构建并验证了一个基于 12 个基因的与缺氧相关的风险特征，发现 EXO1 是肺腺癌的预后生物标志物。

BACKGROUND

Increasing evidence has demonstrated the clinical importance of hypoxia and its related factors in lung adenocarcinoma (LUAD).

METHODS

RNA-seq datasets from The Cancer Genome Atlas (TCGA) were analyzed using the differentially expressed genes in hypoxia pathway by the Least Absolute Shrinkage and Selection Operator (LASSO) model. Applying gene ontology (GO) and gene set enrichment analysis (GSEA), a risk signature associated with the survival of LUAD patients was constructed between LUAD and normal tissue.

RESULTS

In total, 166 hypoxia-related genes were identified. Based on the LASSO Cox regression, 12 genes were selected for the development of the risk signature. Then, we designed an OS-associated nomogram that included the risk score and clinical factors. The concordance index of the nomogram was 0.724. ROC curve showed better predictive ability using the nomogram (AUC = 0.811 for 5-year OS). Finally, the expressions of the 12 genes were validated in two external datasets and EXO1 was recognized as a potential biomarker in the progression of LUAD patients.

CONCLUSIONS

Overall, our data suggested that hypoxia is associated with the prognosis, and EXO1 acted as a promising biomarker in LUAD.

背景

越来越多的证据表明，缺氧及其相关因素在肺腺癌（LUAD）中具有重要的临床意义。

方法

利用最小绝对收缩和选择算子（LASSO）模型分析癌症基因组图谱（TCGA）中的 RNA-seq 数据集，以确定缺氧途径中的差异表达基因。通过基因本体（GO）和基因集富集分析（GSEA），在 LUAD 和正常组织之间构建与 LUAD 患者生存相关的风险特征。

结果

总共确定了 166 个与缺氧相关的基因。基于 LASSO Cox 回归，选择了 12 个基因用于开发风险特征。然后，我们设计了一个与 OS 相关的列线图，其中包括风险评分和临床因素。列线图的一致性指数为 0.724。ROC 曲线显示，使用列线图具有更好的预测能力（AUC=0.811，用于 5 年 OS）。最后，在两个外部数据集验证了这 12 个基因的表达，发现 EXO1 是 LUAD 患者进展的潜在生物标志物。