Liu Boxuan, Yang Shuanying
Department of Critical Care and Respiratory Medicine, The Second Affiliated Hospital, Xi'an Jiaotong University, Xi'an, Shaanxi, 710004, People's Republic of China.
Int J Gen Med. 2021 Oct 27;14:7145-7158. doi: 10.2147/IJGM.S334601. eCollection 2021.
Lung adenocarcinoma is the most common pathological type among non-small cell lung cancer. Although huge progress has been made in terms of early diagnosis and precision treatment in recent years, the overall 5-year survival rate of a patient remains low. In our study, we try to construct an autophagy-related lncRNA prognostic signature that may guide clinical practice.
The mRNA and lncRNA expression matrix of lung adenocarcinoma patients were retrieved from the TCGA database. Next, we constructed a co-expression network of lncRNAs and autophagy-related genes. Lasso regression and multivariate Cox regression were then applied to establish a prognostic risk model. Subsequently, a risk score was generated to differentiate the high and low risk groups and a ROC curve and nomogram to visualize the predictive ability of the current signature. Finally, gene ontology and pathway enrichment analysis were executed via GSEA.
A total of 1,703 autophagy-related lncRNAs were screened and five autophagy-related lncRNAs (LINC01137, AL691432.2, LINC01116, AL606489.1, and HLA-DQB1-AS1) were finally included in our signature. Judging from univariate (HR=1.075, 95% CI=1.046-1.104) and multivariate (HR=1.088, 95% CI=1.057-1.120) Cox regression analysis, the risk score is an independent factor for LUAD patients. Further, the AUC value based on the risk score for 1-year, 3-years, and 5-years, was 0.735, 0.672, and 0.662, respectively, indicating a reliable model. Drug sensitivity analysis revealed low risk patients were more sensitive to Gemcitabine and Gefitinib, while high risk patients had a better response to Paclitaxel and Erlotinib. Moreover, the lncRNAs included in our signature were primarily enriched in the autophagy process, metabolism, p53 pathway, and JAK/STAT pathway. Finally, a multi-omics analysis of correlated genes showed CFLAR overexpressed in the tumor sample, while GAPDH and MLST8 had a slightly higher expression in the normal sample.
Overall, our study indicated that the prognostic model we generated had certain predictability for LUAD patients' prognosis and the related genes might be potential biomarkers and therapeutic targets.
肺腺癌是非小细胞肺癌中最常见的病理类型。尽管近年来在早期诊断和精准治疗方面取得了巨大进展，但患者的总体5年生存率仍然较低。在我们的研究中，我们试图构建一个与自噬相关的lncRNA预后特征，以指导临床实践。
从TCGA数据库中检索肺腺癌患者的mRNA和lncRNA表达矩阵。接下来，我们构建了lncRNAs与自噬相关基因的共表达网络。然后应用Lasso回归和多变量Cox回归建立预后风险模型。随后，生成风险评分以区分高风险组和低风险组，并绘制ROC曲线和列线图以直观显示当前特征的预测能力。最后，通过GSEA进行基因本体和通路富集分析。
共筛选出1703个与自噬相关的lncRNAs，最终5个与自噬相关的lncRNAs（LINC01137、AL691432.2、LINC01116、AL606489.1和HLA-DQB1-AS1）被纳入我们的特征。单变量（HR=1.075，95%CI=1.046-1.104）和多变量（HR=1.088，95%CI=1.057-1.120）Cox回归分析表明，风险评分是LUAD患者的独立因素。此外，基于风险评分的1年、3年和5年的AUC值分别为0.735、0.672和0.662，表明该模型可靠。药物敏感性分析显示，低风险患者对吉西他滨和吉非替尼更敏感，而高风险患者对紫杉醇和厄洛替尼反应更好。此外，我们特征中包含的lncRNAs主要富集于自噬过程、代谢、p53通路和JAK/STAT通路。最后，对相关基因的多组学分析显示，CFLAR在肿瘤样本中过表达，而GAPDH和MLST8在正常样本中表达略高。
总体而言，我们的研究表明，我们生成的预后模型对LUAD患者的预后具有一定的预测能力，相关基因可能是潜在的生物标志物和治疗靶点。
