| 摘要: |
| ［摘要］ 目的 基于铜死亡相关长链非编码RNAs（CRLS）构建肺腺癌的预后风险模型并进行实验验证。方法 从TCGA数据库获取肺腺癌数据，构建CRLS预后风险模型并计算风险评分，根据中位数将患者分为高风险组（≥风险评分中位数）和低风险组（<风险评分中位数）。使用风险评分分布曲线和散点图显示风险评分与生存状态的关系，采用主成分分析（PCA）、Kaplan-Meier分析及C-index曲线评估预测效能。利用肿瘤药敏多组学库评估两组对不同药物的敏感性，通过肿瘤免疫功能障碍和排除算法（TIDE）比较两组对免疫检查点抑制剂（ICIs）的敏感性。选取CASC15进行细胞实验，将其转染至A549细胞和H1975细胞，通过实时荧光定量聚合酶链反应（RT-qPCR）、CCK-8和Transwell实验评估细胞表达、增殖和侵袭能力。结果 最终筛选出7个与总生存期（OS）显著相关的CRLS，风险评分=-0.308 1×AC090948.1的表达量+0.460 4×CASC15的表达量-0.417 0×AL353804.1的表达量-1.432 2×AP000302.1的表达量+0.441 7×AC026356.1的表达量-0.619 0×AC007613.1的表达量+0.271 0×AL161431.1的表达量。随着风险评分增加，患者死亡数升高。所构建的风险模型能够有效区分低风险和高风险患者，且低风险组生存预后优于高风险组（log-rank检验： χ2=29.352，P<0.001）。风险评分的符合指数高于年龄、性别和临床分期，其预测准确性较好。高风险组顺铂、紫杉醇、多西他赛、5-氟尿嘧啶、长春瑞滨、喜树碱、吉西他滨、吉非替尼、塞卡替尼的IC50值低于低风险组，埃罗替尼和雷帕霉素的IC50值高于低风险组，差异有统计学意义（P<0.05）。低风险组TIDE评分低于高风险组，差异有统计学意义（P<0.05）。细胞实验显示，与空白组和对照组比较，CASC15组细胞增殖率、克隆形成率和细胞侵袭率显著增加（P<0.05）。结论 7个CRLS组成的风险模型可以有效预测肺腺癌患者的生存预后，模型的风险评分可作为独立的预后因素。该模型为肺腺癌患者在选择常规药物或免疫治疗时提供了新的理论依据。 |
| 关键词: 肺腺癌 铜死亡 长链非编码RNAs 预后风险模型 药敏分析 免疫治疗 |
| DOI：10.3969/j.issn.1674-3806.2025.01.10 |
| 分类号:R 734.2 |
| 基金项目:内蒙古自治区自然科学基金项目（编号：2023QN08049）；内蒙古自治区高等学校科学研究项目（编号：NJZY23091）；内蒙古自治区首府地区公立医院高水平临床专科建设科技项目（编号：2024SGGZ137）；呼和浩特市基础研究与应用基础研究项目（编号：2024-规-基-5） |
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| Construction of a prognostic risk model for lung adenocarcinoma based on cuproptosis-related lncRNAs signature and the experimental validation |
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JIAN Ruonan1, ZHEN Hua1, YANG Lei1, GUO Jingjing2
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1.Department of Pharmacy, Huhhot First Hospital, Huhhot 010030, China; 2.School of Pharmacy, Baotou Medical College, Inner Mongolia University of Science and Technology, Baotou 014040, China
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| Abstract: |
| ［Abstract］ Objective To construct a prognostic risk model for lung adenocarcinoma based on cuproptosis-related long non-coding RNAs(lncRNAs) signature and to verify the model by experiment. Methods The lung adenocarcinoma data were obtained from The Cancer Genome Atlas(TCGA) database to construct the prognostic risk model of cuproptosis-related lncRNAs signature(CRLS) and risk scores were calculated, and the patients were divided into high-risk group(≥the median of risk scores) and low-risk group(Results Seven CRLS were finally screened to be significantly associated with overall survival(OS), with risk scores=-0.308 1×AC090948.1 expression+0.460 4×CASC15 expression-0.417 0×AL353804.1 expression-1.432 2×AP000302.1 expression+0.441 7×AC026356.1 expression-0.619 0×AC007613.1 expression+0.271 0×AL161431.1 expression. As the risk scores increased, the number of patients who died increased. The constructed risk model was able to effectively differentiate between low-risk patients and high-risk patients, and the survival prognosis of the low-risk group was better than that of the high-risk group(log-rank test: χ2=29.352, P<0.001). The conformity index of the risk scores was higher than that of the age, gender, and clinical stage, and the predictive accuracy of the risk scores was better. The IC50 values of cisplatin, paclitaxel, docetaxel, 5-fluorouracil, vinorelbine, camptothecin, gemcitabine, gefitinib and ceritinib in the high-risk group were lower than those in the low-risk group, and the IC50 values of erlotinib and rapamycin in the high-risk group were higher than those in the low-risk group, and the differences were statistically significant(P<0.05). The TIDE scores of the low-risk group were lower than those of the high-risk group, and the differences were statistically significant(P<0.05). Cellular experiments showed that the cell proliferation rate, the clone formation rate and cell invasion rate in the CASC15 group were significantly increased compared with those in the blank group and the control group(P<0.05). Conclusion The risk model consisting of 7 CRLS can effectively predict the survival prognosis of lung adenocarcinoma patients, and risk score of the model can be used as an independent prognostic factor. The model provides a new theoretical basis for lung adenocarcinoma patients to choose conventional drugs or immunotherapy. |
| Key words: Lung adenocarcinoma Cuproptosis Long non-coding RNAs(lncRNAs) Prognostic risk model Drug sensitivity analysis Immunotherapy |
