DataSheet_3_Identification and Validation of 17-lncRNA Related to Regulatory T Cell Heterogeneity as a Prognostic Signature for Head and Neck Squamous Cell Carcinoma.csv

Successful eradication of tumors by the immune system depends on generation of antigen-specific T cells that migrate to tumor sites and kill cancerous cells. However, presence of suppressive Treg populations inside tumor microenvironment hinders effector T cell function and decreases antitumor immunity. In this study we independently evaluated and confirmed prognostic signature of 17-Treg-related-lncRNA. Immune cell infiltration analysis using 17-lncRNA signature as a probe, accurately described Treg populations in tumor immune microenvironment. 17-lncRNA signature model predicted prognosis with excellent accuracy in all three cohorts: training cohort (AUC=0.82), testing cohort (AUC=0.61) and total cohort (AUC=0.72). The Kaplan-Meier analysis confirmed that the overall survival of patients in the low-risk group was significantly better than those in the high-risk group(P<0.001). CIBERSORT analysis confirmed that low risk group had higher infiltration of tumor killer CD8 T cells, memory activated CD4 T cells, follicular helper T cells and T cells regulatory (Tregs), and lower expression of M0 macrophages and Mast cells activated. These results indicate that the 17-lncRNA signature is a novel prognostic and support the use of lncRNA as a stratification tool to help guide the course of treatment and clinical decision making in patients at high risk of HNSCC.

免疫系统成功清除肿瘤，有赖于生成能够迁移至肿瘤部位并杀伤癌细胞的抗原特异性T细胞。然而，肿瘤微环境中抑制性调节性T细胞（Treg）群体的存在，会阻碍效应T细胞的功能并削弱抗肿瘤免疫能力。本研究独立评估并验证了17个与调节性T细胞相关的长链非编码RNA（lncRNA）预后特征。以该17个长链非编码RNA特征为探针开展的免疫细胞浸润分析，可精准刻画肿瘤免疫微环境中的调节性T细胞群体特征。该17长链非编码RNA特征模型在全部三个队列中均展现出优异的预后预测精度：训练队列（曲线下面积AUC=0.82）、测试队列（AUC=0.61）以及总队列（AUC=0.72）。卡普兰-迈耶（Kaplan-Meier）生存分析证实，低风险组患者的总生存期显著优于高风险组（P<0.001）。CIBERSORT分析证实，低风险组的肿瘤杀伤性CD8+T细胞、活化记忆性CD4+T细胞、滤泡辅助性T细胞以及调节性T细胞（Treg）的浸润水平更高，而M0型巨噬细胞与活化肥大细胞的表达水平更低。本研究结果表明，该17个长链非编码RNA特征是一种全新的预后标志物，可支持将长链非编码RNA作为分层工具，用于指导头颈部鳞状细胞癌（HNSCC）高风险患者的治疗方案制定与临床决策。