Identification of a 10-pseudogenes signature as a novel prognosis biomarker for ovarian cancer

Abstract

The outcomes of ovarian cancer are complicated and usually unfavorable due to their diagnoses at a late stage.Identifying the efficient prognostic biomarkers to improve the survival of ovarian cancer is urgently warranted. The survival-related pseudogenes retrieved from the Cancer Genome Atlas database were screened by univariate Cox regression analysis and further assessed by least absolute shrinkage and selection operator (LASSO) method. A risk score model based on the prognostic pseudogenes was also constructed. The pseudogene-mRNA regulatory networks were established using correlation analysis, and their potent roles in the ovarian cancer progression were uncovered by functional enrichment analysis. Lastly, ssGSEA and ESTIMATE algorithms was used to evaluate the levels of immune cell infiltrations in cancer tissues and explore their relationship with risk signature. A prediction model of 10pseudogenes including RPL10P6, AC026688.1, FAR2P4, AL391840.2, AC068647.2, FAM35BP, GBP1P1, ARL4AP5, RPS3AP2, and AMD1P1 was established. The 10-pseudogenes signature was demonstrated to be an independent prognostic factor in patient with ovarian cancer in the random set (hazard ratio [HR] = 2.512, 95% confidence interval[CI] = 2.03-3.11, P < 0.001) and total set (HR = 1.71, 95% CI = 1.472-1.988, P < 0.001). When models integrating with age, grade, stage, and risk signature, the Area Under Curve (AUC) of the 1-year, 3-year, 5-year and 10-year Receiver Operating Characteristic curve in the random set and total set were 0.854, 0.824, 0.855, 0.805 and 0.679, 0.697, 0.739, 0.790, respectively. The results of functional enrichment analysis indicated that the underlying mechanisms by which these pseudogenes influence cancer prognosis may involve the immune-related biological processes and signaling pathways. Correlation analysis showed that risk signature was significantly correlated with immune cell infiltration and immune score. We identified a novel 10-pseudogenes signature to predict the survival of patients with ovarian cancer, and that may serve as novel possible prognostic biomarkers and therapeutic targets for ovarian cancer.