Comprehensive bioinformatics analysis confirms RBMS3 as the central candidate biological target for ovarian cancer. (December 2022)

• Record Type: Journal Article
• Authors: Wang, Mei; Fu, Xiangjun; Wang, Wei; Zhang, Yuan; Jiang, Zhenyi; Gu, Yan; Chu, Menglong; Shao, Yanting; Li, Shuqin
• Is Part Of: Medical engineering & physics. Volume 110(2022)
• Publication Date: 2022-12
• Subjects: Ovarian cancer -- Bioinformatics analysis -- Molecular biology technology -- RBMS3; Biomedical engineering -- Periodicals; Biomedical Engineering -- Periodicals; Physics -- Periodicals; Génie biomédical -- Périodiques; Biomedical engineering; Electronic journals; Periodicals; 610.28
• DOI: 10.1016/j.medengphy.2022.103883
• Languages: English
• ISSNs: 1350-4533

Abstract

Highlights: Screening of ovarian cancer (OC) targets using bioinformatics methods. OC target screening using GEO and TCGA databases. MEIS2, TSTA3, CNTN1, RBMS3, and TRA2A are differentially expressed in OC. To explore the molecular biological mechanism of RBMS3 in OC. Detection of the effect of RBMS3 on OC cell function. Abstract: Ovarian cancer (OC) is one of the most lethal malignancies in the female reproductive system. To find genes related to cancer progression targeting specific biological factors for targeted therapy, bioinformatics technology has been widely used. To screen the prognostic gene markers of OC by bioinformatics and explore their potential molecular biological mechanisms. Two data sets related to OC, GSE54388, and GSE119056, were rooted in the open comprehensive gene expression database (GEO). To correct the background of the data, standardize and screen differentially expressed genes (DEGs) using the R software limma package. The selected DEGs were enriched by Gene Ontology (GO) and through DAVID online database. Kyoto Encyclopedia of Genes and Genomes (KEGG) signal pathway analysis and protein-protein interaction network (PPI-network) map were constructed by STRING online database and Cytoscape software. Combined with the TCGA database, univariate and multivariate COX regression were used to screen prognostic genes. QRT-PCR was used to verify DEGs in clinical tissue samples. Eventually, the function of RBMS3 on the viability, migration, invasion, and apoptosis of OC cells was tested through functional experiments in vitro. 352 common DEGs were screened from GSE54388 and GSE119056 data sets. Survival analysis showed that MEIS2, TSTA3, CNTN1, RBMS3, and TRA2A were considered to be connected with the prognosis of OC. We discover that the expression level of RBMS3 was positively connected with the overall survival (OS) rate of sufferers with OC. The level of RBMS3 in OC tissues was markedly lower than that in neighboring structures and the outcomes of the GEPIA database were consistent with those of the qRT-PCR experiment. Through gene transfection technology it was found that overexpression of RBMS3 in OC cells substantially suppressed the vitality, migration, and invasion of OC cells and raised the rates of apoptosis in the OC cells. In this experiment, we distinguish 5 genes that may participate in the prognosis of OC and showed the key genes and pathways related to OC. It is speculated that RBMS3, a tumor suppressor gene, can be applied as a potential biological marker for the treatment of OC, gene expression summary, and prognosis.