肺腺癌中端粒相关lncRNA的表达及免疫相关分析
Expression of telomere-associated lncRNA in lung adenocarcinoma and its correlation with the immune system
韦春浩 1宿振国 1刘相东2
作者信息
- 1. 滨州医学院烟台附属医院检验科,山东烟台 264000
- 2. 山东第一医科大学附属省立医院检验科,山东济南 256200
- 折叠
摘要
目的:本研究致力于开发一种基于端粒相关长链非编码RNA(lncRNA)的肺腺癌(LUAD)预后模型,以填补当前在高活性端粒酶作为肿瘤治疗靶点研究中的空白.通过综合分析端粒相关lncRNA的表达模式和临床数据,旨在构建可靠模型,预测LUAD患者的生存率和疾病进展,从而为个性化治疗提供精准指导.方法:通过癌症基因组图谱(TCGA)数据库提取肺腺癌(LUAD)样本数据,包括长链非编码RNA(lncRNA)表达谱和临床信息.采用共表达分析识别与端粒相关lncRNA,再通过Cox回归和LASSO回归分析构建LUAD患者生存期风险模型.模型预测效力通过Log-rank检验和Kaplan-Meier分析评估.并使用GO和KEGG分析进行端粒相关lncRNA功能富集可视化.免疫浸润评估采用CIBERSORT算法,分析肿瘤组织中免疫细胞的分布.结果:本研究构建了一个基于端粒相关lncRNA的预测模型,该模型包括6个预后相关lncRNA,它们在肺腺癌中的表达水平在不同风险组和临床模型中存在显著差异.高风险组的患者总生存(OS)时间显著缩短,且风险评分被验证为肺腺癌OS的独立预后因素.通过GSEA分析,揭示了与高表达基因相关的关键生物学通路,包括细胞周期、DNA复制和有丝分裂中的微管细胞骨架组织.免疫检查点分析表明PD-1、CTLA-4等基因在高风险组中表达水平显著不同,这一发现暗示针对肺腺癌患者,通过评估这些基因的表达情况,可以为实施个性化的免疫检查点阻断疗法提供依据.这表明,基于患者特定的基因表达模式,可以定制化免疫治疗方案,从而提高治疗的针对性和效果.结论:端粒相关lncRNAs风险模型具有准确预测LUAD患者预后的潜力,可作为未来潜在的治疗靶点.
Abstract
Objective:This research focuses on the development of a prognostic model for lung adenocarcinoma(LU AD),centered around telomere-associated long non-coding RNAs(lncRNAs).This initiative aims to bridge the existing research gap concerning the targeting of highly active telomerase in cancer therapeutics.Through an in-depth analysis of the expression patterns of telomere-associated lncRNAs and pertinent clinical data,the study seeks to establish a robust predictive model.This model is designed to accurately forecast the survival rates and progression of LUAD,thereby providing targeted and refined guidance for personalized treatment approaches.Methods:Lung adenocarcinoma(LUAD)sample data,including long non-coding RNA(lncRNA)expression profiles and clinical information,were extracted from The Cancer Genome Atlas(TCGA)database.Co-expression analysis was utilized to identify telomere-associated lncRNAs,followed by the construction of a LUAD patient survival risk model using Cox regression and LASSO regression analyses.The predictive efficacy of this model was assessed through Log-rank tests and Kaplan-Meier analysis.Additionally,Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)analyses were employed for visualizing the functional enrichment of telomere-associated lncRNAs.Immune infiltration was evaluated using the CIBERSORT algorithm,analyzing the distribution of immune cells within the tumor tissue.Results:This study developed a prognostic model based on telomere-associated long non-coding RNAs(lncRNAs)for lung adenocarcinoma(LUAD),incorporating six prognostically significant lncRNAs.These lncRNAs exhibited notable differences in expression levels across various risk groups and clinical models in LUAD.Patients categorized in the high-risk group demonstrated significantly reduced overall survival(OS)times,and the risk score was validated as an independent prognostic factor for LUAD OS.Gene Set Enrichment Analysis(GSEA)revealed key biological pathways associated with highly expressed genes,including cell cycle,DNA replication,and microtubule cellular skeleton organization during mitosis.Immune checkpoint analysis highlighted significant expression differences in genes such as PD-1 and CTLA-4 in the high-risk group,suggesting the potential for personalized therapy through immune checkpoint blockade.Conclusion:The telomere-associated lncRNAs risk model demonstrates promising potential in accurately predicting the prognosis of patients with lung adenocarcinoma(LUAD),positioning it as a prospective therapeutic target in future treatments.
关键词
肺腺癌/端粒/预后模型/免疫浸润/生物信息学Key words
Lung Adenocarcinoma/Telomere/Prognostic model/Immune Infiltration/Bioinformatics引用本文复制引用
出版年
2024
NETL
NSTL
维普
万方数据