Identification And Analysis Of Module Biomarkers For Prostate Cancer

Prostate cancer is one of the most common complex diseases with high leading causeof death in men. Identifications of prostate cancer associated genes and biomarkers are thusessential as they can gain insights into the mechanisms underlying disease progression andadvancing for early diagnosis and developing effective therapies.In this study, we presented an integrative analysis of gene expression profiling andprotein interaction network at a systematic level to reveal candidate disease-associatedgenes and biomarkers for prostate cancer progression. At first, we reconstructed the humanprostate cancer protein-protein interaction network (HPC-PPIN) and the network was thenintegrated with the prostate cancer gene expression data to identify modules related todifferent phases in prostate cancer. At last, the candidate module biomarkers were validatedby its predictive ability of prostate cancer progression. Different phases-specific moduleswere identified for prostate cancer. Among these modules, transcription AndrogenReceptor (AR) nuclear signaling and Epidermal Growth Factor Receptor (EGFR) signalingpathway were shown to be the pathway targets for prostate cancer progression. Theidentified candidate disease-associated genes showed better predictive ability of prostatecancer progression than those of published biomarkers. In context of functional enrichmentanalysis, interestingly candidate disease-associated genes were enriched in the nucleus anddifferent functions were encoded for potential transcription factors, for examples keyplayers as AR, Myc, ESR1and hidden player as Sp1which was considered as a potentialnovel biomarker for prostate cancer.Meanwhile, with the advent of high-throughput sequencing technologies whichgenerate explosive data, we can’t obtain a consistency result in the reason of complexcancer and the heterogeneity of different datasets. However, an integrative―Omics‖dataanalysis (IOA) tool is proposed by us for biological interpretation of multiple―Omics‖data at a system level, such as gene expression data, microRNA expression data and ChIP-Seqdata. IOA is a friendly graphical user interface based on Java platform and supports eachintegrated function.