Ferroptosis and cellular senescence -Related Genes in Cervical Cancer: Mechanistic Insights from Multi-Omics and Clinical Sample Analysis

• Published in: Translational oncology Vol. 60; p. 102487
• Main Authors: Luo, Yongjin, Tang, Lihua, Zeng, Zhonghong, Trang, DinhHuyen, Mo, Dan, Yang, Yihua
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.10.2025; Neoplasia Press; Elsevier
• Subjects: Cellular senescence; Cervical cancer; Ferroptosis; Immunotherapy; Original Research; Ferroptosis; Immunotherapy; Cervical cancer; Cellular senescence
• ISSN: 1936-5233; 1936-5233
• DOI: 10.1016/j.tranon.2025.102487

•Ferroptosis and aging have serious effects on human tumors.•The GSVA scores of ferroptosis and aging are upregulated in cervical cancer tissues.•Ferroptosis and aging score related genes in different data sets can effectively predict the prognosis of cervical cancer patients.•Verification of abnormal expression of ferroptosis and aging score related genes in cervical cancer by multiple experiments. 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. [Display omitted]