Identification of prognostic markers of high grade prostate cancer through an integrated bioinformatics approach

Prostate cancer is one of the leading causes of cancer death for male. In the present study, we applied an integrated bioinformatics approach to provide a novel perspective and identified some hub genes of prostate cancer. Microarray data of fifty-nine prostate cancer were downloaded from Gene Expression Omnibus. Gene Ontology and pathway analysis were applied for differentially expressed genes between high and low grade prostate cancer. Weighted gene coexpression network analysis was applied to construct gene network and classify genes into different modules. The most related module to high grade prostate cancer was identified and hub genes in the module were revealed. Ingenuity pathway analysis was applied to check the chosen module's relationship to high grade prostate cancer. Hub gene's expression profile was verified with clinical samples and a dataset from The Cancer Genome Atlas project. 3193 differentially expressed genes were filtered and gene ontology and pathway analysis revealed some cancer- and sex hormone-related results. Weighted gene coexpression network was constructed and genes were classified into six modules. The red module was selected and ingenuity pathway analysis confirmed its relationship with high grade prostate cancer. Hub genes were identified and their expression profile was also confirmed. The present study applied integrate bioinformatics approaches to generate a holistic view of high grade prostate cancer and identified hub genes could serve as prognosis markers and potential treatment targets.