多项研究表明失巢凋亡影响癌症的发生、转移和预后。本研究旨在鉴定宫颈癌（CC）中失巢凋亡相关的标记基因。采用最小绝对收缩和选择算子（LASSO）结合Cox回归分析构建预后模型并分析riskcore的独立预后能力。使用受试者工作特征曲线（ROC）和生存曲线来评估和验证模型的性能和准确性。利用风险评分结合临床信息绘制CC预后模型列线图。我们分析了预后风险评分与免疫浸润水平之间的关系，并分析了免疫表型评分。最后，使用qRT-PCR检测来验证特征基因。通过Cox分析，我们发现预后风险模型可以独立于其他临床因素有效预测患者CC的风险。高危CC患者的免疫浸润水平和免疫表型评分均显着低于低危患者，表明高危患者可能对免疫治疗反应不良。特征基因的qRT-PCR结果与数据库中基因表达结果一致。基于CC中失巢凋亡相关基因构建的预后模型可以预测CC患者的预后。这里描述的模型可以为CC患者的预后提供有效的依据。支持在临床治疗期间评估预后风险和制定个性化方案。

Several studies have demonstrated that anoikis affects the development, metastasis and prognosis of cancer.This study aimed to identify anoikis-related marker genes in cervical cancer (CC).Least absolute shrinkage and selection operator (LASSO) combined with Cox regression analysis was used to construct a prognostic model and analyse the independent prognostic ability of riskscore. Receiver operating characteristic curve (ROC) and survival curves were used to evaluate and verify the performance and accuracy of the model. The nomogram of CC prognostic model was drawn using riskscore combined with clinical information. We analysed the relationship between prognostic riskscore and immune infiltration level and analysed immunophenoscore. Finally, qRT-PCR assay was used to verify the feature genes.By Cox analysis, we found that the prognostic risk model could effectively predict the risk of CC in patients independently of other clinical factors. Both the levels of immune infiltration and the immunophenoscore were significantly lower in high-risk CC patients than those in low-risk patients, revealing that high-risk patients were likely to have bad response to immunotherapy. The qRT-PCR results of the feature genes were consistent with the results of gene expression in the database.The prognostic model constructed, based on anoikis-related genes in CC, could predict the prognosis of CC patients.The model described here can provide effective support for assessing prognostic risk and devising personalised protocols during clinical treatment.