Cuproptosis-related gene biomarkers to predict overall survival outcomes in cervical cancer

Background: Cervical carcinoma (CC) remains a prevailing gynecologic malignancy. Cuproptosis is a recently identified and extensively researched type of cellular death. However, the understanding of cuproptosis-associated genes in CC and their correlation with prognosis is still uncertain. Methods: We identified 6 genes related to cuproptosis that were differentially expressed between normal cervical tissue and CC from 18 cuproptosis-related genes. We obtained prognostic information, along with associated clinical information, in normal cervical tissue and CCs from TCGA (The Cancer Genome Atlas). Results: Six differentially expressed cuproptosis-associated genes were utilized to develop a prognostic pattern and categorize the overall CC patients in the TCGA cohort into low- or high-cohort groups. Gene Expression Omnibus (GEO) database data were employed to certify the model of prognosis. There was a significantly higher survival rate for CC patients in low-risk than in high-risk group (p = 0.001) for the TCGA cohort. Both univariate (p = 0.0012) and multivariate Cox regression analyses (p < 0.001) illustrated that the risk score was obviously linked to poor survival. The outside validation was carried out by data in GEO database. There also existed an exceedingly obvious distinction in the survival rate between the two groups (p = 0.0239). There were also evident differences between poor survival and risk score in univariate (p = 0.0242) as well as multivariate Cox regression analysis (p = 0.0279). KEGG (Kyoto Encyclopedia of Genes and Genomes) and GO (Gene Ontology) were utilized. The results forecasted that extracellular matrix organization, signaling receptor activator activity, receptor ligand activity, neuroactive ligand-receptor interplay, and cytokine-cytokine receptor interplay were closely associated with CC cuproptosis. Conclusions: The risk prediction model based on genes related to cuproptosis could excellently predict CC prognosis. The prognostic model can offer a vital reference for future biomarkers and therapeutic targets for the sake of precise therapy of cervical carcinoma.