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A novel immune checkpoint-related seven-gene signature for predicting prognosis and immunotherapy response in melanoma.

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.

journal_name

Int Immunopharmacol

journal_title

International immunopharmacology

authors

Tian M,Yang J,Han J,He J,Liao W

doi

10.1016/j.intimp.2020.106821

subject

Has Abstract

pub_date

2020-10-01 00:00:00

pages

106821

eissn

1567-5769

issn

1878-1705

pii

S1567-5769(20)31424-7

journal_volume

87

pub_type

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