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Comprehensive analysis of biomarkers for prostate cancer based on weighted gene co-expression network analysis.

ABSTRACT: BACKGROUND:Prostate cancer (PCa) is one of the leading causes of cancer-related death. In the present research, we adopted a comprehensive bioinformatics method to identify some biomarkers associated with the tumor progression and prognosis of PCa. METHODS:Differentially expressed genes (DEGs) analysis and weighted gene co-expression network analysis (WGCNA) were applied for exploring gene modules correlative with tumor progression and prognosis of PCa. Clinically Significant Modules were distinguished, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used to Annotation, Visualization and Integrated Discovery (DAVID). Protein-protein interaction (PPI) networks were used in selecting potential hub genes. RNA-Seq data and clinical materials of prostate cancer from The Cancer Genome Atlas (TCGA) database were used for the identification and validation of hub genes. The significance of these genes was confirmed via survival analysis and immunohistochemistry. RESULTS:2688 DEGs were filtered. Weighted gene co-expression network was constructed, and DEGs were divided into 6 modules. Two modules were selected as hub modules which were highly associated with the tumor grades. Functional enrichment analysis was performed on genes in hub modules. Thirteen hub genes in these hub modules were identified through PPT networks. Based on TCGA data, 4 of them (CCNB1, TTK, CNN1, and ACTG2) were correlated with prognosis. The protein levels of CCNB1, TTK, and ACTG2 had a degree of differences between tumor tissues and normal tissues. CONCLUSION:Four hub genes were identified as candidate biomarkers and potential therapeutic targets for further studies of exploring molecular mechanisms and individual therapy on PCa.

SUBMITTER: Chen X

PROVIDER: S-EPMC7440253 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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Comprehensive analysis of biomarkers for prostate cancer based on weighted gene co-expression network analysis.

Chen Xuan X Wang Jingyao J Peng Xiqi X Liu Kaihao K Zhang Chunduo C Zeng Xingzhen X Lai Yongqing Y

Medicine 20200401 14

<h4>Background</h4>Prostate cancer (PCa) is one of the leading causes of cancer-related death. In the present research, we adopted a comprehensive bioinformatics method to identify some biomarkers associated with the tumor progression and prognosis of PCa.<h4>Methods</h4>Differentially expressed genes (DEGs) analysis and weighted gene co-expression network analysis (WGCNA) were applied for exploring gene modules correlative with tumor progression and prognosis of PCa. Clinically Significant Modu ...[more]

PMID: 32243390

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Project description:BackgroundDiabetic nephropathy (DN) is the major complication of diabetes mellitus, and leading cause of end-stage renal disease. The underlying molecular mechanism of DN is not yet completely clear. The aim of this study was to analyze a DN microarray dataset using weighted gene co-expression network analysis (WGCNA) algorithm for better understanding of DN pathogenesis and exploring key genes in the disease progression.MethodsThe identified differentially expressed genes (DEGs) in DN dataset GSE47183 were introduced to WGCNA algorithm to construct co-expression modules. STRING database was used for construction of Protein-protein interaction (PPI) networks of the genes in all modules and the hub genes were identified considering both the degree centrality in the PPI networks and the ranked lists of weighted networks. Gene ontology and Reactome pathway enrichment analyses were performed on each module to understand their involvement in the biological processes and pathways. Following validation of the hub genes in another DN dataset (GSE96804), their up-stream regulators, including microRNAs and transcription factors were predicted and a regulatory network comprising of all these molecules was constructed.ResultsAfter normalization and analysis of the dataset, 2475 significant DEGs were identified and clustered into six different co-expression modules by WGCNA algorithm. Then, DEGs of each module were subjected to functional enrichment analyses and PPI network constructions. Metabolic processes, cell cycle control, and apoptosis were among the top enriched terms. In the next step, 23 hub genes were identified among the modules in genes and five of them, including FN1, SLC2A2, FABP1, EHHADH and PIPOX were validated in another DN dataset. In the regulatory network, FN1 was the most affected hub gene and mir-27a and REAL were recognized as two main upstream-regulators of the hub genes.ConclusionsThe identified hub genes from the hearts of co-expression modules could widen our understanding of the DN development and might be of targets of future investigations, exploring their therapeutic potentials for treatment of this complicated disease.

Dataset Information

Comprehensive analysis of biomarkers for prostate cancer based on weighted gene co-expression network analysis.

Publications

Comprehensive analysis of biomarkers for prostate cancer based on weighted gene co-expression network analysis.

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