摘要
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,IC50)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
Objective To construct a prognostic model of cervical cancer based on cuproptosis-related long noncoding RNAs(CRLs)and analyze the differences in drug sensitivity among different risk groups,in order to provide theoretical basis for the prediction of prog-nosis and individualized treatment of cervical cancer.Methods The gene-expression profiles,mutation data,and clinical data of 304 cervical cancer patients were downloaded from The Cancer Genome Atlas(TCGA)database.The patients were divided into a training set(n=152)and a test set(n=152)by random sampling.Pearson correlation analysis was used to identify CRLs.Using univariate Cox,LASSO,and multivariate Cox regression analysis,a CRL risk score model was constructed in the training set,and validated in the test set and the entire queue.Based on the median risk score,the patients in the training and test sets were divided into high-risk groups(the training set:n=76,the test set:n=83)and low-risk groups(the training set:n=76,the test set:n=69).The CRL risk score model was evaluated using Kaplan-Meier(K-M)survival analysis,receiver operating characteristic(ROC)curves,univariate and multivariate Cox regression,and principal component analysis(PCA).A nomogram predicting the prognosis of cervical cancer patients using CRL risk score model com-bined with clinicopathological characteristics and its calibration curve were constructed.The potential molecular mechanisms of this model was explored through gene set enrichment analysis(GSEA).The correlation between immune cell infiltration and risk score was analyzed by Spearman correlation analysis.The gene mutation map of cervical cancer patients was used to analyze the correlation between the CRL risk score model and somatic mutations.The sensitivity of immunotherapy drugs and the difference in half maximal inhibitory concentration(IC50)values of 20 chemotherapy drugs among different risk groups were evaluated.Results A total of 704 CRLs were obtained,and a risk score prediction model consisting of 6 CRLs(AC103591.4,AC021851.
作者
张玉俊
赵璇
朱琳
地力亚尔·吾斯曼江
王岩
Zhang Yujun;Zhao Xuan;Zhu Lin;Diliyaer Wusimanjiang;Wang Yan(Public Health Institute,Xinjiang Medical University,Urumqi 830011,China;Medical Department,Xinjiang Medical University Affi liated Cancer Hospital,Urumqi 830000,China)
出处
《实用肿瘤杂志》
CAS
2024年第2期111-123,共13页
Journal of Practical Oncology
基金
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)。
