Identification and validation of a novel defined stress granule-related gene signature for predicting the prognosis of ovarian cancer via bioinformatics analysis

Abstract Background Ovarian cancer (OC) is a malignant gynecological cancer with an extremely poor prognosis. Stress granules (SGs), nonmembrane organelles, are responses to stress stimuli. However, the correlations between SG-related genes and prognosis in OC remain unclear. Methods In this study, RNA-seq data and clinical information from GSE18520 and GSE14407 in the Gene Expression Omnibus (GEO) and ovarian plasmacytoma adenocarcinoma in The Cancer Genome Atlas (TCGA) were downloaded. SG-related genes were obtained from GeneCards, MSigDB, and the literature. First, thirteen SG-related genes were identified in the prognostic model by using least absolute shrinkage and selection operator (LASSO) Cox regression. The prognostic value of each SG-related gene for survival and its relationship with clinical characteristics were evaluated. Next, we performed functional enrichment analysis of SG-related genes. Then, the protein-protein interactions (PPIs) of SG-related genes were visualized by Cytoscape with STRING. Results According to the median risk score from the LASSO Cox regression, a thirteen-gene signature was created and classified all OC patients in the TCGA cohort and GEO into high- and low-risk groups. A total of five SG-related genes were differentially expressed between the high-risk and low-risk groups of OC in GEO. A total of thirteen SG-related genes were related to several important oncogenic pathways (TNF-α signaling, PI3K-AKT-mTOR signaling, and WNT-βcatenin signaling) and several cellular components (cytoplasmic stress granule, cytoplasmic ribonucleoprotein granule, and ribonucleoprotein granule). The PPI network identified eleven hub genes that obtained the highest interaction between ELAVL1 and other genes. Conclusion Taken together, SG-related genes (DNAJA1, ELAVL1, FBL, GRB7, MOV10, PABPC3, PCBP2, PFN1, RFC4, SYNCRIP, USP10, ZFP36, ZFP36L1) can be used to predict the prognosis of OC.