The Key Laboratory of Zoonosis, Department of Pathogenobiology, College of Basic Medicine, Jilin University, Changchun 130021, China.
The Key Laboratory for Bionics Engineering, Jilin University, Changchun 130021, China.
Int J Mol Sci. 2020 Nov 11;21(22):8479. doi: 10.3390/ijms21228479.
Accumulating evidence indicates that the reliable gene signature may serve as an independent prognosis factor for lung adenocarcinoma (LUAD) diagnosis. Here, we sought to identify a risk score signature for survival prediction of LUAD patients. In the Gene Expression Omnibus (GEO) database, GSE18842, GSE75037, GSE101929, and GSE19188 mRNA expression profiles were downloaded to screen differentially expressed genes (DEGs), which were used to establish a protein-protein interaction network and perform clustering module analysis. Univariate and multivariate proportional hazards regression analyses were applied to develop and validate the gene signature based on the TCGA dataset. The signature genes were then verified on GEPIA, Oncomine, and HPA platforms. Expression levels of corresponding genes were also measured by qRT-PCR and Western blotting in HBE, A549, and PC-9 cell lines. The prognostic signature based on eight genes (, , , , , , , and ) was established, which was independent of other clinical factors. The risk model offered better discrimination between risk groups, and patients with high-risk scores tended to have poor survival rate at 1-, 3- and 5-year follow-up. The model also presented better survival prediction in cancer-specific cohorts of age, gender, clinical stage III/IV, primary tumor 1/2, and lymph node metastasis 1/2. The signature genes, moreover, were highly expressed in A549 and PC-9 cells. In conclusion, the risk score signature could be used for prognostic estimation and as an independent risk factor for survival prediction in patients with LUAD.
越来越多的证据表明,可靠的基因特征可作为肺腺癌(LUAD)诊断的独立预后因素。在这里,我们试图确定用于预测 LUAD 患者生存的风险评分特征。在基因表达综合数据库(GEO)中,下载 GSE18842、GSE75037、GSE101929 和 GSE19188 的 mRNA 表达谱,以筛选差异表达基因(DEGs),用于建立蛋白质-蛋白质相互作用网络并进行聚类模块分析。应用单变量和多变量比例风险回归分析,基于 TCGA 数据集建立和验证基因特征。然后在 GEPIA、Oncomine 和 HPA 平台上验证特征基因。还通过 qRT-PCR 和 Western blot 在 HBE、A549 和 PC-9 细胞系中测量相应基因的表达水平。基于八个基因(,, , , , , 和 )建立了预后特征,该特征独立于其他临床因素。该风险模型在风险组之间提供了更好的区分能力,并且高风险评分的患者在 1、3 和 5 年随访时的生存率往往较差。该模型在年龄、性别、临床分期 III/IV、原发肿瘤 1/2 和淋巴结转移 1/2 的特定癌症队列中也具有更好的生存预测能力。此外,特征基因在 A549 和 PC-9 细胞中高表达。总之,风险评分特征可用于预后估计,并作为 LUAD 患者生存预测的独立风险因素。