Abstract
Mortality and treatment failure in cervical cancer (CC) patients are primarily due to extensive metastasis and chemoresistance. Immunotherapy has emerged as a crucial clinical treatment strategy for CC patients; however, the current methods and biomarkers are inadequate for accurately predicting immunotherapy responses and patient prognosis. This study comprehensively analyzed ferroptosis and cellular senescence, two processes intricately linked to tumorigenesis, progression, and therapy, utilizing multi-omics data from TCGA-CESC, GEO cohorts, and clinical data from CC patients. Based on ferroptosis- and cellular senescence -related patterns, two distinct clusters with divergent prognoses and tumor microenvironment (TME) characteristics were identified. A prognostic model was subsequntly constructed, demonstrating robust reliability in predicting CC prognosis and response to immunotherapy. Patients in the low-risk group exhibited enriched immune cell infiltration, lower TIDE scores, higher IPS scores, and higher expression levels of immune checkpoint inhibitor-related genes, such as PDCD1 and CTLA4, which were associated with improved overall outcomes. Validation with clinical samples confirmed the differential expression of model-associated genes in CC, further supporting the model's accuracy. This prognostic model provides valuable insights into predicting CC prognosis and optimizing immunotherapy, offering potential benefits for personalized treatment strategies.