Combining glycolysis and oxidative stress characterizations to assess prognosis in ovarian cancer

Abstract

Abstract Background The research assessed glycolysis and oxidative stress scores as a potential indicator of prognosis in those afflicted with progressive ovarian cancer, which is known to have a poorer prognosis and is prone to platinum resistance. Methods The patients from the Ovarian Cancer (OV) dataset (TCGA-OV)is a test set. A total of 3 independent cohorts, GSE23554, GSE30587, and GSE14407 were utilized for outside verification. To discover glycolysis and oxidative stress-associated molecular patterns, unsupervised hierarchical clustering based on glycolysis and oxidative stress-associated genes was performed. Significant prognostic glycolysis and oxidative stress-associated genes were identified by LASSO(least absolute shrinkage and selection operator) regression analysis, as well as univariate and multivariate Cox regression. To differentiate between high-risk and low-risk categories, gene mutations, tumor immune microenvironments, and functional pathways were examined. Immunohistochemistry assays were utilized in this investigation to confirm the link between GLO1 and ovarian cancer prognosis. Results With 82 genes defined as gl ycolysis and oxidative stress- related genes ( GOSRGs ), the five GOSRGs (AKT1, ERBB2, GLO1, H6PD, and RB1) were identified to bulid a glycolysis and oxidative stress prognostic risk model. An analysis of the risk score via ROC curve revealed that the AUCs for 1, 3, and 5-years were 0.638, 0.588, and 0.635 respectively. The key genes are: GLO1, H6PD, and RB1. Glycolysis and oxidative stress-related pathways were discovered using GO, KEGG, and GSEA function analysis. The immune infiltration analysis revealed a statistically significant difference in 19 types of immune cells between the GOSs high and low groups. In addition, 15 genes were more prevalent in the GOSs high group. In univariate Cox regression analysis, GOSs, stage, and age are significantly related to prognosis. In nomogram analysis, the prognostic ability of age and stage on the model is higher than that of other variables. Conclusions The glycolysis-oxidative stress gene signature represents a promising tool for risk classification tool in OV patients.