Table2_An Immune-Related Gene Signature Can Predict Clinical Outcomes and Immunotherapeutic Response in Oral Squamous Cell Carcinoma.XLSX

Objective: Immune landscape is a key feature that affects cancer progression, survival, and treatment response. Herein, this study sought to comprehensively characterize the immune-related genes (IRGs) in oral squamous cell carcinoma (OSCC) and conduct an immune-related risk score (IRS) model for prognosis and therapeutic response prediction.
Methods: Transcriptome profiles and follow-up data of OSCC cohorts were curated from TCGA, GSE41613, and IMvigor210 datasets. An IRS model was established through univariate Cox, Random Survival Forest, and multivariate Cox analyses. Prognostic significance was evaluated with Kaplan–Meier curves, ROC, uni- and multivariate Cox, and subgroup analyses. A nomogram was conducted and assessed with C-index, ROC, calibration curves, and decision curve analyses. Immune cell infiltration and immune response were estimated with ESTIMATE and ssGSEA methods.
Results: An IRS model was constructed for predicting the overall survival and disease-free survival of OSCC, containing MASP1, HBEGF, CCL22, CTSG, LBP, and PLAU. High-risk patients displayed undesirable prognosis, and the predictive efficacy of this model was more accurate than conventional clinicopathological indicators. Multivariate Cox analyses demonstrated that this model was an independent risk factor. The nomogram combining IRS, stage, and age possessed high clinical application values. The IRS was positively associated with a nonflamed tumor microenvironment. Moreover, this signature enabled to predict immunotherapeutic response and sensitivity to chemotherapeutic agents (methotrexate and paclitaxel).
Conclusion: Collectively, our study developed a robust IRS model with machine learning method to stratify OSCC patients into subgroups with distinct prognosis and benefits from immunotherapy, which might assist identify biomarkers and targets for immunotherapeutic schemes.

研究背景与目的：免疫景观是影响癌症进展、患者生存及治疗响应的关键特征。本研究旨在全面表征口腔鳞状细胞癌（oral squamous cell carcinoma, OSCC）中的免疫相关基因（immune-related genes, IRGs），并构建免疫相关风险评分（immune-related risk score, IRS）模型以用于预后及治疗响应预测。
方法：本研究从TCGA、GSE41613及IMvigor210数据集中共整理获取口腔鳞状细胞癌队列的转录组谱与随访数据。通过单变量Cox分析、随机生存森林（Random Survival Forest）及多变量Cox分析构建免疫相关风险评分模型。采用Kaplan–Meier曲线、ROC曲线、单变量及多变量Cox分析、亚组分析评估模型的预后价值；构建列线图（nomogram），并通过C指数（C-index）、ROC曲线、校准曲线及决策曲线分析对其进行评估。采用ESTIMATE与ssGSEA方法评估免疫细胞浸润情况与免疫应答水平。
结果：本研究构建了可预测口腔鳞状细胞癌总生存期与无病生存期的免疫相关风险评分模型，包含MASP1、HBEGF、CCL22、CTSG、LBP及PLAU六个基因。高风险组患者预后较差，且该模型的预测效能优于传统临床病理指标。多变量Cox分析表明，该模型为独立的预后风险因素。联合免疫相关风险评分、肿瘤分期与年龄构建的列线图具有较高的临床应用价值。免疫相关风险评分与非炎性肿瘤微环境（tumor microenvironment）呈正相关。此外，该基因特征可用于预测免疫治疗响应及化疗药物（甲氨蝶呤与紫杉醇）的敏感性。
结论：综上，本研究通过机器学习方法构建了稳健的免疫相关风险评分模型，可将口腔鳞状细胞癌患者划分为具有不同预后特征及免疫治疗获益潜力的亚组，有助于筛选免疫治疗方案的生物标志物与治疗靶点。