Identification of Potential Key Genes in Prostate Cancer with Gene Expression, Pivotal Pathways and Regulatory Networks Analysis Using Integrated Bioinformatics Methods

Abstract

Prostate cancer (PCa) is the most prevalent cancer (20%) in males and is accountable for a fifth (6.8%) cancer-related deaths in males globally. Smoking, obesity, race/ethnicity, diet, age, chemicals and radiation exposure, sexually transmitted diseases, etc. are among the most common risk factors for PCa. However, the basic change at the molecular level is the manifested confirmation of PCa. Thus, this study aims to evaluate the molecular signature for PCa in comparison to benign prostatic hyperplasia (BPH). Additionally, representation of differentially expressed genes (DEGs) are conducted with the help of some bioinformatics tools like DAVID, STRING, GEPIA, Cytoscape. The gene expression profile for the four data sets GSE55945, GSE104749, GSE46602, and GSE32571 was downloaded from NCBI, Gene Expression Omnibus (GEO). For the extracted DEGs, different types of analysis including functional and pathway enrichment analysis, protein–protein interaction (PPI) network construction, survival analysis and transcription factor (TF) prediction were conducted. We obtained 633 most significant upregulated genes and 1219 downregulated genes, and a sum total of 1852 DEGs were found from all four datasets after assessment. The key genes, including EGFR, MYC, VEGFA, and PTEN, are targeted by TF such as AR, Sp1, TP53, NF-KB1, STAT3, RELA. Moreover, miR-21-5p also found significantly associated with all the four key genes. Further, The Cancer Genome Atlas data (TCGA) independent database was used for validation of key genes EGFR, MYC, VEGFA, PTEN expression in prostate adenocarcinoma. All four key genes were found to be significantly correlated with overall survival in PCa. Therefore, the therapeutic target may be determined by the information of these key gene’s findings for the diagnosis, prognosis and treatment of PCa.

Integrated grade-wise profiling analysis reveals potential plasma miR-373-3p as prognostic indicator in Prostate Cancer & its target KPNA2

“…To learn how specific miRNAs play a role in disease development, we need to find the linked candidate genes. Because of the strong association between these two molecular regulators and numerous diseases, including cancer, many investigations have concentrated on the interplay between miRNA regulation and mRNA expression [ 21 ]. Information about the genes that may be targeted by miRNAs was gathered using the bioinformatics tool.…”

Microarray and bioinformatic analysis of conventional ameloblastoma: an observational analysis

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Role of ribosomal pathways and comorbidity in COVID-19: Insight from SARS-CoV-2 proteins and host proteins interaction network analysis

“…These proteins are pivotal in facilitating SARS-CoV-2 replication within the human body, leading to cell infection, illness induction, and the onset of diverse manifestations throughout the body [ [12] , [13] , [14] , [15] ]. The systematic network theoretical approach to studying host-pathogen protein interactions offers valuable insights, contributing to our understanding and illumination of the mechanisms that underlie a wide range of infectious diseases [ [16] , [17] , [18] ].…”