Text Mining Identifies Key Pattern Analysis and Process in Prostate Cancer

Abstract

One of the most typical malignancies in males is prostate cancer, and its global burden is increasing. Using text-mining technology, this study seeks to pinpoint important genes and biochemical processes related to prostate cancer. Using certain terms related to gene expression, PUBMED abstracts of interest were found. The extracted abstracts included gene pairings and functional connections. On the genes identified from the function interactions, biological procedures enrichment, network analysis, and gene prioritizing utilizing edge centrality of betweenness were carried out. For the modules containing at least five genes, which were retrieved from the network analysis, gene clustering and pathway enrichment analyses were built. The biological functions of the newly identified genes showed that they were involved in positive transcriptional regulation from the RNA polymerase II promoter, positive regulation of cell proliferation, and drug responsiveness. The prostate cancer enrichment analysis processes revealed that the NF signalling pathway, PI3k-Art signalling pathway, thyroid hormone signalling, and ErbB signalling pathways were enriched. According to the network analysis results, which were further sorted by their values for degree of between-ness, it was discovered that AKT1, AR, and KDM3A were the important genes. In conclusion, by concentrating on the discovered hub genes, prostate cancer can be medically treated.