肺腺癌中关键lncRNA的表达及免疫相关分析

目的:通过鉴定与肺腺癌（LUAD）预后相关的长非编码RNA（lncRNA），研究LUAD的发生机制及其预后意义，确定与LUAD预后相关的敏感性生物标志物，并对其进行免疫途径相关性分析。方法:从肿瘤基因组图谱数据库(TCGA)中获取与LUAD相关的数据，通过单因素Cox回归分析及套索算法（LASSO）筛选lncRNA，使用多因素Cox回归进行预后风险评分分析，建立预后风险模型，用计算曲线下面积（AUC）和Kaplan-Meier（K-M）生存分析方法评价模型的稳健性和准确性。利用K-M生存分析方法确定与生存状态相关的潜在生物标志物，并通过ImmLnc平台对其进行免疫途径相关性研究。结果:从49个与生存相关的lncRNAs中确定了12个预后相关生物标志物，通过K-M生存分析，MIR34AHG和PRKCA-AS1被确定为与预后相关的生物标志物(P＜0.05)。模型的3年和5年生存率的AUC分别为0.82和0.846。与MIR34AHG相关的免疫途径分别为“细胞因子受体”(P＜0.05)，“抗原处理和提呈”(P＜0.05)，与PRKCA-AS1相关的免疫途径为“抗原处理和提呈”(P＜0.05)。结论:通过对生物信息大数据的分析，我们确定了两个关键lncRNAs及其相关的免疫途径，为LUAD的预后评估提供了新的生物标志物。

Expression of key lncRNA in lung adenocarcinoma and correlation analysis of immunity

Objective:To identify the long non-coding RNA(lncRNA)related to the prognosis of LUAD,to explore the developmental mechanism of LUAD and its prognostic significance,to determine the sensitive biomarkers related to the prognosis of LUAD,and to analyze the immune pathway correlation of the key markers.Methods:Obtaining data related to LUAD from tumor Genome Map Database(TCGA).Screening lncRNA by single factor Cox regression analysis and lasso algorithm(LASSO).The prognostic risk score was analyzed by multivariate Cox regression,and the prognostic risk model was established.The robustness and accuracy of the model were evaluated by calculating the area under the curve(AUC)and Kaplan-Meier(K-M)survival analysis method.The potential biomarkers related to survival status are identified by K-M survival analysis method.The immune pathway correlation of prognostic biomarkers was studied by ImmLnc platform.Results:12 prognostic biomarkers were identified from 49 survival-related lncRNAs.By K-M survival analysis,MIR34AHG and PRKCA-AS1 were identified as biomarkers related to prognosis(P＜0.05).The AUC of 3-year and 5-year survival rates of the model were 0.82 and 0.846,respectively.The immune pathway related to MIR34AHG are "cytokine receptors"(P＜0.05),"antigen processing and presentation"(P＜0.05),and the immune pathway related to PRKCA-AS1 is "antigen processing and presentation"(P＜0.05).Conclusion:By analyzing the bioinformation of big data,we identified two key lncRNAs and their related immune pathways,which provided a new biomarker for evaluating the prognosis of LUAD.