Table_2_Immune-Related lncRNAs Pairs to Construct a Novel Signature for Predicting Prognosis in Gastric Cancer.XLS

BackgroundImmune-related long non-coding RNAs (irlncRNAs) appear valuable in predicting prognosis in patients with cancer. In this study, we used a fresh modeling algorithm to construct irlncRNAs signature and then assessed its predictive value for prognosis, tumor immune infiltration, and chemotherapy efficacy in gastric cancer (GC) patients.
Materials and MethodsThe raw transcriptome data were extracted from the Cancer Genome Atlas (TCGA). Patients were randomly divided into the training and testing cohort. irlncRNAs were identified through co-expression analysis, after which differentially expressed irlncRNA (DEirlncRNA) pairs were identified. Next, we developed a model to distinguish between high- or low-risk groups in GC patients through univariate and LASSO regression analyses. A ROC curve was used to verify this model. After subgrouping patients according to the median risk score, we investigated the connection between the risk score of GC and clinicopathological characteristics. Functional enrichment analysis was also performed.
ResultsWe find that the results indicate that immune-related lncRNA signaling has essential value in predicting prognosis, and it may be potential to measure the Efficacy for immunotherapy. This feature may be a guide to the selection of GC immunotherapy.
ConclusionOur data revealed that immune-related lncRNA signaling had essential value in predicting prognosis, and it may be potentially used to measure the efficacy for immunotherapy. This feature may also be used to guide the selection of GC immunotherapy.

背景
免疫相关长链非编码RNA（immune-related long non-coding RNAs，irlncRNAs）在癌症患者的预后预测中具有重要应用价值。本研究采用新型建模算法构建免疫相关长链非编码RNA特征，并评估其在胃癌（gastric cancer, GC）患者中的预后预测价值、肿瘤免疫浸润情况以及化疗疗效。

材料与方法
原始转录组数据从癌症基因组图谱（The Cancer Genome Atlas, TCGA）中提取。将患者随机划分为训练队列与测试队列。通过共表达分析筛选免疫相关长链非编码RNA，进而鉴定差异表达免疫相关长链非编码RNA（differentially expressed irlncRNA，DEirlncRNA）对。随后，借助单因素与套索（LASSO）回归分析构建模型，以区分胃癌患者的高、低风险组。采用受试者工作特征（ROC）曲线验证该模型的预测效能。根据风险评分的中位数对患者进行亚组分组后，分析胃癌患者风险评分与临床病理特征的关联，并开展功能富集分析。

结果
本研究结果显示，免疫相关长链非编码RNA特征在预后预测中具有重要价值，其或可用于评估免疫治疗疗效，该特征或可为胃癌免疫治疗的方案选择提供指导。

结论
本研究数据表明，免疫相关长链非编码RNA特征在预后预测中具有重要价值，其或可用于评估免疫治疗疗效，该特征亦可用于指导胃癌免疫治疗的方案选择。