Exploring program-cell death patterns to predict prognosis and sensitivity of cervical cancer immunotherapy via multi-omics analysis and clinical samples.

backgroundCervical cancer (CC) progression and therapeutic resistance are driven by metastatic dissemination and immune evasion. Although immunotherapy has emerged as a promising strategy, current biomarkers fail to adequately predict patient prognosis or immune checkpoint inhibitor (ICI) responsiveness. Programmed cell death (PCD) pathways are intricately linked to tumor-immune crosstalk, yet their systematic integration into predictive models remains unexplored in CC.

methodsWe constructed a prognostic gene model for PCD by mining the Cancer Genome Atlas (TCGA), GEO, and Genecards databases. The predictive capability of the model was assessed using Kaplan-Meier (K-M) analysis and Receiver Operating Characteristic (ROC) curve analysis. A nomogram was generated through Cox regression. The model was validated in both training and testing cohorts. Real-time quantitative PCR (qRT-PCR) and immunohistochemistry were used to verify the expression of the model genes. Finally, functional analysis of low- and high-risk groups based on the median risk score was performed, including immune infiltration, genomic mutations, tumor mutational burden (TMB), and drug sensitivity.

resultsWe established a prognostic model based on six PCD-related genes: CD46, TFRC, PGK1, GNG5, GAPDH, and PLAU. The risk score demonstrated good performance, with area under the curve (AUC) values indicating strong predictive ability (TCGA: AUC 1-year = 0.761, AUC 3-year = 0.754, AUC 5-year = 0.803; GEO: AUC 1-year = 0.702, AUC 3-year = 0.632, AUC 5-year = 0.579). Higher risk scores were associated with poorer overall survival (OS). Additionally, low-risk patients exhibited increased immune cell infiltration, higher IPS scores, enhanced expression of PDCD1 and CTLA4, and greater sensitivity to Niraparib, Paclitaxel, and Cisplatin. qRT-PCR confirmed overexpression of CD46, TFRC, PGK1, GNG5, and PLAU in cervical cancer cell lines and tissues, with consistent findings in immunohistochemistry (IHC).

conclusionThis study establishes CDI as the PCD-based immune signature for CC, enabling precise prognosis prediction and ICI candidate selection. The CDI framework provides actionable insights for combination therapies targeting PCD-immune interplay, with translational potential for personalized oncology.