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.

中文翻译：

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一种新型的免疫检查点相关的七基因标记，可预测黑色素瘤的预后和免疫治疗反应。

背景

免疫疗法的新出现大大改善了患有晚期和转移性疾病的黑素瘤患者的临床结局。我们旨在基于PD-1 / PD-L1信号通路基因的表达来开发基因签名，以预测黑色素瘤患者的预后和免疫治疗反应。

方法

来自癌症基因组图谱（TCGA）数据库和基因表达综合库（GEO）的黑色素瘤样本分别用作训练集和外部验证集。通过单变量Cox回归分析确定了总生存期（OS）的预后基因。然后，建立了具有最小绝对收缩和选择器操作（LASSO）回归和多元Cox回归的多基因风险特征。通过Kaplan Meier曲线，时间依赖性受体工作特征（ROC）曲线和曲线下面积（AUC）评估基因签名的预测和预后价值。进行了基因集富集分析（GSEA），以研究低风险和高风险的黑色素瘤患者之间差异丰富的生物学过程。

结果

构建了七个基因的风险签名（BATF2，CTLA4，EGFR，HLA-DQB1，IKBKG，PIK3R2，PPP3CA）。签名是OS的独立危险因素（危险比= 1.544，p  <0.001），它可以在训练和验证组中强有力地预测OS。此外，高风险评分表明黑色素瘤患者的临床阶段已到，对免疫疗法无反应。GSEA证明高风险评分与免疫反应和免疫调节密切相关。总之，新的七基因签名可以作为黑色素瘤预后的可靠生物标志物和免疫疗法反应的潜在指标。