Identification of Four Hub Genes as Promising Biomarkers for the Evaluation of Ovarian Cancer Prognosis

Abstract

Ovarian malignant growth is perhaps the most lethal disease in females. There are no exact biomarkers for the early determination of ovarian disease. We obtained a total of 12 gene clusters through WGCNA and studied the azure gene modules related to the lymphatic infiltration of ovarian cancer further. What’s more, endurance investigation was utilized to decide three qualities connected with the by and large and infection-free endurance in ovarian disease patients, including GOGA8B [Hazard Ratio (HR)=1.53, p=0.037, 95% CI: 1.05–2.24], LRRC26 (HR=0.7, p =0.045, 95% CI: 0.48–1.01), and CCDC114 (HR = 0.72, p = 0.042, 95% CI: 0.53–0.98). A prognostic risk score model was built to anticipate the endurance pace of patients at 1, 3, and 5 years, individually. The area under the receiver operating characteristic (ROC) curve (AUC) of the training set was 0.749, 0.764, and 0.784, respectively; the test AUC was 0.601, 0.623, and 0.709. Our review gives a point of view on significant possible biomarkers for the determination, anticipation, and therapy of ovarian malignant growth.