Identification of Oxidative Stress-Associated Molecular Subtypes and Signature for Predicting Survival Outcome of Cervical Squamous Cell Carcinoma

BACKGROUND: Cervical squamous cell carcinoma (CESC) is the gynecologic malignancy with high incidence rate and high mortality rate. Oxidative stress participates in gene regulation and malignant tumor progression, including CESC. METHODS: RNA-seq, clinical information, and genomic mutation were from The Cancer Genome Atlas- (TCGA-) CESC and GSE44001 datasets. Oxidative stress-related genes were obtained from the gene set enrichment analysis (GSEA) website. ConsensusClusterPlus was used for clustering, which was assessed by the Kaplan-Meier (KM) survival curve analysis, mutation analysis, immunocharacteristic analysis, and therapy. Prognostic signatures were built by combining weighted correlation network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO) algorithm, and stepAIC. The prognostic power of this model was evaluated using the KM survival curve analysis, receiver operating characteristic (ROC) curve analysis, nomogram, and decision curve analysis (DCA). RESULTS: 218 of the 291 CESC cases (74.91%) presented oxidative stress-related gene mutation, especially FBXW7. Three clusters were determined based on oxidative stress-related genes, among which cluster 3 (C3) presented low-frequency mutation and hyperimmune state and was sensitive to immunotherapy. This research developed a 5-gene oxidative stress-related prognostic signature and a RiskScore model. As shown by ROC analysis, in the TCGA and GSE44001 datasets, the RiskScore model showed a high prediction accuracy for 1-, 3-, and 5-year CESC overall survival. High RiskScore was associated with enhanced immune status. The nomogram model was greatly predictive of the overall survival of CESC patients. CONCLUSION: Our prognostic model was based on oxidative stress-related genes in CESC, potentially aids in CESC prognosis, and provides potential targets against CESC.