Table1_A Novel Glycolysis-Related Long Noncoding RNA Signature for Predicting Overall Survival in Gastric Cancer.XLSX

Background: The aim of this study was to construct a glycolysis-related long noncoding RNA (lncRNA) signature to predict the prognosis of patients with gastric cancer (GC). Methods: Glycolysis-related genes were obtained from the Molecular Signatures Database (MSigDB), lncRNA expression profiles and clinical data of GC patients were obtained from The Cancer Genome Atlas database (TCGA). Furthermore, univariate Cox regression analysis, Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate Cox regression analysis were used to construct prognostic glycolysis-related lncRNA signature. The specificity and sensitivity of the signature was verified by receiver operating characteristic (ROC) curves. We constructed a nomogram to predict the 1-year, 3-year, and 5-year survival rates of GC patients. Besides, the relationship between immune infiltration and the risk score was analyzed in the high and low risk groups. Multi Experiment Matrix (MEM) was used to analyze glycolysis-related lncRNA target genes. R “limma” package was used to analyze the mRNA expression levels of the glycolysis-related lncRNA target genes in TCGA. Gene set enrichment analysis (GSEA) was employed to further explore the biological pathways in the high-risk group and the glycolysis-related lncRNA target gene. Results: A prognostic signature was conducted based on nine glycolysis-related lncRNAs, which are AL391152.1, AL590705.3, RHOXF1-AS1, CFAP61-AS1, LINC00412, AC005165.1, AC110995.1, AL355574.1 and SCAT1. The area under the ROC curve (AUC) values at 1-year, 3-year, and 5-year were 0.765, 0.828 and 0.707 in the training set, and 0.669, 740 and 0.807 in the testing set, respectively. In addition, the nomogram could efficaciously predict the 1-year, 3-year, and 5-year survival rates of the GC patients. Then, we discovered that GC patients with high-risk scores were more likely to respond to immunotherapy. GSEA revealed that the signature was mainly associated with the calcium signaling pathway, extracellular matrix (ECM) receptor interaction, and focal adhesion in high-risk group, also indicated that SBSPON is related to aminoacyl-tRNA biosynthesis, citrate cycle, fructose and mannose metabolism, pentose phosphate pathway and pyrimidine metabolism. Conclusion: Our study shows that the signature can predict the prognosis of GC and may provide new insights into immunotherapeutic strategies.研究背景：本研究旨在构建糖酵解相关长链非编码RNA（long noncoding RNA, lncRNA）特征模型，以预测胃癌（gastric cancer, GC）患者的预后。 研究方法：糖酵解相关基因从分子特征数据库（Molecular Signatures Database, MSigDB）获取，胃癌患者的lncRNA表达谱及临床数据源自癌症基因组图谱（The Cancer Genome Atlas, TCGA）。本研究采用单因素Cox回归分析、最小绝对收缩和选择算子（Least Absolute Shrinkage and Selection Operator, LASSO）及多因素Cox回归分析，构建预后相关糖酵解lncRNA特征模型；通过受试者工作特征（receiver operating characteristic, ROC）曲线验证该模型的特异性与敏感性。此外，本研究构建列线图以预测胃癌患者的1年、3年及5年生存率，并分析高低风险组中免疫浸润与风险评分的相关性。采用多实验矩阵（Multi Experiment Matrix, MEM）分析糖酵解相关lncRNA的靶基因；采用R语言"limma"包分析TCGA数据集中糖酵解相关lncRNA靶基因的mRNA表达水平；采用基因集富集分析（Gene Set Enrichment Analysis, GSEA）进一步探究高风险组的生物学通路及糖酵解相关lncRNA靶基因的功能。 研究结果：本研究以9个糖酵解相关lncRNA为基础构建胃癌预后特征模型，所涉及的lncRNA分别为AL391152.1、AL590705.3、RHOXF1-AS1、CFAP61-AS1、LINC00412、AC005165.1、AC110995.1、AL355574.1及SCAT1。训练集内1年、3年、5年的受试者工作特征曲线下面积（area under the ROC curve, AUC）分别为0.765、0.828及0.707，测试集对应AUC值分别为0.669、0.740及0.807（注：原文测试集3年AUC值仅标注为740，疑似笔误，已修正为0.740）。此外，该列线图可有效预测胃癌患者的1年、3年及5年生存率。进一步分析发现，高风险评分的胃癌患者更可能从免疫治疗中获益。GSEA结果显示，高风险组的特征模型主要与钙信号通路、细胞外基质（extracellular matrix, ECM）受体相互作用及黏着斑通路相关；同时发现SBSPON与氨酰-tRNA生物合成、三羧酸循环、果糖与甘露糖代谢、磷酸戊糖通路及嘧啶代谢密切相关。 研究结论：本研究表明，该糖酵解相关lncRNA特征模型可有效预测胃癌患者的预后，可为胃癌免疫治疗策略提供新的研究思路。