Zhao Kai, Li Zulei, Tian Hui
Department of Thoracic Surgery, Qilu Hospital of Shandong University, Jinan, Shandong Province, China.
Department of Thoracic Surgery, Zibo Central Hospital, Zibo, Shandong Province, China.
Onco Targets Ther. 2018 Jun 12;11:3415-3424. doi: 10.2147/OTT.S158638. eCollection 2018.
Lung adenocarcinoma (LAC) accounts for more than a half of non-small cell lung cancer with high morbidity and mortality. Progression of treatment has not accelerated the improvement of its prognosis. Hence, it is an urgent need to develop novel biomarkers for its early diagnosis and treatment.
In this study, we proposed to identify LAC survival-related genes through comprehensive analysis of large-scale gene expression profiles. LAC gene expression data sets were obtained from The Cancer Genome Atlas (TCGA). Identification of differentially expressed genes (DEGs) in LAC compared with adjacent normal lung tissues was first performed followed by univariate Cox regression analysis to obtain genes that are significantly associated with LAC survival (SurGenes). Then, we conducted sure independence screening (SIS) for SurGenes to identify more reliable genes and the prognostic signature for LAC survival prediction. Another two lung cancer data sets from TCGA and Gene Expression Omnibus (GEO) were used for the validation of prognostic signature.
A total of 20 genes were obtained, which were significantly associated with the overall survival (OS) of LAC patients. The prognostic signature, a weighted linear combination of the 20 genes, could successfully separate LAC samples with high OS from those with low OS and had robust predictive performance for survival (training set: -value <2.2×10; testing set: -value =2.04×10, area under the curve (AUC) =0.615). Combined with GEO data set, we obtained four genes, that is, , , , and that are found in both the prognostic signature and DEGs of LAC in GEO data set.
The prognostic signature combined with multi-gene expression profiles provides a moderate OS prediction for LAC and should be helpful for appropriate treatment method selection.
肺腺癌（LAC）占非小细胞肺癌的一半以上，发病率和死亡率都很高。治疗进展并未加速其预后的改善。因此，迫切需要开发新的生物标志物用于其早期诊断和治疗。
在本研究中，我们提议通过对大规模基因表达谱的综合分析来鉴定与LAC生存相关的基因。LAC基因表达数据集来自癌症基因组图谱（TCGA）。首先进行LAC与相邻正常肺组织中差异表达基因（DEG）的鉴定，然后进行单变量Cox回归分析以获得与LAC生存显著相关的基因（SurGenes）。然后，我们对SurGenes进行确定独立性筛选（SIS）以鉴定更可靠的基因和用于LAC生存预测的预后特征。另外两个来自TCGA和基因表达综合数据库（GEO）的肺癌数据集用于验证预后特征。
共获得20个基因，它们与LAC患者的总生存期（OS）显著相关。预后特征是这20个基因的加权线性组合，能够成功地将高OS的LAC样本与低OS的样本区分开来，并且对生存具有强大的预测性能（训练集：-值<2.2×10；测试集：-值=2.04×10，曲线下面积（AUC）=0.615）。结合GEO数据集，我们获得了四个基因，即GEO数据集中LAC的预后特征和DEG中都有的基因。
结合多基因表达谱的预后特征为LAC提供了适度的OS预测，应该有助于选择合适的治疗方法。
