Identification of ARHGEF38, NETO2, GOLM1, and SAPCD2 Associated With Prostate Cancer Progression by Bioinformatic Analysis and Experimental Validation

Abstract

Prostate cancer (PCa) represents one of the most prevalent types of cancers and is a large health burden for men. The pathogenic mechanisms of PCa still need further investigation. The aim of this study was to construct an effective signature to predict the prognosis of PCa patients and identify the biofunctions of signature-related genes. First, we screened differentially expressed genes (DEGs) between PCa and normal control tissues in The Cancer Genome Atlas (TCGA) and GSE46602 datasets, and we performed weighted gene co-expression network analysis (WGCNA) to determine gene modules correlated with tumors. In total, 124 differentially co-expressed genes were retained. Additionally, five genes (ARHGEF38, NETO2, PRSS21, GOLM1, and SAPCD2) were identified to develop the prognostic signature based on TCGA dataset. The five-gene risk score was verified as an independent prognostic indicator through multivariate Cox regression analyses. The expression of the five genes involved in the signature was detected in the Gene Expression Omnibus (GEO), Gene Expression Profiling Interactive Analysis (GEPIA), and Oncomine databases. In addition, we utilized DiseaseMeth 2.0 and MEXPRESS for further analysis and found that abnormal methylation patterns may be a potential mechanism for these five DEGs in PCa. Finally, we observed that these genes, except PRSS21, were highly expressed in tumor samples and PCa cells. Functional experiments revealed that silencing ARHGEF38, NETO2, GOLM1, and SAPCD2 suppressed the proliferation, migration, and invasiveness of PCa cells. In summary, this prognostic signature had significant clinical significance for treatment planning and prognostic evaluation of patients with PCa. Thus, ARHGEF38, NETO2, GOLM1, and SAPCD2 may serve as oncogenes in PCa.