Prognostic value of a novel glycolysis-related gene expression signature for gastrointestinal cancer in the Asian population.
Prognostic value of a novel glycolysis-related gene expression signature for gastrointestinal cancer in the Asian population.
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新型糖酵解相关基因表达特征对亚洲人群胃肠癌的预后价值
DOI：
10.1186/s12935-021-01857-4
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发表时间：
2021-03-04
影响因子：
5.8
通讯作者：
Wang C
中科院分区：
文献类型：
--
作者：
Xia R;Tang H;Shen J;Xu S;Liang Y;Zhang Y;Gong X;Min Y;Zhang D;Tao C;Wang S;Zhang Y;Yang J;Wang C
在全球范围内，胃肠道肿瘤是最常见的恶性肿瘤之一。然而，研究尚未建立与糖酵解相关的基因特征，可用于构建亚洲人群胃肠道癌症的准确预后模型。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胃肠道癌症的预后。首先，我们在癌症基因组图谱（TCGA）数据库（TCGA- lihc、TCGA- stad、TCGA- esca、TCGA- paad、TCGA- coad、TCGA- chol和TCGA- read）中评估了296名亚洲GI癌症患者的mRNA表达谱和相应的临床数据。研究了胃肠道肿瘤与正常组织之间mrna的差异表达。基因集富集分析（GSEA）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）曲线评价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胃肠道肿瘤预后的风险评分。此外，该风险评分在两个基因表达综合（GEO）数据集（GSE116174和GSE84433）和28对组织样本中得到验证。筛选鉴定差异表达糖酵解相关基因中的预后相关基因（NUP85、HAX1、GNPDA1、HDLBP、GPD1）。五基因表达标记用于将患者划分为高危组和低危组（p < 0.05），并显示出令人满意的总生存预后价值（OS, p = 6.383 × 10-6）。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，分层分析显示，五基因标记的预后价值独立于其他临床特征，可以明显区分胃肠道肿瘤组织和正常组织。最后，5种预后相关基因在临床组织样本中的表达水平与TCGA数据集的结果一致。该模型基于5个糖酵解相关基因（NUP85、HAX1、GNPDA1、HDLBP和GPD1），结合临床特点，能够独立预测亚洲胃肠道肿瘤患者的OS。在线版本包含补充材料，可在10.1186/s12935-021-01857-4获得。
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 × 10–6). 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. The online version contains supplementary material available at 10.1186/s12935-021-01857-4.
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影响因子：
5.2
作者：
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