A comprehensive study of oxidative stress-related effects on the prognosis and drug therapy of cervical cancer.

BACKGROUND: Cervical cancer (CC) is a serious global disease with poor prognoses and a significant recurrence rate in patients with advanced disease. Oxidative stress (OS) greatly influences many types of human cancers, making it crucial to understand the functional mechanisms of OS-related genes in CC. METHODS: The transcriptome and clinical data of three normal samples and 306 patients with CC were obtained from The Cancer Genome Atlas dataset. The GSE44001 dataset was acquired from the Gene Expression Omnibus database. OS-related subtypes in the cohort with CC were identified using unsupervised hierarchical clustering, univariate Cox analysis, gene set enrichment analysis (GSEA), and least absolute shrinkage and selection operator regression analysis. Additionally, molecular pathways that differ across subtypes were determined and OS-related genes linked to the prognosis of patients of CC were determined. Finally, a clinical prognostic gene signature was developed and validated. The relative infiltration level of immune cell subpopulations in different risk groups and subtypes was evaluated using the cell-type identification by estimating relative subsets of RNA transcripts (CIBERPORT) algorithm and single-sample GSEA (ssGSEA) techniques. RESULTS: The present study established two distinct OS subtypes (OS clusters A and B). Analysis using ssGSEA and CIBERSPORT revealed that OS cluster B exhibited a significant level of immune infiltration. A clinical prognostic gene signature was established using OS-related characteristic genes identified by examining the differentially expressed genes across both subtypes. Furthermore, patients with CC were grouped into high- and low-risk groups, with the low-risk group showing higher survival rates. Additionally, these individuals exhibited significant advantages in terms of survival and immunotherapy. Receiver operating characteristic curve analysis demonstrated the higher predictive value of the clinical prognostic gene signature. The outcomes of the validation group depicted congruence with those recorded in the training group. CONCLUSIONS: A new model was constructed based on eight OS-related characteristic genes to aid the prediction of the survival rates of individuals with CC. The present study contributes to the existing literature on the mechanisms of OS genes in CC and offers a fresh perspective for future advancements in immunotherapy for such individuals.