Zhu Ting, Ma Zhifeng, Wang Haiyong, Wei Desheng, Wang Bin, Zhang Chu, Fu Linhai, Li Zhupeng, Yu Guangmao
Department of Thoracic Surgery, Shaoxing People's Hospital, Shaoxing, China.
Front Cell Dev Biol. 2021 Mar 4;9:641960. doi: 10.3389/fcell.2021.641960. eCollection 2021.
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（ESCC）是最常见的癌症类型之一，在全球患者中导致相对较高的死亡率。在本研究中，从GEO数据库获取了ESCC患者的基因芯片数据，将所涉及的样本分为训练集和验证集。基于我们鉴定出的免疫相关差异长链非编码RNA（lncRNA），经过回归分析构建了一个基于八个lncRNA的预后风险特征。然后，使用生存曲线和受试者工作特征曲线在训练集和验证集中评估该模型的预测能力。此外，基于临床信息和基于模型的风险评分的单因素和多因素回归分析也证明了风险评分独立确定患者预后的能力。此外，基于CIBERSORT工具对肿瘤样本中的免疫浸润丰度进行评分，高风险组和低风险组之间存在显著差异。与免疫检查点（PD1、PDL1和CTLA4）的相关性分析表明，基于八个lncRNA特征的风险评分与PD1表达呈负相关，这表明八个lncRNA特征可能对ESCC的免疫治疗有影响。最后，对与八个lncRNA共表达的差异mRNA进行了GO注释，发现它们在免疫相关生物学功能中显著富集。这些结果表明，基于八个lncRNA特征的风险模型可作为ESCC预后独立的生物标志物，并可能在评估ESCC对免疫检查点阻断免疫治疗的反应中发挥作用。
