Prostate Cancer Related Gene Analysis Based on Literature Mining

This paper aims to identify prostate cancer associated genes and to understand their interactions based on literature mining techniques. Nine hundred and sixty-two prostate cancer associated genes were extracted with the disease-gene association (DGA) classifier, and then the protein-protein interactions (PPIs) in cancer associated genes were extracted to construct a PPI network. It is found that AR, PSA, TP53, VEGFA, IL6, BCL2, P38, MMP2, EGF and PTEN are ten hub proteins and play key roles in the network. The KEGG pathway analysis shows that 22 enriched pathways may participate in prostate carcinogenesis. Some of them involved in the immune response might also be associated with prostate cancer, which suggests a relationship between the host immune system and cancers. The PPI network and pathway analysis implies the complexity of the occurrence, progression and prognosis of prostate cancer.