Background. Cutaneous melanoma is defined as one of the most aggressive skin tumors in the world. An increasing body of evidence suggested an indispensable association between immune-associated gene (IAG) signature and melanoma. This article is aimed at formulating an IAG signature to estimate prognosis of melanoma. Methods. 434 melanoma patients were extracted from The Cancer Genome Atlas (TCGA) database, and 1811 IAGs were downloaded from the ImmPort database in our retrospective study. The Cox regression analysis and LASSO regression analysis were utilized to establish a prognostic IAG signature. The Kaplan-Meier (KM) survival analysis was performed, and the time-dependent receiver operating characteristic curve (ROC) analysis was further applied to assess the predictive value. Besides, the propensity score algorithm was utilized to balance the confounding clinical factors between the high- and low-risk groups. Results. A total of six prognostic IAGs comprising of INHA, NDRG1, IFITM1, LHB, GBP2, and CCL8 were eventually filtered out. According to the KM survival analysis, the results displayed a shorter overall survival (OS) in the high-risk group compared to the low-risk group. In the multivariate Cox model, the gene signature was testified as a remarkable prognostic factor (, ). Additionally, the ROC curve analyses were performed which demonstrated our IAG signature was superior to four known biomarkers mentioned in the study. Moreover, the IAG signature was significantly related to immunotherapy-related biomarkers. Conclusion. Our study demonstrated that the six IAG signature played a critical role in the prognosis and immunotherapy of melanoma, which might help clinicians predict patients’ survival and provide individualized treatment.

中文翻译：

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皮肤黑色素瘤中强大的免疫相关基因特征的鉴定和验证


背景。皮肤黑色素瘤被定义为世界上最具侵袭性的皮肤肿瘤之一。越来越多的证据表明免疫相关基因 (IAG) 特征与黑色素瘤之间存在着不可或缺的关联。本文旨在制定 IAG 签名来估计黑色素瘤的预后。方法。在我们的回顾性研究中，从癌症基因组图谱 (TCGA) 数据库中提取了 434 名黑色素瘤患者，并从 ImmPort 数据库中下载了 1811 个 IAG。利用 Cox 回归分析和 LASSO 回归分析来建立预后 IAG 特征。进行Kaplan-Meier（KM）生存分析，并进一步应用时间依赖性受试者工作特征曲线（ROC）分析来评估预测价值。此外，利用倾向评分算法来平衡高风险组和低风险组之间的混杂临床因素。结果。最终筛选出总共 6 个预后 IAG，包括 INHA、NDRG1、IFITM1、LHB、GBP2 和 CCL8。根据KM生存分析，结果显示，与低风险组相比，高风险组的总生存期（OS）较短。在多变量 Cox 模型中，基因特征被证明是一个显着的预后因素（ , ）。此外，还进行了 ROC 曲线分析，表明我们的 IAG 特征优于研究中提到的四种已知生物标志物。此外，IAG 特征与免疫治疗相关的生物标志物显着相关。结论。我们的研究表明，六个 IAG 特征在黑色素瘤的预后和免疫治疗中发挥着关键作用，这可能有助于临床医生预测患者的生存情况并提供个体化治疗。