Prognostic value of potential biomarkers in prostate cancer via bioinformatic analysis
Journal Title: TMR Integrative Medicine - Year 2021, Vol 5, Issue 15
Abstract
Background: The Genotype-Tissue Expression was used to expanded normal tissue of the Cancer Genome Atlas database. This study aimed to investigate genes associated with the pathogenesis and prognosis of prostate cancer. Methods: We conducted prognostic related genes for prostate cancer by using transcriptome data from the Genotype-Tissue Expression Project and the Cancer Genome Atlas data sources, which were analyzed using an integrated bioinformatics strategy. Clinically significant modules were distinguished, and GO and KEGG analysis were used to Database for Annotation, Visualization and Integrated Discovery. Further annotation was performed through Gene set enrichment analysis. Logistic regression was carried out to analyze the associations between clinicopathologic characteristics and the hub genes. Logistic regression model and survival analysis were performed. Results: By using data available from the Cancer Genome Atlas and the Genotype-Tissue Expression databases, we here show that 53 differential expression genes were identified. Through GO and KEGG analysis a prognostic related gene signature consisted of GOLM1, EIF4A1, ABCC4, RPL7P16, NPIPB12 and PCA3 was constructed with a good performance in predicting overall survivals. The majority of the six hub genes were associated with clinical characteristics of prostate cancer. Conclusion: These genes might be considered as new targets for further investigating the diagnostic and prognostic biomarkers to facilitate the molecular targeting therapy since they showed differently expressed in prostate cancer and correlate with overall survival prognosis.
Authors and Affiliations
Shan-Qi Guo, Hao Deng, Ying-Jie Jia, Xiao-Jiang Li, Xing-Kang Jiang
Keywords
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- EP ID EP697534
- DOI 10.12032/TMRIM202105015
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