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Identification of DNA methylation patterns and biomarkers for clear-cell renal cell carcinoma by multi-omics data analysis

BACKGROUND: Tumorigenesis is highly heterogeneous, and using clinicopathological signatures only is not enough to effectively distinguish clear cell renal cell carcinoma (ccRCC) and improve risk stratification of patients. DNA methylation (DNAm) with the stability and reversibility often occurs in t...

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Autores principales: Liu, Pengfei, Tian, Weidong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7409785/
https://www.ncbi.nlm.nih.gov/pubmed/32832275
http://dx.doi.org/10.7717/peerj.9654
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author Liu, Pengfei
Tian, Weidong
author_facet Liu, Pengfei
Tian, Weidong
author_sort Liu, Pengfei
collection PubMed
description BACKGROUND: Tumorigenesis is highly heterogeneous, and using clinicopathological signatures only is not enough to effectively distinguish clear cell renal cell carcinoma (ccRCC) and improve risk stratification of patients. DNA methylation (DNAm) with the stability and reversibility often occurs in the early stage of tumorigenesis. Disorders of transcription and metabolism are also an important molecular mechanisms of tumorigenesis. Therefore, it is necessary to identify effective biomarkers involved in tumorigenesis through multi-omics analysis, and these biomarkers also provide new potential therapeutic targets. METHOD: The discovery stage involved 160 pairs of ccRCC and matched normal tissues for investigation of DNAm and biomarkers as well as 318 cases of ccRCC including clinical signatures. Correlation analysis of epigenetic, transcriptomic and metabolomic data revealed the connection and discordance among multi-omics and the deregulated functional modules. Diagnostic or prognostic biomarkers were obtained by the correlation analysis, the Least Absolute Shrinkage and Selection Operator (LASSO) and the LASSO-Cox methods. Two classifiers were established based on random forest (RF) and LASSO-Cox algorithms in training datasets. Seven independent datasets were used to evaluate robustness and universality. The molecular biological function of biomarkers were investigated using DAVID and GeneMANIA. RESULTS: Based on multi-omics analysis, the epigenetic measurements uniquely identified DNAm dysregulation of cellular mechanisms resulting in transcriptomic alterations, including cell proliferation, immune response and inflammation. Combination of the gene co-expression network and metabolic network identified 134 CpG sites (CpGs) as potential biomarkers. Based on the LASSO and RF algorithms, five CpGs were obtained to build a diagnostic classifierwith better classification performance (AUC > 99%). A eight-CpG-based prognostic classifier was obtained to improve risk stratification (hazard ratio (HR) > 4; log-rank test, p-value < 0.01). Based on independent datasets and seven additional cancers, the diagnostic and prognostic classifiers also had better robustness and stability. The molecular biological function of genes with abnormal methylation were significantly associated with glycolysis/gluconeogenesis and signal transduction. CONCLUSION: The present study provides a comprehensive analysis of ccRCC using multi-omics data. These findings indicated that multi-omics analysis could identify some novel epigenetic factors, which were the most important causes of advanced cancer and poor clinical prognosis. Diagnostic and prognostic biomarkers were identified, which provided a promising avenue to develop effective therapies for ccRCC.
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spelling pubmed-74097852020-08-21 Identification of DNA methylation patterns and biomarkers for clear-cell renal cell carcinoma by multi-omics data analysis Liu, Pengfei Tian, Weidong PeerJ Bioinformatics BACKGROUND: Tumorigenesis is highly heterogeneous, and using clinicopathological signatures only is not enough to effectively distinguish clear cell renal cell carcinoma (ccRCC) and improve risk stratification of patients. DNA methylation (DNAm) with the stability and reversibility often occurs in the early stage of tumorigenesis. Disorders of transcription and metabolism are also an important molecular mechanisms of tumorigenesis. Therefore, it is necessary to identify effective biomarkers involved in tumorigenesis through multi-omics analysis, and these biomarkers also provide new potential therapeutic targets. METHOD: The discovery stage involved 160 pairs of ccRCC and matched normal tissues for investigation of DNAm and biomarkers as well as 318 cases of ccRCC including clinical signatures. Correlation analysis of epigenetic, transcriptomic and metabolomic data revealed the connection and discordance among multi-omics and the deregulated functional modules. Diagnostic or prognostic biomarkers were obtained by the correlation analysis, the Least Absolute Shrinkage and Selection Operator (LASSO) and the LASSO-Cox methods. Two classifiers were established based on random forest (RF) and LASSO-Cox algorithms in training datasets. Seven independent datasets were used to evaluate robustness and universality. The molecular biological function of biomarkers were investigated using DAVID and GeneMANIA. RESULTS: Based on multi-omics analysis, the epigenetic measurements uniquely identified DNAm dysregulation of cellular mechanisms resulting in transcriptomic alterations, including cell proliferation, immune response and inflammation. Combination of the gene co-expression network and metabolic network identified 134 CpG sites (CpGs) as potential biomarkers. Based on the LASSO and RF algorithms, five CpGs were obtained to build a diagnostic classifierwith better classification performance (AUC > 99%). A eight-CpG-based prognostic classifier was obtained to improve risk stratification (hazard ratio (HR) > 4; log-rank test, p-value < 0.01). Based on independent datasets and seven additional cancers, the diagnostic and prognostic classifiers also had better robustness and stability. The molecular biological function of genes with abnormal methylation were significantly associated with glycolysis/gluconeogenesis and signal transduction. CONCLUSION: The present study provides a comprehensive analysis of ccRCC using multi-omics data. These findings indicated that multi-omics analysis could identify some novel epigenetic factors, which were the most important causes of advanced cancer and poor clinical prognosis. Diagnostic and prognostic biomarkers were identified, which provided a promising avenue to develop effective therapies for ccRCC. PeerJ Inc. 2020-08-03 /pmc/articles/PMC7409785/ /pubmed/32832275 http://dx.doi.org/10.7717/peerj.9654 Text en © 2020 Liu and Tian https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Bioinformatics
Liu, Pengfei
Tian, Weidong
Identification of DNA methylation patterns and biomarkers for clear-cell renal cell carcinoma by multi-omics data analysis
title Identification of DNA methylation patterns and biomarkers for clear-cell renal cell carcinoma by multi-omics data analysis
title_full Identification of DNA methylation patterns and biomarkers for clear-cell renal cell carcinoma by multi-omics data analysis
title_fullStr Identification of DNA methylation patterns and biomarkers for clear-cell renal cell carcinoma by multi-omics data analysis
title_full_unstemmed Identification of DNA methylation patterns and biomarkers for clear-cell renal cell carcinoma by multi-omics data analysis
title_short Identification of DNA methylation patterns and biomarkers for clear-cell renal cell carcinoma by multi-omics data analysis
title_sort identification of dna methylation patterns and biomarkers for clear-cell renal cell carcinoma by multi-omics data analysis
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7409785/
https://www.ncbi.nlm.nih.gov/pubmed/32832275
http://dx.doi.org/10.7717/peerj.9654
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