Maolang Tian1, Jiangping Yang1, Jiaqi Han1, Jinlan He1, Wenjun Liao2. 1. Department of Head and Neck Oncology and Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China. 2. Department of Head and Neck Oncology and Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China. Electronic address: 2018324025294@stu.scu.edu.cn.
Abstract
BACKGROUND: New emergence of immunotherapy has significantly improved clinical outcome of melanoma patients with advanced and metastatic diseases. We aimed to develop a gene signature based on the expression of PD-1/PD-L1 signaling pathway genes to predict prognosis and immunotherapy response in melanoma patients. METHODS: Melanoma samples from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) were used as the training set and external validation sets respectively. Prognostic genes for overall survival (OS) were identified by univariate Cox regression analysis. Then a multi-gene risk signature was established with the Least Absolute Shrinkage and Selector Operation (LASSO) regression and multivariate Cox regression. The predictive and prognostic value of gene signature was evaluated by Kaplan Meier curve, Time-dependent receiver operating characteristic (ROC) curve, and area under curve (AUC). Gene set enrichment analysis (GSEA) was performed to investigate the discrepantly enriched biological processes between low-risk and high-risk group of melanoma patients. RESULTS: A seven-gene risk signature (BATF2, CTLA4, EGFR, HLA-DQB1, IKBKG, PIK3R2, PPP3CA) was constructed. The signature was an independent risk factor for OS (hazard ratio = 1.544, p < 0.001) and it could robustly predict OS in both training and validation sets. Besides, high risk scores indicated advanced clinical stage and no response to immunotherapy for melanoma patients. GSEA demonstrated that high risk score was intimately associated with immune response and immune regulation. In conclusion, the novel seven-gene signature could serve as a robust biomarker for prognosis and a potential indicator of immunotherapy response in melanoma.
BACKGROUND: New emergence of immunotherapy has significantly improved clinical outcome of melanomapatients with advanced and metastatic diseases. We aimed to develop a gene signature based on the expression of PD-1/PD-L1 signaling pathway genes to predict prognosis and immunotherapy response in melanomapatients. METHODS:Melanoma samples from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) were used as the training set and external validation sets respectively. Prognostic genes for overall survival (OS) were identified by univariate Cox regression analysis. Then a multi-gene risk signature was established with the Least Absolute Shrinkage and Selector Operation (LASSO) regression and multivariate Cox regression. The predictive and prognostic value of gene signature was evaluated by Kaplan Meier curve, Time-dependent receiver operating characteristic (ROC) curve, and area under curve (AUC). Gene set enrichment analysis (GSEA) was performed to investigate the discrepantly enriched biological processes between low-risk and high-risk group of melanomapatients. RESULTS: A seven-gene risk signature (BATF2, CTLA4, EGFR, HLA-DQB1, IKBKG, PIK3R2, PPP3CA) was constructed. The signature was an independent risk factor for OS (hazard ratio = 1.544, p < 0.001) and it could robustly predict OS in both training and validation sets. Besides, high risk scores indicated advanced clinical stage and no response to immunotherapy for melanomapatients. GSEA demonstrated that high risk score was intimately associated with immune response and immune regulation. In conclusion, the novel seven-gene signature could serve as a robust biomarker for prognosis and a potential indicator of immunotherapy response in melanoma.