Identification of seven-gene signature for prediction of lung squamous cell carcinoma

Zhe Wang; Zhongmiao Wang; Xing Niu; Jie Liu; Zhuning Wang; Lijie Chen; Baoli Qin

doi:10.2147/OTT.S198998

Identification of seven-gene signature for prediction of lung squamous cell carcinoma

Onco Targets Ther. 2019 Jul 24:12:5979-5988. doi: 10.2147/OTT.S198998. eCollection 2019.

Authors

Zhe Wang^#¹, Zhongmiao Wang^#¹, Xing Niu², Jie Liu³, Zhuning Wang², Lijie Chen⁴, Baoli Qin¹

Affiliations

¹ Department of Gastrointestinal Oncology, Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute, Shenyang 110042, Liaoning Province, People's Republic of China.
² Department of Second Clinical College, Shengjing Hospital affiliated to China Medical University, Shenyang 110004, Liaoning Province, People's Republic of China.
³ Science Experiment Center of China Medical University, China Medical University, Shenyang 110122, Liaoning Province, People's Republic of China.
⁴ Department of Third Clinical College, China Medical University, Shenyang 110122, Liaoning Province, People's Republic of China.

^# Contributed equally.

Abstract

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 (p<0.05, |log2fold change| >1) 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.

Keywords: Cox regression model; gene set enrichment analysis; lung squamous cell carcinoma; prognosis; risk score.