Xia Rong, Tang Hua, Shen Jiemiao, Xu Shuyu, Liang Yinyin, Zhang Yuxin, Gong Xing, Min Yue, Zhang Di, Tao Chenzhe, Wang Shoulin, Zhang Yi, Yang Jinyou, Wang Chao
Key Lab of Modern Toxicology of Ministry of Education, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, People's Republic of China.
State Key Lab of Reproductive Medicine, Institute of Toxicology, Nanjing Medical University, 101 Longmian Avenue, Nanjing, 211166, People's Republic of China.
Cancer Cell Int. 2021 Mar 4;21(1):154. doi: 10.1186/s12935-021-01857-4.
Globally, gastrointestinal (GI) cancer is one of the most prevalent malignant tumors. However, studies have not established glycolysis-related gene signatures that can be used to construct accurate prognostic models for GI cancers in the Asian population. Herein, we aimed at establishing a novel glycolysis-related gene expression signature to predict the prognosis of GI cancers.
First, we evaluated the mRNA expression profiles and the corresponding clinical data of 296 Asian GI cancer patients in The Cancer Genome Atlas (TCGA) database (TCGA-LIHC, TCGA-STAD, TCGA-ESCA, TCGA-PAAD, TCGA-COAD, TCGA-CHOL and TCGA-READ). Differentially expressed mRNAs between GI tumors and normal tissues were investigated. Gene Set Enrichment Analysis (GSEA) was performed to identify glycolysis-related genes. Then, univariate, LASSO regression and multivariate Cox regression analyses were performed to establish a key prognostic glycolysis-related gene expression signature. The Kaplan-Meier and receiver operating characteristic (ROC) curves were used to evaluate the efficiency and accuracy of survival prediction. Finally, a risk score to predict the prognosis of GI cancers was calculated and validated using the TCGA data sets. Furthermore, this risk score was verified in two Gene Expression Omnibus (GEO) data sets (GSE116174 and GSE84433) and in 28 pairs of tissue samples.
Prognosis-related genes (NUP85, HAX1, GNPDA1, HDLBP and GPD1) among the differentially expressed glycolysis-related genes were screened and identified. The five-gene expression signature was used to assign patients into high- and low-risk groups (p < 0.05) and it showed a satisfactory prognostic value for overall survival (OS, p = 6.383 × 10). The ROC curve analysis revealed that this model has a high sensitivity and specificity (0.757 at 5 years). Besides, stratification analysis showed that the prognostic value of the five-gene signature was independent of other clinical characteristics, and it could markedly discriminate between GI tumor tissues and normal tissues. Finally, the expression levels of the five prognosis-related genes in the clinical tissue samples were consistent with the results from the TCGA data sets.
Based on the five glycolysis-related genes (NUP85, HAX1, GNPDA1, HDLBP and GPD1), and in combination with clinical characteristics, this model can independently predict the OS of GI cancers in Asian patients.
在全球范围内，胃肠道（GI）癌是最常见的恶性肿瘤之一。然而，尚未有研究建立可用于构建亚洲人群GI癌准确预后模型的糖酵解相关基因特征。在此，我们旨在建立一种新的糖酵解相关基因表达特征来预测GI癌的预后。
首先，我们评估了癌症基因组图谱（TCGA）数据库（TCGA-LIHC、TCGA-STAD、TCGA-ESCA、TCGA-PAAD、TCGA-COAD、TCGA-CHOL和TCGA-READ）中296例亚洲GI癌患者的mRNA表达谱及相应临床数据。研究了GI肿瘤与正常组织之间差异表达的mRNA。进行基因集富集分析（GSEA）以鉴定糖酵解相关基因。然后，进行单因素、LASSO回归和多因素Cox回归分析以建立关键的预后糖酵解相关基因表达特征。采用Kaplan-Meier曲线和受试者工作特征（ROC）曲线评估生存预测的效率和准确性。最后，使用TCGA数据集计算并验证预测GI癌预后的风险评分。此外，在两个基因表达综合数据库（GEO）数据集（GSE116174和GSE84433）以及28对组织样本中验证了该风险评分。
筛选并鉴定了差异表达的糖酵解相关基因中与预后相关的基因（NUP85、HAX1、GNPDA1、HDLBP和GPD1）。利用这五个基因的表达特征将患者分为高风险组和低风险组（p < 0.05），其对总生存期（OS，p = 6.383×10）显示出令人满意的预后价值。ROC曲线分析显示该模型具有较高的敏感性和特异性（5年时为0.757）。此外，分层分析表明这五个基因特征的预后价值独立于其他临床特征，并且能够显著区分GI肿瘤组织和正常组织。最后，临床组织样本中五个预后相关基因的表达水平与TCGA数据集的结果一致。
基于五个糖酵解相关基因（NUP85、HAX1、GNPDA1、HDLBP和GPD1），并结合临床特征，该模型能够独立预测亚洲患者GI癌的总生存期。
