Table_4_Immune-Related Long Non-coding RNA Signature and Clinical Nomogram to Evaluate Survival of Patients Suffering Esophageal Squamous Cell Carcinoma.docx

Esophageal squamous cell carcinoma (ESCC) turns out to be one of the most prevalent cancer types, leading to a relatively high mortality among worldwide sufferers. In this study, gene microarray data of ESCC patients were obtained from the GEO database, with the samples involved divided into a training set and a validation set. Based on the immune-related differential long non-coding RNAs (lncRNAs) we identified, a prognostic eight-lncRNA-based risk signature was constructed following regression analyses. Then, the predictive capacity of the model was evaluated in the training set and validation set using survival curves and receiver operation characteristic curves. In addition, univariate and multivariate regression analyses based on clinical information and the model-based risk score also demonstrated the ability of the risk score in independently determining the prognosis of patients. Besides, based on the CIBERSORT tool, the abundance of immune infiltrates in tumor samples was scored, and a significant difference was presented between the high- and low- risk groups. Correlation analysis with immune checkpoints (PD1, PDL1, and CTLA4) indicated that the eight-lncRNA signature–based risk score was negatively correlated with PD1 expression, suggesting that the eight-lncRNA signature may have an effect in immunotherapy for ESCC. Finally, GO annotation was performed for the differential mRNAs that were co-expressed with the eight lncRNAs, and it was uncovered that they were remarkably enriched in immune-related biological functions. These results suggested that the eight-lncRNA signature–based risk model could be employed as an independent biomarker for ESCC prognosis and might play a part in evaluating the response of ESCC to immunotherapy with immune checkpoint blockade.

食管鳞状细胞癌（Esophageal Squamous Cell Carcinoma, ESCC）是全球范围内高发的恶性肿瘤之一，患者整体死亡率处于较高水平。本研究从基因表达综合数据库（GEO database）获取了ESCC患者的基因微阵列数据集，将纳入研究的样本划分为训练集与验证集。研究团队首先鉴定得到免疫相关差异长链非编码RNA（long non-coding RNAs, lncRNAs），随后通过回归分析构建了基于8个lncRNAs的预后风险签名。接着，分别在训练集与验证集中借助生存曲线与受试者工作特征曲线（Receiver Operating Characteristic curve, ROC曲线）评估了该模型的预测效能。基于临床信息与模型风险评分的单因素及多因素回归分析进一步证实，该风险评分可独立预测患者的预后情况。此外，借助CIBERSORT工具对肿瘤样本中的免疫浸润丰度进行量化评分，结果显示高、低风险组间的免疫浸润水平存在显著差异。与免疫检查点（PD1、PD-L1、CTLA4）的相关性分析表明，基于8个lncRNAs签名的风险评分与PD1表达呈负相关，提示该8-lncRNA签名可能在ESCC免疫治疗中具备潜在应用价值。最后，对与这8个lncRNAs共表达的差异mRNA进行基因本体注释（GO annotation）分析，发现其显著富集于免疫相关生物学功能通路。上述研究结果证实，基于8个lncRNAs签名的风险模型可作为ESCC预后的独立生物标志物，或可用于评估ESCC患者对免疫检查点阻断疗法的响应情况。