张玉俊,赵璇,朱琳,地力亚尔·吾斯曼江,王岩

• 1:新疆医科大学公共卫生学院
• 2:新疆医科大学附属肿瘤医院医务科

摘要(Abstract)：

目的 基于铜死亡相关长链非编码RNA(cuproptosis-related long noncoding RNA,CRL)构建子宫颈癌预后模型并分析不同风险组间药物敏感性差异，为子宫颈癌患者预后预测及个体化治疗提供理论依据。方法 从癌症基因组图谱（The Cancer Genome Atlas,TCGA）数据库中下载304例子宫颈癌患者的基因表达谱、突变数据和临床数据，使用随机抽样的方法将患者分为训练集（n=152例）和测试集（n=152）。采用Pearson相关性分析鉴定CRL。应用单因素Cox、LASSO和多因素Cox回归分析在训练集中构建CRL风险评分模型，在测试集和整个队列中进行验证，并根据风险评分中位数将训练集和测试集患者分为高风险组（训练集76例和测试集83例）和低风险组（训练集76例和测试集69例）。使用Kaplan-Meier(K-M)生存分析、受试者工作特征（receiver operating characteristic,ROC）曲线、单因素与多因素Cox回归和主成分分析（principal component analysis,PCA）评估CRL风险评分模型，并构建结合临床病理特征和CRL风险评分模型的列线图和校准曲线。通过基因集富集分析（gene set enrichment analysis,GSEA）探索该模型的潜在分子机制。使用Spearman相关分析探讨免疫细胞浸润与风险评分之间的相关性。绘制子宫颈癌患者基因突变图谱，分析CRL风险评分模型与体细胞变异之间的相关性。分析免疫治疗药物的敏感性和20种化疗药物在不同风险群体中的半抑制浓度（half maximal inhibitory concentration,IC_(50)）值差异。结果 共获得704个CRL，经单因素Cox、LASSO和多因素Cox回归分析最终构建包含6个CRL(AC103591.4、AC021851.1、MNX1-AS1、FAM27E3、AL603832.1和AC097505.1)的风险评分预测模型。K-M生存曲线、ROC曲线下面积（area under the curve,AUC）和PCA分析均验证该模型具有良好的预测能力。多因素Cox回归显示，CRL风险评分可作为独立预后因子（P<0.05）。列线图对子宫颈癌患者的1、3和5年总生存（overall survival,OS）具有较好的预测能力。GSEA结果显示，高风险组与癌症通路相关。免疫细胞浸润结果表明，多数免疫细胞与CRL风险评分呈正相关（均r>0，均P<0.05）。免疫检查点分析结果显示，低风险组患者免疫检查点表达较高。基因突变图谱结果表明，高低风险组间肿瘤突变负荷（tumor mutation burden,TMB）比较，差异无统计学意义（P>0.05）。药物敏感性分析结果显示，免疫治疗药物细胞毒T淋巴细胞相关抗原4对低风险组患者疗效较好，阿卡地新、二甲基草酰甘氨酸、多柔比星、索拉非尼和阿糖胞苷5种药物的IC_(50)值在高低风险组间比较，差异均具有统计学意义（均P<0.05）。结论 6个CRL的风险评分特征可独立预测子宫颈癌患者的预后，有助于阐明子宫颈癌中CRL的机制，并为患者临床个体化治疗提供理论指导。

关键词(KeyWords)： 子宫颈癌;铜死亡;长链非编码RNA;药物敏感性

Abstract:

Keywords:

基金项目(Foundation): 省部共建中亚高发病成因与防治国家重点实验室开放课题项目（SKL-HIDCA-2020-33,SKL-HIDCA-2021-13）;; 新疆维吾尔自治区自然科学基金项目（2021D01C379）

作者(Author): 张玉俊,赵璇,朱琳,地力亚尔·吾斯曼江,王岩

DOI: 10.13267/j.cnki.syzlzz.2024.019

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