Affiliations: [a] Department of Oncology, Zhu Jiang Hospital, Southern Medical University, Guangzhou, Guangdong, China | [b] Department of Anesthesiology, Luo He Central Hospital, Henan, China
Correspondence:
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Corresponding author: Jian Zhang and Peng Luo, Department of Oncology, Zhu Jiang Hospital, Southern Medical University, 253 Gongye Avenue, Hai Zhu District, Guangzhou, Guangdong 510220, China. E-mails: zhangjian@i.smu.edu.cn and luopeng@smu.edu.cn.
Abstract: BACKGROUND:DNA methylation plays a vital role in modulating genomic function and warrants evaluation as a biomarker for the diagnosis and treatment of lung squamous cell carcinoma (LUSC). OBJECTIVE:In this study, we aimed to identify effective potential biomarkers for predicting prognosis and drug sensitivity in LUSC. METHODS:A univariate Cox proportional hazards regression analysis, a random survival forests-variable hunting (RSFVH) algorithm, and a multivariate Cox regression analysis were adopted to analyze the methylation profile of patients with LUSC included in public databases: The Cancer Genome Atlas (TCGA), and the Gene Expression Omnibus (GEO). RESULTS:A methylated region consisting of 3 sites (cg06675147, cg07064331, cg20429172) was selected. Patients were divided into a high-risk group and a low-risk group in the training dataset. High-risk patients had shorter overall survival (OS) (hazard ratio [HR]: 2.72, 95% confidence interval [CI]: 1.82–4.07, P< 0.001) compared with low-risk patients. The accuracy of the prognostic signature was validated in the test and validation cohorts (TCGA, n= 94; GSE56044, n= 23). Gene set variation analysis (GSVA) showed that activity in the cell cycle/mitotic, ERBB, and ERK/MAPK pathways was higher in the high-risk compared with the low-risk group, which may lead to differences in OS.Interestingly, we observed that patients in the high-risk group were more sensitive to gemcitabine and docetaxel than the low-risk group, which is consistent with results of the GSVA. CONCLUSION:We report novel methylation sites that could be used as powerful tools for predicting risk factors for poorer survival in patients with LUSC.
