Motivation:

Although several prognostic signatures for lung adenocarcinoma (LUAD) have been developed, they are mainly based on a single-omics data set. This article aims to develop a novel set of prognostic signatures by combining genetic mutation and expression profiles of LUAD patients.

Methods:

The genetic mutation and expression profiles, together with the clinical profiles of a cohort of LUAD patients from The Cancer Genome Atlas (TCGA), were downloaded. Patients were separated into 2 groups, namely, the high-risk and low-risk groups, according to their overall survivals. Then, differential analysis was performed to determine differentially expressed genes (DEGs) and mutated genes (DMGs) in the expression and mutation profiles, respectively, between the 2 groups. Finally, a prognostic model based on the support vector machine (SVM) algorithm was developed by combining the expression values of the DEGs and the mutation times of the DMGs.

Results:

A total of 13 DEGs and 7 DMGs were recognized between the 2 groups. Their prognostic values were validated using independent cohorts. Compared with several existing signatures, the proposed prognostic signatures exhibited better prediction performance in the testing set. In addition, it is found that 1 of the 7 DMGs, GRIN2B, is mutated much more frequently in the high-risk group, showing a potential value as a therapy target.

Conclusions:

Combining multi-omics data sets is an applicable manner to identify novel prognostic signatures and to improve the prognostic prediction for LUAD, which will be heuristic to other types of cancers.

中文翻译：

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结合基因突变和表达谱确定肺腺癌的新预后生物标志物

动机：

尽管已经开发了几种肺腺癌 (LUAD) 的预后特征，但它们主要基于单一组学数据集。本文旨在通过结合 LUAD 患者的基因突变和表达谱来开发一组新的预后特征。

方法：

下载了基因突变和表达谱，以及来自癌症基因组图谱 (TCGA) 的一组 LUAD 患者的临床谱。根据总生存期将患者分为高危组和低危组2组。然后，进行差异分析以分别确定两组之间表达和突变谱中的差异表达基因（DEGs）和突变基因（DMGs）。最后，结合DEGs的表达值和DMGs的突变时间，开发了基于支持向量机（SVM）算法的预后模型。

结果：

两组之间共识别出 13 个 DEGs 和 7 个 DMGs。使用独立队列验证了他们的预后价值。与几个现有的特征相比，所提出的预后特征在测试集中表现出更好的预测性能。此外，发现 7 个 DMG 中的 1 个 GRIN2B在高危组中的突变频率更高，显示出作为治疗目标的潜在价值。

结论：

结合多组学数据集是识别新预后特征和改善 LUAD 预后预测的一种适用方式，这将启发其他类型的癌症。