School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, United States.
Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, China.
Front Immunol. 2021 May 10;12:659444. doi: 10.3389/fimmu.2021.659444. eCollection 2021.
Immunotherapy shows efficacy in only a subset of melanoma patients. Here, we intended to construct a risk score model to predict melanoma patients' sensitivity to immunotherapy.
Integration analyses were performed on melanoma patients from high-dimensional public datasets. The CD8+ T cell infiltration related genes (TIRGs) were selected TIMER and CIBERSORT algorithm. LASSO Cox regression was performed to screen for the crucial TIRGs. Single sample gene set enrichment analysis (ssGSEA) and ESTIMATE algorithm were used to evaluate the immune activity. The prognostic value of the risk score was determined by univariate and multivariate Cox regression analysis.
184 candidate TIRGs were identified in melanoma patients. Based on the candidate TIRGs, melanoma patients were classified into three clusters which were characterized by different immune activity. Six signature genes were further screened out of 184 TIRGs and a representative risk score for patient survival was constructed based on these six signature genes. The risk score served as an indicator for the level of CD8+ T cell infiltration and acted as an independent prognostic factor for the survival of melanoma patients. By using the risk score, we achieved a good predicting result for the response of cancer patients to immunotherapy. Moreover, pan-cancer analysis revealed the risk score could be used in a wide range of non-hematologic tumors.
Our results showed the potential of using signature gene-based risk score as an indicator to predict melanoma patients' sensitivity to immunotherapy.
免疫疗法仅在一小部分黑色素瘤患者中显示出疗效。在这里，我们旨在构建一个风险评分模型，以预测黑色素瘤患者对免疫疗法的敏感性。
对来自高维公共数据集的黑色素瘤患者进行整合分析。使用 TIMER 和 CIBERSORT 算法选择 CD8+T 细胞浸润相关基因（TIRGs）。使用 LASSO Cox 回归筛选关键 TIRGs。使用单样本基因集富集分析（ssGSEA）和 ESTIMATE 算法评估免疫活性。通过单因素和多因素 Cox 回归分析确定风险评分的预后价值。
在黑色素瘤患者中鉴定出 184 个候选 TIRGs。基于候选 TIRGs，将黑色素瘤患者分为三个聚类，其特征是不同的免疫活性。从 184 个 TIRGs 中进一步筛选出 6 个特征基因，并基于这些 6 个特征基因构建了一个代表患者生存的代表性风险评分。风险评分作为 CD8+T 细胞浸润水平的指标，并作为黑色素瘤患者生存的独立预后因素。通过使用风险评分，我们实现了对癌症患者对免疫疗法反应的良好预测结果。此外，泛癌分析表明，该风险评分可用于广泛的非血液病肿瘤。
我们的结果表明，使用基于特征基因的风险评分作为预测黑色素瘤患者对免疫疗法敏感性的指标具有潜力。
