Luo Yongjin, Tang Lihua, Zeng Zhonghong, Trang DinhHuyen, Mo Dan, Yang Yihua
Guangxi Reproductive Medical Center, the First Affiliated Hospital of Guangxi Medical University, Nanning, 530021, China; Nanning Second People's Hospital, Nanning, 530021, China.
Nanning Second People's Hospital, Nanning, 530021, China.
Transl Oncol. 2025 Oct;60:102487. doi: 10.1016/j.tranon.2025.102487. Epub 2025 Aug 9.
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.
宫颈癌（CC）患者的死亡率和治疗失败主要归因于广泛转移和化疗耐药。免疫疗法已成为CC患者关键的临床治疗策略；然而，目前的方法和生物标志物不足以准确预测免疫疗法反应和患者预后。本研究利用来自TCGA-CESC的多组学数据、GEO队列以及CC患者的临床数据，全面分析了铁死亡和细胞衰老这两个与肿瘤发生、进展及治疗密切相关的过程。基于铁死亡和细胞衰老相关模式，识别出了两个具有不同预后和肿瘤微环境（TME）特征的不同簇。随后构建了一个预后模型，该模型在预测CC预后和免疫疗法反应方面显示出强大的可靠性。低风险组患者表现出丰富的免疫细胞浸润、较低的TIDE评分、较高的IPS评分以及免疫检查点抑制剂相关基因（如PDCD1和CTLA4）的较高表达水平，这些与更好的总体结局相关。临床样本验证证实了CC中模型相关基因的差异表达，进一步支持了模型的准确性。该预后模型为预测CC预后和优化免疫疗法提供了有价值的见解，为个性化治疗策略带来潜在益处。
