Integrated analysis of hypoxia-associated lncRNA signature to predict prognosis and immune microenvironment of lung adenocarcinoma patients

Lung adenocarcinoma (LUAD) represents the main lung cancer (LC) subtype that possesses a disappointing clinical outcome over the decades. Tumor hypoxia is closely bound up with dismal survival for malignant tumor cases. We identified hypoxia-associated long non-coding RNA (lncRNA) signature to be an explicit indicator for predicting prognosis. The present work acquired RNA-seq and associated clinical data from The Cancer Genome Atlas (TCGA) database. Consensus cluster analysis characterized the hypoxia status of LUAD patients. Cox regression analysis with the least absolute shrinkage and selection operator (LASSO) method determined significantly prognosis-related lncRNAs which were used to create a prognostic model. Diverse statistical approaches like the Kaplan-Meier curve, receiver operating characteristic (ROC) curve, and nomogram were adopted to verify the accuracy of the risk score. The potential immune environment landscape was unearthed by the CIBERSORT algorithm. Three hypoxia-related clusters were determined and 221 differentially expressed hypoxia-related lncRNAs were screened out. We developed a new predictive model based on seven lncRNAs (LINC00941, AC022784.1, AC079949.2, LINC00707, AL161431.1, AC010980.2 and AC090001.1). Kaplan-Meier curves and ROC plots uncovered the reliable predictive power of the risk score model. In addition, the immunosuppressive landscape was presented in the high-risk group by immune cell infiltration analysis. The seven hypoxia lncRNAs survival signature in our article are robust, accurate tools for predicting overall survival in LUAD patients.

肺腺癌（LUAD）是肺癌（LC）的主要亚型，数十年来其临床预后始终不容乐观。肿瘤缺氧与恶性肿瘤患者的不良生存结局紧密相关。本研究鉴定出缺氧相关长链非编码RNA（lncRNA）特征可作为预后预测的明确标志物。本研究从癌症基因组图谱（TCGA）数据库获取了RNA测序（RNA-seq）及配套临床数据。通过一致性聚类分析明确肺腺癌患者的缺氧分型特征。采用基于最小绝对收缩和选择算子（LASSO）的Cox回归分析，筛选出与预后显著相关的lncRNA，并以此构建预后风险模型。本研究采用Kaplan-Meier曲线、受试者工作特征（ROC）曲线及列线图等多种统计方法，验证了该风险评分模型的准确性。通过CIBERSORT算法探究了样本潜在的免疫微环境特征。研究共确定3个缺氧相关亚型，并筛选出221个差异表达的缺氧相关lncRNA。本研究基于7个lncRNA（LINC00941、AC022784.1、AC079949.2、LINC00707、AL161431.1、AC010980.2及AC090001.1）构建了全新的预测模型。Kaplan-Meier曲线与ROC曲线分析证实该风险评分模型具备可靠的预测效能。此外，免疫细胞浸润分析显示，高危组呈现免疫抑制微环境。本研究中的7个缺氧相关lncRNA生存特征，可作为肺腺癌患者总体生存预测的稳健、精准工具。