and relapse. The ease of obtaining a "liquid biopsy" from blood or urine in GU cancers enhances its potential to be used as a biomarker. Interrogating these "liquid biopsies" for ctDNA can then be used to detect common cancer mutations, novel genomic alterations, or epigenetic modifications. CtDNA has undergone investigation in numerous clinical trials, which could address clinical needs in GU cancers, for instance, earlier detection in RCC, therapeutic response prediction in castration-resistant prostate cancer, and monitoring for recurrence in bladder cancers. The utilization of liquid biopsy for ctDNA analysis provides a promising method of advancing precision medicine within the field of GU cancers.
Collapse
Affiliation(s)
- Margo B. Gerke
- Emory University School of Medicine, Atlanta, GA 30322, USA; (M.B.G.); (C.S.J.)
| | - Caroline S. Jansen
- Emory University School of Medicine, Atlanta, GA 30322, USA; (M.B.G.); (C.S.J.)
- Winship Cancer Institute of Emory University, Atlanta, GA 30322, USA
| | - Mehmet A. Bilen
- Winship Cancer Institute of Emory University, Atlanta, GA 30322, USA
- Department of Hematology and Medical Oncology, Emory University School of Medicine, Atlanta, GA 30322, USA
| |
Collapse
|
|
4
|
Yuan T, Edelmann D, Fan Z, Alwers E, Kather JN, Brenner H, Hoffmeister M. Machine learning in the identification of prognostic DNA methylation biomarkers among patients with cancer: A systematic review of epigenome-wide studies. Artif Intell Med 2023; 143:102589. [PMID: 37673571 DOI: 10.1016/j.artmed.2023.102589] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 04/19/2023] [Accepted: 04/30/2023] [Indexed: 09/08/2023]
Abstract
BACKGROUND DNA methylation biomarkers have great potential in improving prognostic classification systems for patients with cancer. Machine learning (ML)-based analytic techniques might help overcome the challenges of analyzing high-dimensional data in relatively small sample sizes. This systematic review summarizes the current use of ML-based methods in epigenome-wide studies for the identification of DNA methylation signatures associated with cancer prognosis. METHODS We searched three electronic databases including PubMed, EMBASE, and Web of Science for articles published until 2 January 2023. ML-based methods and workflows used to identify DNA methylation signatures associated with cancer prognosis were extracted and summarized. Two authors independently assessed the methodological quality of included studies by a seven-item checklist adapted from 'A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies (PROBAST)' and from the 'Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK). Different ML methods and workflows used in included studies were summarized and visualized by a sunburst chart, a bubble chart, and Sankey diagrams, respectively. RESULTS Eighty-three studies were included in this review. Three major types of ML-based workflows were identified. 1) unsupervised clustering, 2) supervised feature selection, and 3) deep learning-based feature transformation. For the three workflows, the most frequently used ML techniques were consensus clustering, least absolute shrinkage and selection operator (LASSO), and autoencoder, respectively. The systematic review revealed that the performance of these approaches has not been adequately evaluated yet and that methodological and reporting flaws were common in the identified studies using ML techniques. CONCLUSIONS There is great heterogeneity in ML-based methodological strategies used by epigenome-wide studies to identify DNA methylation markers associated with cancer prognosis. In theory, most existing workflows could not handle the high multi-collinearity and potentially non-linearity interactions in epigenome-wide DNA methylation data. Benchmarking studies are needed to compare the relative performance of various approaches for specific cancer types. Adherence to relevant methodological and reporting guidelines are urgently needed.
Collapse
Affiliation(s)
- Tanwei Yuan
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany; Medical Faculty Heidelberg, Heidelberg University, Heidelberg, Germany
| | - Dominic Edelmann
- Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Ziwen Fan
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Elizabeth Alwers
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jakob Nikolas Kather
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany; Medical Oncology, National Center of Tumour Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany; Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.
| |
Collapse
|
|
5
|
Nam AR, Heo M, Lee KH, Kim JY, Won SH, Cho JY. The landscape of PBMC methylome in canine mammary tumors reveals the epigenetic regulation of immune marker genes and its potential application in predicting tumor malignancy. BMC Genomics 2023; 24:403. [PMID: 37460953 DOI: 10.1186/s12864-023-09471-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 06/20/2023] [Indexed: 07/20/2023] Open
Abstract
BACKGROUND Genome-wide dysregulation of CpG methylation accompanies tumor progression and characteristic states of cancer cells, prompting a rationale for biomarker development. Understanding how the archetypic epigenetic modification determines systemic contributions of immune cell types is the key to further clinical benefits. RESULTS In this study, we characterized the differential DNA methylome landscapes of peripheral blood mononuclear cells (PBMCs) from 76 canines using methylated CpG-binding domain sequencing (MBD-seq). Through gene set enrichment analysis, we discovered that genes involved in the growth and differentiation of T- and B-cells are highly methylated in tumor PBMCs. We also revealed the increased methylation at single CpG resolution and reversed expression in representative marker genes regulating immune cell proliferation (BACH2, SH2D1A, TXK, UHRF1). Furthermore, we utilized the PBMC methylome to effectively differentiate between benign and malignant tumors and the presence of mammary gland tumors through a machine-learning approach. CONCLUSIONS This research contributes to a better knowledge of the comprehensive epigenetic regulation of circulating immune cells responding to tumors and suggests a new framework for identifying benign and malignant cancers using genome-wide methylome.
Collapse
Affiliation(s)
- A-Reum Nam
- Department of Biochemistry, College of Veterinary Medicine, Seoul National University, 1 Gwanak-Ro, Gwanak-Gu, Seoul, 08826, Republic of Korea
- BK21 Plus and Research Institute for Veterinary Science, Seoul National University, Seoul, 08826, Republic of Korea
- Comparative Medicine Disease Research Center, Seoul National University, Seoul, 08826, Republic of Korea
| | - Min Heo
- Comparative Medicine Disease Research Center, Seoul National University, Seoul, 08826, Republic of Korea
- Interdisciplinary Program of Bioinformatics, College of Natural Sciences, Seoul National University, Seoul, 08826, Republic of Korea
| | - Kang-Hoon Lee
- Department of Biochemistry, College of Veterinary Medicine, Seoul National University, 1 Gwanak-Ro, Gwanak-Gu, Seoul, 08826, Republic of Korea
- BK21 Plus and Research Institute for Veterinary Science, Seoul National University, Seoul, 08826, Republic of Korea
| | - Ji-Yoon Kim
- Department of Biochemistry, College of Veterinary Medicine, Seoul National University, 1 Gwanak-Ro, Gwanak-Gu, Seoul, 08826, Republic of Korea
- BK21 Plus and Research Institute for Veterinary Science, Seoul National University, Seoul, 08826, Republic of Korea
- Comparative Medicine Disease Research Center, Seoul National University, Seoul, 08826, Republic of Korea
| | - Sung-Ho Won
- Comparative Medicine Disease Research Center, Seoul National University, Seoul, 08826, Republic of Korea
- Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, 08826, Republic of Korea
| | - Je-Yoel Cho
- Department of Biochemistry, College of Veterinary Medicine, Seoul National University, 1 Gwanak-Ro, Gwanak-Gu, Seoul, 08826, Republic of Korea.
- BK21 Plus and Research Institute for Veterinary Science, Seoul National University, Seoul, 08826, Republic of Korea.
- Comparative Medicine Disease Research Center, Seoul National University, Seoul, 08826, Republic of Korea.
| |
Collapse
|
|
6
|
Choi JM, Park C, Chae H. meth-SemiCancer: a cancer subtype classification framework via semi-supervised learning utilizing DNA methylation profiles. BMC Bioinformatics 2023; 24:168. [PMID: 37101254 PMCID: PMC10131478 DOI: 10.1186/s12859-023-05272-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 04/05/2023] [Indexed: 04/28/2023] Open
Abstract
BACKGROUND Identification of the cancer subtype plays a crucial role to provide an accurate diagnosis and proper treatment to improve the clinical outcomes of patients. Recent studies have shown that DNA methylation is one of the key factors for tumorigenesis and tumor growth, where the DNA methylation signatures have the potential to be utilized as cancer subtype-specific markers. However, due to the high dimensionality and the low number of DNA methylome cancer samples with the subtype information, still, to date, a cancer subtype classification method utilizing DNA methylome datasets has not been proposed. RESULTS In this paper, we present meth-SemiCancer, a semi-supervised cancer subtype classification framework based on DNA methylation profiles. The proposed model was first pre-trained based on the methylation datasets with the cancer subtype labels. After that, meth-SemiCancer generated the pseudo-subtypes for the cancer datasets without subtype information based on the model's prediction. Finally, fine-tuning was performed utilizing both the labeled and unlabeled datasets. CONCLUSIONS From the performance comparison with the standard machine learning-based classifiers, meth-SemiCancer achieved the highest average F1-score and Matthews correlation coefficient, outperforming other methods. Fine-tuning the model with the unlabeled patient samples by providing the proper pseudo-subtypes, encouraged meth-SemiCancer to generalize better than the supervised neural network-based subtype classification method. meth-SemiCancer is publicly available at https://github.com/cbi-bioinfo/meth-SemiCancer .
Collapse
Affiliation(s)
- Joung Min Choi
- Department of Computer Science, Virginia Tech, Blacksburg, USA
| | - Chaelin Park
- Division of Computer Science, Sookmyung Women's University, Seoul, Republic of Korea
| | - Heejoon Chae
- Division of Computer Science, Sookmyung Women's University, Seoul, Republic of Korea.
| |
Collapse
|
|
7
|
Jin J, Xie Y, Zhang JS, Wang JQ, Dai SJ, He WF, Li SY, Ashby CR, Chen ZS, He Q. Sunitinib resistance in renal cell carcinoma: From molecular mechanisms to predictive biomarkers. Drug Resist Updat 2023; 67:100929. [PMID: 36739809 DOI: 10.1016/j.drup.2023.100929] [Citation(s) in RCA: 68] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Revised: 01/13/2023] [Accepted: 01/14/2023] [Indexed: 01/19/2023]
Abstract
Currently, renal cell carcinoma (RCC) is the most prevalent type of kidney cancer. Targeted therapy has replaced radiation therapy and chemotherapy as the main treatment option for RCC due to the lack of significant efficacy with these conventional therapeutic regimens. Sunitinib, a drug used to treat gastrointestinal tumors and renal cell carcinoma, inhibits the tyrosine kinase activity of a number of receptor tyrosine kinases, including vascular endothelial growth factor receptor (VEGFR), platelet-derived growth factor receptor (PDGFR), c-Kit, rearranged during transfection (RET) and fms-related receptor tyrosine kinase 3 (Flt3). Although sunitinib has been shown to be efficacious in the treatment of patients with advanced RCC, a significant number of patients have primary resistance to sunitinib or acquired drug resistance within the 6-15 months of therapy. Thus, in order to develop more efficacious and long-lasting treatment strategies for patients with advanced RCC, it will be crucial to ascertain how to overcome sunitinib resistance that is produced by various drug resistance mechanisms. In this review, we discuss: 1) molecular mechanisms of sunitinib resistance; 2) strategies to overcome sunitinib resistance and 3) potential predictive biomarkers of sunitinib resistance.
Collapse
Affiliation(s)
- Juan Jin
- Department of Nephrology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, Zhejiang 310003, China
| | - Yuhao Xie
- Institute for Biotechnology, St. John's University, Queens, NY 11439, USA; Department of Pharmaceutical Sciences, College of Pharmacy and Health Sciences, St. John's University, Queens, NY 11439, USA
| | - Jin-Shi Zhang
- Urology & Nephrology Center, Department of Nephrology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, Zhejiang 310014, China
| | - Jing-Quan Wang
- Department of Pharmaceutical Sciences, College of Pharmacy and Health Sciences, St. John's University, Queens, NY 11439, USA
| | - Shi-Jie Dai
- Zhejiang Eyoung Pharmaceutical Research and Development Center, Hangzhou, Zhejiang 311258, China
| | - Wen-Fang He
- Department of Nephrology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, Zhejiang 310003, China
| | - Shou-Ye Li
- Zhejiang Eyoung Pharmaceutical Research and Development Center, Hangzhou, Zhejiang 311258, China
| | - Charles R Ashby
- Department of Pharmaceutical Sciences, College of Pharmacy and Health Sciences, St. John's University, Queens, NY 11439, USA
| | - Zhe-Sheng Chen
- Institute for Biotechnology, St. John's University, Queens, NY 11439, USA; Department of Pharmaceutical Sciences, College of Pharmacy and Health Sciences, St. John's University, Queens, NY 11439, USA.
| | - Qiang He
- Department of Nephrology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Traditional Chinese Medicine), Hangzhou, Zhejiang 310003, China.
| |
Collapse
|
|
8
|
Urine Molecular Biomarkers for Detection and Follow-Up of Small Renal Masses. Int J Mol Sci 2022; 23:ijms232416110. [PMID: 36555747 PMCID: PMC9785854 DOI: 10.3390/ijms232416110] [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: 11/15/2022] [Revised: 12/09/2022] [Accepted: 12/14/2022] [Indexed: 12/23/2022] Open
Abstract
Active surveillance (AS) is the best strategy for small renal masses (SRMs) management; however, reliable methods for early detection and disease aggressiveness prediction are urgently needed. The aim of the present study was to validate DNA methylation biomarkers for non-invasive SRM detection and prognosis. The levels of methylated genes TFAP2B, TAC1, PCDH8, ZNF677, FLRT2, and FBN2 were evaluated in 165 serial urine samples prospectively collected from 39 patients diagnosed with SRM, specifically renal cell carcinoma (RCC), before and during the AS via quantitative methylation-specific polymerase chain reaction. Voided urine samples from 92 asymptomatic volunteers were used as the control. Significantly higher methylated TFAP2B, TAC1, PCDH8, ZNF677, and FLRT2 levels and/or frequencies were detected in SRM patients' urine samples as compared to the control. The highest diagnostic power (AUC = 0.74) was observed for the four biomarkers panel with 92% sensitivity and 52% specificity. Methylated PCDH8 level positively correlated with SRM size at diagnosis, while TFAP2B had the opposite effect and was related to SRM progression. To sum up, SRMs contribute significantly to the amount of methylated DNA detectable in urine, which might be used for very early RCC detection. Moreover, PCDH8 and TFAP2B methylation have the potential to be prognostic biomarkers for SRMs.
Collapse
|
|
9
|
Rossi SH, Newsham I, Pita S, Brennan K, Park G, Smith CG, Lach RP, Mitchell T, Huang J, Babbage A, Warren AY, Leppert JT, Stewart GD, Gevaert O, Massie CE, Samarajiwa SA. Accurate detection of benign and malignant renal tumor subtypes with MethylBoostER: An epigenetic marker-driven learning framework. SCIENCE ADVANCES 2022; 8:eabn9828. [PMID: 36170366 PMCID: PMC9519038 DOI: 10.1126/sciadv.abn9828] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 08/10/2022] [Indexed: 06/01/2023]
Abstract
Current gold standard diagnostic strategies are unable to accurately differentiate malignant from benign small renal masses preoperatively; consequently, 20% of patients undergo unnecessary surgery. Devising a more confident presurgical diagnosis is key to improving treatment decision-making. We therefore developed MethylBoostER, a machine learning model leveraging DNA methylation data from 1228 tissue samples, to classify pathological subtypes of renal tumors (benign oncocytoma, clear cell, papillary, and chromophobe RCC) and normal kidney. The prediction accuracy in the testing set was 0.960, with class-wise ROC AUCs >0.988 for all classes. External validation was performed on >500 samples from four independent datasets, achieving AUCs >0.89 for all classes and average accuracies of 0.824, 0.703, 0.875, and 0.894 for the four datasets. Furthermore, consistent classification of multiregion samples (N = 185) from the same patient demonstrates that methylation heterogeneity does not limit model applicability. Following further clinical studies, MethylBoostER could facilitate a more confident presurgical diagnosis to guide treatment decision-making in the future.
Collapse
Affiliation(s)
- Sabrina H. Rossi
- Department of Oncology, University of Cambridge, Hutchison–MRC Research Centre, Cambridge Biomedical Campus, Cambridge, UK
- Early Cancer Institute, Cancer Research UK Cambridge Centre, Cambridge Biomedical Campus, Cambridge, UK
| | - Izzy Newsham
- MRC Cancer Unit, University of Cambridge, Hutchison–MRC Research Centre, Cambridge Biomedical Campus, Cambridge, UK
| | - Sara Pita
- Department of Oncology, University of Cambridge, Hutchison–MRC Research Centre, Cambridge Biomedical Campus, Cambridge, UK
- Early Cancer Institute, Cancer Research UK Cambridge Centre, Cambridge Biomedical Campus, Cambridge, UK
| | - Kevin Brennan
- Stanford Centre for Biomedical Informatics Research, Department of Medicine and Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Gahee Park
- Department of Oncology, University of Cambridge, Hutchison–MRC Research Centre, Cambridge Biomedical Campus, Cambridge, UK
- Early Cancer Institute, Cancer Research UK Cambridge Centre, Cambridge Biomedical Campus, Cambridge, UK
| | - Christopher G. Smith
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
- Cancer Research UK Major Centre, Cambridge, UK
| | - Radoslaw P. Lach
- Department of Oncology, University of Cambridge, Hutchison–MRC Research Centre, Cambridge Biomedical Campus, Cambridge, UK
- Early Cancer Institute, Cancer Research UK Cambridge Centre, Cambridge Biomedical Campus, Cambridge, UK
| | - Thomas Mitchell
- Department of Surgery, University of Cambridge, Addenbrooke’s Hospital, Cambridge Biomedical Campus, Cambridge, UK
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Junfan Huang
- MRC Cancer Unit, University of Cambridge, Hutchison–MRC Research Centre, Cambridge Biomedical Campus, Cambridge, UK
| | - Anne Babbage
- Department of Oncology, University of Cambridge, Hutchison–MRC Research Centre, Cambridge Biomedical Campus, Cambridge, UK
- Early Cancer Institute, Cancer Research UK Cambridge Centre, Cambridge Biomedical Campus, Cambridge, UK
| | - Anne Y. Warren
- Department of Histopathology, University of Cambridge, Addenbrooke’s Hospital, Cambridge Biomedical Campus, Cambridge, UK
| | - John T. Leppert
- Department of Urology, Stanford University School of Medicine, Stanford University, Stanford, CA, USA
- Urology Surgical Service, VA Palo Alto Health Care System, Palo Alto, CA 94304, USA
| | - Grant D. Stewart
- Department of Surgery, University of Cambridge, Addenbrooke’s Hospital, Cambridge Biomedical Campus, Cambridge, UK
| | - Olivier Gevaert
- Stanford Centre for Biomedical Informatics Research, Department of Medicine and Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Charles E. Massie
- Department of Oncology, University of Cambridge, Hutchison–MRC Research Centre, Cambridge Biomedical Campus, Cambridge, UK
- Early Cancer Institute, Cancer Research UK Cambridge Centre, Cambridge Biomedical Campus, Cambridge, UK
| | - Shamith A. Samarajiwa
- MRC Cancer Unit, University of Cambridge, Hutchison–MRC Research Centre, Cambridge Biomedical Campus, Cambridge, UK
| |
Collapse
|
|
10
|
Xu K, Wu Y, Chi H, Li Y, She Y, Yin X, Liu X, He B, Li X, Du H. SLC22A8: An indicator for tumor immune microenvironment and prognosis of ccRCC from a comprehensive analysis of bioinformatics. Medicine (Baltimore) 2022; 101:e30270. [PMID: 36123895 PMCID: PMC9478252 DOI: 10.1097/md.0000000000030270] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 07/15/2022] [Indexed: 11/25/2022] Open
Abstract
Clear cell renal cell carcinoma (ccRCC) is one of the most common renal malignancies worldwide. SLC22A8 plays a key role in renal excretion of organic anions. However, its role in ccRCC remains unclear; therefore, this study aimed to elucidate the relationship between SLC22A8 and ccRCC. The The Cancer Genome Atlas-kidney renal clear cell carcinoma cohort was included in this study. The Wilcoxon signed-rank test and logistic regression were used to analyze the relationship between SLC22A8 expression and clinicopathological characteristics. Multifactorial analysis and Kaplan-Meier survival curves were adopted for correlation between SLC22A8 expression and clinicopathological parameters and overall survival. Utilizing the UALCAN database, the correlation of the expression levels of SLC22A8 DNA methylation in ccRCC was explored. Immunological characterization of SLC22A8 regarding the ccRCC tumor microenvironment was carried out by the single sample Gene Set Enrichment Analysis algorithm and the CIBERSORT algorithm. With the CellMiner database, the analysis of the association between SLC22A8 gene expression and drug sensitivity was further performed. Eventually, gene ontology and Kyoto Encyclopedia of Gene and Genome enrichment analyses were applied to identify the functional and signaling pathways involved in SLC22A8. SLC22A8 expression is associated with age, grade, stage, and tumor status. SLC22A8 protein expression levels, phosphorylated protein levels, and DNA methylation expression levels were lower in ccRCC tissues than in normal tissues, and low methylation levels predicted poor overall survival. Comprehensive analysis of tumor immune infiltration and the tumor microenvironment indicated a higher level of overall immunity in the SLC22A8 low expression group. Gene Enrichment Analysis results showed that low expression of SLC22A8 was associated with immune pathways, such as phagocytosis recognition and humoral immune response. SLC22A8 expression was significantly correlated with survival and immune infiltration in ccRCC and can be used as a prognostic biomarker for ccRCC.
Collapse
Affiliation(s)
- Ke Xu
- Department of Oncology, Chongqing General Hospital, Chongqing, China
| | - Yuni Wu
- Department of Oncology, Chongqing General Hospital, Chongqing, China
| | - Hao Chi
- Clinical Medical College, Southwest Medical University, Luzhou, Sichuan, China
| | - Yunyue Li
- Queen Mary College, Medical School of Nanchang University, Nanchang, Jiangxi, China
| | - Yuchen She
- Clinical Medical College, Southwest Medical University, Luzhou, Sichuan, China
| | - Xisheng Yin
- Clinical Medical College, Southwest Medical University, Luzhou, Sichuan, China
| | - Xin Liu
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China
| | - Bingsheng He
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, China
| | - Xiaosong Li
- Clinical Molecular Medicine Testing Center, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Hongjuan Du
- Department of Oncology, Chongqing General Hospital, Chongqing, China
| |
Collapse
|
|
11
|
Epidemiology and Prevention of Renal Cell Carcinoma. Cancers (Basel) 2022; 14:cancers14164059. [PMID: 36011051 PMCID: PMC9406474 DOI: 10.3390/cancers14164059] [Citation(s) in RCA: 62] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 08/16/2022] [Accepted: 08/18/2022] [Indexed: 11/17/2022] Open
Abstract
With 400,000 diagnosed and 180,000 deaths in 2020, renal cell carcinoma (RCC) accounts for 2.4% of all cancer diagnoses worldwide. The highest disease burden developed countries, primarily in Europe and North America. Incidence is projected to increase in the future as more countries shift to Western lifestyles. Risk factors for RCC include fixed factors such as gender, age, and hereditary diseases, as well as intervening factors such as smoking, obesity, hypertension, diabetes, diet and alcohol, and occupational exposure. Intervening factors in primary prevention, understanding of congenital risk factors and the establishment of early diagnostic tools are important for RCC. This review will discuss RCC epidemiology, risk factors, and biomarkers involved in reducing incidence and improving survival.
Collapse
|
|
12
|
Liu S, Lu Y, Geng D. Molecular Subgroup Classification in Alzheimer's Disease by Transcriptomic Profiles. J Mol Neurosci 2022; 72:866-879. [PMID: 35080766 DOI: 10.1007/s12031-021-01957-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Accepted: 12/08/2021] [Indexed: 12/19/2022]
Abstract
Alzheimer's disease (AD) is a progressive cognitive disorder that occurs worldwide, and the lack of disease-modifying targets and pathways is a pressing issue. This study aimed to provide new targets and pathways by performing molecular subgroup classification. After normalizing the collected data, the subgroup number was confirmed with consensus clustering. Comparisons of clinical features among subgroups were conducted to clarify the clinical traits of each subgroup. Subgroup-specific genes were identified to perform weighted gene coexpression analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were carried out. Next, gene set enrichment analysis (GSEA) was performed. Protein-protein interaction networks were built to screen core genes and in each subgroup to perform Spearman correlation analysis with clinical traits. Sequencing profiles of 1068 AD samples collected from 2 datasets were classified into 3 subgroups. Clinical comparisons revealed that patients in subgroup III tended to be younger, while their pathological grades were the most severe. WGCNA detected four gene modules, and the turquoise module, where the dopaminergic synapse pathway was enriched, was related to subgroup I. The neurotrophin signaling pathway and TGF-beta signaling pathway were robustly enriched in the blue and brown modules, respectively, in subgroup III. Moreover, 3 hub genes in subgroup I were negatively correlated with the sum of neurofibrillary tangle (Nft) density. Conversely, hub genes in subgroups II and III exhibited positive correlations with the sum of Nft density. These results provide new pathways and targets for AD treatment.
Collapse
Affiliation(s)
- Sha Liu
- Department of Neurology, Affiliated Hospital of Xuzhou Medical University, West Huaihai Road 99, Xuzhou, 221002, Jiangsu, China
| | - Yan Lu
- Department of Neurology, The Municipal Hospital, Xuzhou Medical University, Xuzhou, 221116, Jiangsu, China
| | - Deqin Geng
- Department of Neurology, Affiliated Hospital of Xuzhou Medical University, West Huaihai Road 99, Xuzhou, 221002, Jiangsu, China.
| |
Collapse
|
|
13
|
Papanicolau-Sengos A, Aldape K. DNA Methylation Profiling: An Emerging Paradigm for Cancer Diagnosis. ANNUAL REVIEW OF PATHOLOGY-MECHANISMS OF DISEASE 2021; 17:295-321. [PMID: 34736341 DOI: 10.1146/annurev-pathol-042220-022304] [Citation(s) in RCA: 134] [Impact Index Per Article: 33.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Histomorphology has been a mainstay of cancer diagnosis in anatomic pathology for many years. DNA methylation profiling is an additional emerging tool that will serve as an adjunct to increase accuracy of pathological diagnosis. Genome-wide interrogation of DNA methylation signatures, in conjunction with machine learning methods, has allowed for the creation of clinical-grade classifiers, most prominently in central nervous system and soft tissue tumors. Tumor DNA methylation profiling has led to the identification of new entities and the consolidation of morphologically disparate cancers into biologically coherent entities, and it will progressively become mainstream in the future. In addition, DNA methylation patterns in circulating tumor DNA hold great promise for minimally invasive cancer detection and classification. Despite practical challenges that accompany any new technology, methylation profiling is here to stay and will become increasingly utilized as a cancer diagnostic tool across a range of tumor types. Expected final online publication date for the Annual Review of Pathology: Mechanisms of Disease, Volume 17 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
Collapse
Affiliation(s)
| | - Kenneth Aldape
- Laboratory of Pathology, National Cancer Institute, Bethesda, Maryland 20892, USA; ,
| |
Collapse
|
|
14
|
Epigenetic Biomarkers of Renal Cell Carcinoma for Liquid Biopsy Tests. Int J Mol Sci 2021; 22:ijms22168846. [PMID: 34445557 PMCID: PMC8396354 DOI: 10.3390/ijms22168846] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 08/06/2021] [Accepted: 08/13/2021] [Indexed: 12/16/2022] Open
Abstract
Renal cell carcinomas (RCC) account for 2–3% of the global cancer burden and are characterized by the highest mortality rate among all genitourinary cancers. However, excluding conventional imagining approaches, there are no reliable diagnostic and prognostic tools available for clinical use at present. Liquid biopsies, such as urine, serum, and plasma, contain a significant amount of tumor-derived nucleic acids, which may serve as non-invasive biomarkers that are particularly useful for early cancer detection, follow-up, and personalization of treatment. Changes in epigenetic phenomena, such as DNA methylation level, expression of microRNAs (miRNAs), and long noncoding RNAs (lncRNAs), are observed early during cancer development and are easily detectable in biofluids when morphological changes are still undetermined by conventional diagnostic tools. Here, we reviewed recent advances made in the development of liquid biopsy-derived DNA methylation-, miRNAs- and lncRNAs-based biomarkers for RCC, with an emphasis on the performance characteristics. In the last two decades, a mass of circulating epigenetic biomarkers of RCC were suggested, however, most of the studies done thus far analyzed biomarkers selected from the literature, used relatively miniature, local, and heterogeneous cohorts, and suffered from a lack of sufficient validations. In summary, for improved translation into the clinical setting, there is considerable demand for the validation of the existing pool of RCC biomarkers and the discovery of novel ones with better performance and clinical utility.
Collapse
|
|
15
|
Chan DW, Lam WY, Chen F, Yung MMH, Chan YS, Chan WS, He F, Liu SS, Chan KKL, Li B, Ngan HYS. Genome-wide DNA methylome analysis identifies methylation signatures associated with survival and drug resistance of ovarian cancers. Clin Epigenetics 2021; 13:142. [PMID: 34294135 PMCID: PMC8296615 DOI: 10.1186/s13148-021-01130-5] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 07/12/2021] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND In contrast to stable genetic events, epigenetic changes are highly plastic and play crucial roles in tumor evolution and development. Epithelial ovarian cancer (EOC) is a highly heterogeneous disease that is generally associated with poor prognosis and treatment failure. Profiling epigenome-wide DNA methylation status is therefore essential to better characterize the impact of epigenetic alterations on the heterogeneity of EOC. METHODS An epigenome-wide association study was conducted to evaluate global DNA methylation in a retrospective cohort of 80 mixed subtypes of primary ovarian cancers and 30 patients with high-grade serous ovarian carcinoma (HGSOC). Three demethylating agents, azacytidine, decitabine, and thioguanine, were tested their anti-cancer and anti-chemoresistant effects on HGSOC cells. RESULTS Global DNA hypermethylation was significantly associated with high-grade tumors, platinum resistance, and poor prognosis. We determined that 9313 differentially methylated probes (DMPs) were enriched in their relative gene regions of 4938 genes involved in small GTPases and were significantly correlated with the PI3K-AKT, MAPK, RAS, and WNT oncogenic pathways. On the other hand, global DNA hypermethylation was preferentially associated with recurrent HGSOC. A total of 2969 DMPs corresponding to 1471 genes were involved in olfactory transduction, and calcium and cAMP signaling. Co-treatment with demethylating agents showed significant growth retardation in ovarian cancer cells through differential inductions, such as cell apoptosis by azacytidine or G2/M cell cycle arrest by decitabine and thioguanine. Notably, azacytidine and decitabine, though not thioguanine, synergistically enhanced cisplatin-mediated cytotoxicity in HGSOC cells. CONCLUSIONS This study demonstrates the significant association of global hypermethylation with poor prognosis and drug resistance in high-grade EOC and highlights the potential of demethylating agents in cancer treatment.
Collapse
Affiliation(s)
- David W Chan
- Department of Obstetrics and Gynaecology, L747 Laboratory Block, LKS Faculty of Medicine, 21 Sassoon Road, Pokfulam, Hong Kong, SAR, People's Republic of China.
| | - Wai-Yip Lam
- Lee's Pharmaceutical (HK) Ltd, 1/F Building 20E, Phase 3, Hong Kong Science Park, Shatin, Hong Kong, People's Republic of China
| | - Fushun Chen
- Department of Obstetrics and Gynaecology, L747 Laboratory Block, LKS Faculty of Medicine, 21 Sassoon Road, Pokfulam, Hong Kong, SAR, People's Republic of China
| | - Mingo M H Yung
- Department of Obstetrics and Gynaecology, L747 Laboratory Block, LKS Faculty of Medicine, 21 Sassoon Road, Pokfulam, Hong Kong, SAR, People's Republic of China
| | - Yau-Sang Chan
- Department of Obstetrics and Gynaecology, L747 Laboratory Block, LKS Faculty of Medicine, 21 Sassoon Road, Pokfulam, Hong Kong, SAR, People's Republic of China
| | - Wai-Sun Chan
- Department of Obstetrics and Gynaecology, L747 Laboratory Block, LKS Faculty of Medicine, 21 Sassoon Road, Pokfulam, Hong Kong, SAR, People's Republic of China
| | - Fangfang He
- Department of Obstetrics and Gynaecology, L747 Laboratory Block, LKS Faculty of Medicine, 21 Sassoon Road, Pokfulam, Hong Kong, SAR, People's Republic of China
| | - Stephanie S Liu
- Department of Obstetrics and Gynaecology, L747 Laboratory Block, LKS Faculty of Medicine, 21 Sassoon Road, Pokfulam, Hong Kong, SAR, People's Republic of China
| | - Karen K L Chan
- Department of Obstetrics and Gynaecology, L747 Laboratory Block, LKS Faculty of Medicine, 21 Sassoon Road, Pokfulam, Hong Kong, SAR, People's Republic of China
| | - Benjamin Li
- Lee's Pharmaceutical (HK) Ltd, 1/F Building 20E, Phase 3, Hong Kong Science Park, Shatin, Hong Kong, People's Republic of China
| | - Hextan Y S Ngan
- Department of Obstetrics and Gynaecology, L747 Laboratory Block, LKS Faculty of Medicine, 21 Sassoon Road, Pokfulam, Hong Kong, SAR, People's Republic of China. .,Department of Obstetrics and Gynaecology, 6/F Professorial Block, Queen Mary Hospital, Pokfulam, Hong Kong, People's Republic of China.
| |
Collapse
|
|
16
|
The Role of Epigenetics in the Progression of Clear Cell Renal Cell Carcinoma and the Basis for Future Epigenetic Treatments. Cancers (Basel) 2021; 13:cancers13092071. [PMID: 33922974 PMCID: PMC8123355 DOI: 10.3390/cancers13092071] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 04/22/2021] [Accepted: 04/23/2021] [Indexed: 12/13/2022] Open
Abstract
Simple Summary The accumulated evidence on the role of epigenetic markers of prognosis in clear cell renal cell carcinoma (ccRCC) is reviewed, as well as state of the art on epigenetic treatments for this malignancy. Several epigenetic markers are likely candidates for clinical use, but still have not passed the test of prospective validation. Development of epigenetic therapies, either alone or in combination with tyrosine-kinase inhibitors of immune-checkpoint inhibitors, are still in their infancy. Abstract Clear cell renal cell carcinoma (ccRCC) is curable when diagnosed at an early stage, but when disease is non-confined it is the urologic cancer with worst prognosis. Antiangiogenic treatment and immune checkpoint inhibition therapy constitute a very promising combined therapy for advanced and metastatic disease. Many exploratory studies have identified epigenetic markers based on DNA methylation, histone modification, and ncRNA expression that epigenetically regulate gene expression in ccRCC. Additionally, epigenetic modifiers genes have been proposed as promising biomarkers for ccRCC. We review and discuss the current understanding of how epigenetic changes determine the main molecular pathways of ccRCC initiation and progression, and also its clinical implications. Despite the extensive research performed, candidate epigenetic biomarkers are not used in clinical practice for several reasons. However, the accumulated body of evidence of developing epigenetically-based biomarkers will likely allow the identification of ccRCC at a higher risk of progression. That will facilitate the establishment of firmer therapeutic decisions in a changing landscape and also monitor active surveillance in the aging population. What is more, a better knowledge of the activities of chromatin modifiers may serve to develop new therapeutic opportunities. Interesting clinical trials on epigenetic treatments for ccRCC associated with well established antiangiogenic treatments and immune checkpoint inhibitors are revisited.
Collapse
|
|
17
|
Reclassification of Kidney Clear Cell Carcinoma Based on Immune Cell Gene-Related DNA CpG Pairs. Biomedicines 2021; 9:biomedicines9020215. [PMID: 33672457 PMCID: PMC7923436 DOI: 10.3390/biomedicines9020215] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Revised: 02/11/2021] [Accepted: 02/15/2021] [Indexed: 12/18/2022] Open
Abstract
Background: A new method was developed based on the relative ranking of gene expression level, overcoming the flaw of the batch effect, and having reliable results in various studies. In the current study, we defined the two methylation sites as a pair. The methylation level in a specific sample was subject to pairwise comparison to calculate a score for each CpGs-pair. The score was defined as a CpGs-pair score. If the first immune-related CpG value was higher than the second one in a specific CpGs-pair, the output score of this immune-related CpGs-pair was 1; otherwise, the output score was 0. This study aimed to construct a new classification of Kidney Clear Cell Carcinoma (KIRC) based on DNA CpGs (methylation sites) pairs. Methods: In this study, the biomarkers of 28 kinds of immune infiltration cells and corresponding methylation sites were acquired. The methylation data were compared between KIRC and normal tissue samples, and differentially methylated sites (DMSs) were obtained. Then, DNA CpGs-pairs were obtained according to the pairs of DMSs. In total, 441 DNA CpGs-pairs were utilized to construct a classification using unsupervised clustering analysis. We also analyzed the potential mechanism and therapy of different subtypes, and validated them in a testing set. Results: The classification of KIRC contained three subgroups. The clinicopathological features were different across three subgroups. The distribution of immune cells, immune checkpoints and immune-related mechanisms were significantly different across the three clusters. The mutation and copy number variation (CNV) were also different. The clinicopathological features and potential mechanism in the testing dataset were consistent with those in the training set. Conclusions: Our findings provide a new accurate and stable classification for developing personalized treatments for the new specific subtypes.
Collapse
|
|
18
|
Li K, Wu Z, Yao J, Fan J, Wei Q. DNA methylation patterns-based subtype distinction and identification of soft tissue sarcoma prognosis. Medicine (Baltimore) 2021; 100:e23787. [PMID: 33592836 PMCID: PMC7870194 DOI: 10.1097/md.0000000000023787] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 11/13/2020] [Indexed: 01/05/2023] Open
Abstract
Soft tissue sarcomas (STSs) are heterogeneous at the clinical with a variable tendency of aggressive behavior. In this study, we constructed a specific DNA methylation-based classification to identify the distinct prognosis-subtypes of STSs based on the DNA methylation spectrum from the TCGA database. Eventually, samples were clustered into 4 subgroups, and their survival curves were distinct from each other. Meanwhile, the samples in each subgroup reflected differentially in several clinical features. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was also conducted on the genes of the corresponding promoter regions of the above-described specific methylation sites, revealing that these genes were mainly concentrated in certain cancer-associated biological functions and pathways. In addition, we calculated the differences among clustered methylation sites and performed the specific methylation sites with LASSO algorithm. The selection operator algorithm was employed to derive a risk signature model, and a prognostic signature based on these methylation sites performed well for risk stratification in STSs patients. At last, a nomogram consisted of clinical features and risk score was developed for the survival prediction. This study declares that DNA methylation-based STSs subtype classification is highly relevant for future development of personalized therapy as it identifies the prediction value of patient prognosis.
Collapse
Affiliation(s)
- Kai Li
- Department of Orthopedics Trauma and Hand Surgery
| | - Zhengyuan Wu
- Department of Orthopedics Trauma and Hand Surgery
| | - Jun Yao
- Department of Bone and Joint Surgery, The First Affiliated Hospital of Guangxi Medical University
- Guangxi Collaborative Innovation Center for Biomedicine, Guangxi Medical University, Nanning, China
| | - Jingyuan Fan
- Department of Orthopedics Trauma and Hand Surgery
| | - Qingjun Wei
- Department of Orthopedics Trauma and Hand Surgery
| |
Collapse
|
|
19
|
Xu B, Peng YJ, Ma BL, Cheng SD. Aberrant methylation of the 16q23.1 tumor suppressor gene ADAMTS18 promotes tumorigenesis and progression of clear cell renal cell carcinoma. Genes Genomics 2021; 43:123-131. [DOI: 10.1007/s13258-021-01036-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 01/02/2021] [Indexed: 12/12/2022]
|
|
20
|
DNA methylation profiling reveals new potential subtype-specific gene markers for early-stage renal cell carcinoma in caucasian population. QUANTITATIVE BIOLOGY 2021. [DOI: 10.15302/j-qb-021-0279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
|
|
21
|
Zhang Y, Zhang R, Liang F, Zhang L, Liang X. Identification of Metabolism-Associated Prostate Cancer Subtypes and Construction of a Prognostic Risk Model. Front Oncol 2020; 10:598801. [PMID: 33324566 PMCID: PMC7726320 DOI: 10.3389/fonc.2020.598801] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 10/26/2020] [Indexed: 12/11/2022] Open
Abstract
Background Despite being the second most common tumor in men worldwide, the tumor metabolism-associated mechanisms of prostate cancer (PCa) remain unclear. Herein, this study aimed to investigate the metabolism-associated characteristics of PCa and to develop a metabolism-associated prognostic risk model for patients with PCa. Methods The activity levels of PCa metabolic pathways were determined using mRNA expression profiling of The Cancer Genome Atlas Prostate Adenocarcinoma cohort via single-sample gene set enrichment analysis (ssGSEA). The analyzed samples were divided into three subtypes based on the partitioning around medication algorithm. Tumor characteristics of the subsets were then investigated using t-distributed stochastic neighbor embedding (t-SNE) analysis, differential analysis, Kaplan–Meier survival analysis, and GSEA. Finally, we developed and validated a metabolism-associated prognostic risk model using weighted gene co-expression network analysis, univariate Cox analysis, least absolute shrinkage and selection operator, and multivariate Cox analysis. Other cohorts (GSE54460, GSE70768, genotype-tissue expression, and International Cancer Genome Consortium) were utilized for external validation. Drug sensibility analysis was performed on Genomics of Drug Sensitivity in Cancer and GSE78220 datasets. In total, 1,039 samples and six cell lines were concluded in our work. Results Three metabolism-associated clusters with significantly different characteristics in disease-free survival (DFS), clinical stage, stemness index, tumor microenvironment including stromal and immune cells, DNA mutation (TP53 and SPOP), copy number variation, and microsatellite instability were identified in PCa. Eighty-four of the metabolism-associated module genes were narrowed to a six-gene signature associated with DFS, CACNG4, SLC2A4, EPHX2, CA14, NUDT7, and ADH5 (p <0.05). A risk model was developed, and external validation revealed the strong robustness our risk model possessed in diagnosis and prognosis as well as the association with the cancer feature of drug sensitivity. Conclusions The identified metabolism-associated subtypes reflected the pathogenesis, essential features, and heterogeneity of PCa tumors. Our metabolism-associated risk model may provide clinicians with predictive values for diagnosis, prognosis, and treatment guidance in patients with PCa.
Collapse
Affiliation(s)
- Yanlong Zhang
- Department of Urology, First Hospital of Shanxi Medical University, Taiyuan, China.,First Clinical Medical College, Shanxi Medical University, Taiyuan, China
| | - Ruiqiao Zhang
- Department of Urology, First Hospital of Shanxi Medical University, Taiyuan, China.,First Clinical Medical College, Shanxi Medical University, Taiyuan, China
| | - Fangzhi Liang
- Department of Urology, First Hospital of Shanxi Medical University, Taiyuan, China.,First Clinical Medical College, Shanxi Medical University, Taiyuan, China
| | - Liyun Zhang
- Department of Rheumatology, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Taiyuan, China
| | - Xuezhi Liang
- Department of Urology, First Hospital of Shanxi Medical University, Taiyuan, China
| |
Collapse
|
|
22
|
Renal Cell Carcinoma: Predicting DNA Methylation Subtyping and Its Consequences on Overall Survival With Computed Tomography Imaging Characteristics. J Comput Assist Tomogr 2020; 44:737-743. [PMID: 32842065 DOI: 10.1097/rct.0000000000001077] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE The aim of the study was to investigate associations between computed tomography (CT) imaging characteristics, DNA methylation subtyping, and overall survival in renal cell carcinomas. METHODS Survival curves were calculated using the Kaplan-Meier analysis. The CT data from 212 patients generated with The Cancer Imaging Archive (TCIA) were reviewed. Identified were 70 (33.0%) M1 subtype, 17 (8.0%) M2 subtype, and 125 (59.0%) M3 subtype. Univariate and multivariate analyses were performed using the logistic regression model. RESULTS Patients with M1 subtype had the shortest median overall survival (P < 0.001). On univariate analysis, long axis of 70 mm, intratumoral calcifications, enhancement, long axis > median, short axis > median, and intratumoral vascularity were associated with a significantly higher incidence of M1 subtype (P < 0.05). Short axis ≤ median, absence of necrosis, absence of intratumoral vascularity, and nodular enhancement were associated with M2 subtype (P < 0.05). Short axis ≤ median, long axis ≤ median, long axis of less than 70 mm, and necrosis were associated with a significantly higher incidence of M3 subtype (P < 0.05). On multivariate logistic regression analysis, long axis of greater than 70 mm (odds ratio [OR] = 2.452, P = 0.004; 95% confidence interval [CI] = 1.332-4.514) and necrosis (OR = 4.758, P = 0.041, 95% CI = 1.065-21.250) were associated with M1 subtype (area under the curve [AUC] = 0. 664). Necrosis (OR = 0.047, P < 0.001, 95% CI = 0.012-0.178) and enhancement (OR = 0.083, P = 0.024, 95% CI = 0.010-0.716) were associated with M2 subtype (AUC = 0.909). Long axis > median (OR = 0.303, P < 0.001, 95% CI = 0.164-0.561) and necrosis (OR = 3.256, P = 0.003, 95% CI = 1.617-10.303) were associated with M3 subtype (AUC = 0. 664). CONCLUSIONS The shortest survival was observed in patients with M1 subtype. This preliminary radiogenomics analysis of renal cell carcinoma demonstrated associations between CT imaging characteristic and DNA methylation subtyping.
Collapse
|
|
23
|
Perez E, Capper D. Invited Review: DNA methylation-based classification of paediatric brain tumours. Neuropathol Appl Neurobiol 2020; 46:28-47. [PMID: 31955441 DOI: 10.1111/nan.12598] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Accepted: 01/13/2020] [Indexed: 12/18/2022]
Abstract
DNA methylation-based machine learning algorithms represent powerful diagnostic tools that are currently emerging for several fields of tumour classification. For various reasons, paediatric brain tumours have been the main driving forces behind this rapid development and brain tumour classification tools are likely further advanced than in any other field of cancer diagnostics. In this review, we will discuss the main characteristics that were important for this rapid advance, namely the high clinical need for improvement of paediatric brain tumour diagnostics, the robustness of methylated DNA and the consequential possibility to generate high-quality molecular data from archival formalin-fixed paraffin-embedded pathology specimens, the implementation of a single array platform by most laboratories allowing data exchange and data pooling to an unprecedented extent, as well as the high suitability of the data format for machine learning. We will further discuss the four most central output qualities of DNA methylation profiling in a diagnostic setting (tumour classification, tumour sub-classification, copy number analysis and guidance for additional molecular testing) individually for the most frequent types of paediatric brain tumours. Lastly, we will discuss DNA methylation profiling as a tool for the detection of new paediatric brain tumour classes and will give an overview of the rapidly growing family of new tumours identified with the aid of this technique.
Collapse
Affiliation(s)
- E Perez
- Department of Neuropathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany.,Berlin Institute of Health, Berlin, Germany
| | - D Capper
- Department of Neuropathology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany.,German Cancer Consortium (DKTK), Partner Site Berlin, German Cancer Research Center (DKFZ), Heidelberg, Germany
| |
Collapse
|