An integrative analysis of GEO data to identify possible therapeutic biomarkers of prostate cancer and targeting potential protein through Zea mays phytochemicals by virtual screening approaches

AbstractProstate cancer (PC) is a prevalent type of cancer among men. Delaying the treatment of patients with upgraded or upstaged cancer may lead to unmanageable circumstances. The aim of this study is to contribute to the finding of biomarkers that are specific to PC and identify drug candidates derived from plants. The information about cancer is critical for clinicians to make decisions about patient treatment in the era of precision medicine. Advances in genomics technology have opened up new possibilities for identifying genes that are associated with cancer, including PC. This study identifies novel differentially expressed genes for PC. The seven PC microarray datasets were selected from the National Center for Biotechnology Information (NCBI)/Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) were found based on a fold change of |logFC| ≥ 1 and an adjusted p-value of <0.05. The DEGs were further studied using several bioinformatics tools, including STRING, CytoHubba, SRplot, Coremine Medical database, FunRich and GeneMANIA, cBioPortal. The six new potential biomarkers, GAGE2A, GAGE12G, GAGE2E, GAGE13, GAGE12F and CSAG1 were identified. These biomarkers are associated with biological processes (BPs) such as cell division, and gene expression regulation, so these genes may have a crucial role in PC progression and may serve as potential biomarkers for PC. A total of 497 phytochemicals from corn plants have been screened against the target protein and found LTS0176591 as the best lead molecule with docking score of −6.31 kcal/mol. Further, molecular mechanics–generalized born surface area (MM-GBSA), molecular dynamics simulation, principal component analysis (PCA), free energy landscape (FEL) and molecular mechanics–Poisson–Boltzmann surface area (MM-PBSA) were carried out to validate the findings.Communicated by Ramaswamy H. SarmaKeywords: Prostate cancerProtein–protein interactionbiomarkersfunctional enrichment analysis AcknowledgmentThe research work was carried out in the laboratory, Department of Applied Sciences, Indian Institute of Information Technology Allahabad, Prayagraj-India. MDS was carried out on CCF facility of IIIT Allahabad. Shristi Modanwal is thankful to the Ministry of Education, Govt. of India, for the fellowship to pursue a doctoral degree.Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThe author(s) reported there is no funding associated with the work featured in this article.