Screening of ferroptosis genes related to the prognosis of cervical cancer and construction of a prognostic model

Objective To screen ferroptosis genes related to the prognosis of cervical cancer and to construct a prognosis model. Methods Ferroptosis genes were obtained from FerrDb database, and cervical cancer related data were obtained from The Genome-Wide Association Study Catalog database and The Cancer Genome Atlas database. Transcriptome-Wide Association Study, colocalization analysis and differential expression analysis were conducted to screen out candidate ferroptosis genes; Gene Ontology functional and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were conducted on candidate genes. Univariate Cox regression analysis was used to further screen out genes related to the prognosis of cervical cancer. Kaplan-Meier method was used to analyze the relationship between genes and the overall survival of patients. The expression levels of genes in pan-cancer were analyzed through the TIMER database. Two prognostic models were conducted, Model 1 included age and tumor stage, while Model 2 incorporated age, tumor stage, and prognostic genes. The predictive capabilities of the two models were compared. Results A total of 91 candidate genes related to ferroptosis were obtained. Univariate Cox regression analysis showed that 15 genes were associated with the prognosis of cervical cancer. CA9, SCD, TFRC, QSOX1 and CDO1 were risk factors affecting the prognosis of cervical cancer patients (P＜0.05), while PTPN6, ALOXE3, HELLS, IFNG, MIOX, ALOX12B, DUOX1, ALOX15, AQP3 and IDO1 were protective factors (P＜0.05). The mRNA expression levels of the 15 genes showed significant upregulation or downregulation in at least 7 types of cancers, among which TFRC was associated with the largest number of cancer types. Kaplan-Meier analysis showed that HELLS, DUOX1 and ALOXE3 were associated with poor prognosis in cervical cancer. The AUC of the model 1 for predicting 1-year and 3-year overall survival rates of cervical cancer patients was 0.455 and 0.478, and the AUC of Model 2 was 0.854 and 0.595. Model 2 (C-index = 0.727) had better predictive ability than Model 1 (C-index = 0.502).Conclusion The prognostic model composed of 15 prognostic-related genes selected based on bioinformatics has better predictive performance for the survival outcomes of cervical cancer patients, providing important reference value for the prognostic assessment of cervical cancer patients.