Identification of seven-gene signature for prediction of lung squamous cell carcinoma
Identification of seven-gene signature for prediction of lung squamous cell carcinoma
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DOI：
10.2147/ott.s198998
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发表时间：
2019-01-01
影响因子：
4
通讯作者：
Qin, Baoli
中科院分区：
文献类型：
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作者：
Wang, Zhe;Wang, Zhongmiao;Qin, Baoli
背景与目的：肺鳞状细胞癌（Lung squamous cell carcinoma，LUSC）是肺癌的一种病理亚型，约占肺癌的30%。方法：从TCGA数据库中下载LUSC的RNA-Seq数据，筛选差异表达基因（p1）。通过单因素和多因素考克斯回归分析，我们发现了7个与乳腺癌相关的基因。然后，我们建立了一个风险评分分期系统来预测LUSC患者的预后。与其他临床参数相比，危险评分是一个独立的预后因素，对预后的预测有更好的表现。最后，进行GSEA分析，以确定富集途径显着。采用考克斯比例风险回归分析建立风险评分模型，应用ROC曲线检验风险评分模型的性能。结果：本研究建立了一个预测预后的模型，该模型包含7个基因：CSRNP 1、CLEC 18 B、MIR 27 A、AC130456.4、DEFA 6、ARL 14 EPL和ZFP 42。基于该模型，使用LUSC计算每例患者的风险评分（风险比[HR] =2.673，95% CI=1.871-3.525）。结果发现，风险评分可以独立区分LUSC患者预后的高风险组和低风险组。此外，该模型在测试数据集和整个数据集的ROC曲线进行了验证。最后，通过基因集富集分析（GSEA），我们发现主要的富集途径是DNA损伤刺激、DNA修复和DNA复制。结论：基于7个基因的风险评分可作为判断LUSC患者预后的一个独立的生物标志物。
Background and aim: Lung squamous cell carcinoma (LUSC), is a pathological subtype of lung cancer, accounting for 30% of the lung cancers. A reliable model was constructed, based on the whole gene expression profiles, to predict the prognosis of patients with LUSC.Methods: The RNA-Seq data of LUSC was downloaded from the TCGA database, and differentially expressed genes (p1) were screened out. By univariate and multivariate Cox regression analysis, we identified seven prognosis-related genes. Then, we established a risk score staging system to predict the prognosis of patients with LUSC. Compared with other clinical parameters, the risk score was an independent prognostic factor and had a better performance in predicting prognosis. Finally, GSEA analysis was carried out to determine the enrichment pathway significantly. The risk score models were established by Cox proportional hazard regression analysis; the ROC curve was applied to test the performance of risk score model. All the statistical analysis was accomplished by R packages.Results: In this study, a model was constructed to predict prognosis, which contains seven genes: CSRNP1, CLEC18B, MIR27A, AC130456.4, DEFA6, ARL14EPL, and ZFP42. Based on the model, the risk score of each patient was calculated with LUSC (hazard ratio [HR] =2.673, 95% CI=1.871-3.525). It was found that the risk score can distinguish high-risk and low-risk groups in prognosis of LUSC patients, independently. Furthermore, the model was validated by ROC curves in the testing dataset and the whole dataset. Lastly, by gene set enrichment analysis (GSEA), we showed the main enrichment pathways were DNA damage stimulus, DNA repair, and DNA replication. It was suggested that the risk score may provide a new and reliable method for prognosis prediction.Conclusion: The results of this study suggested that the risk score based on seven-genes could indicate a promising and independent prognostic biomarker for LUSC patients.