Department of Colorectal Surgery, Affiliated Tumor Hospital of Harbin Medical University, Harbin 150040, Heilongjiang Province, China.
J BUON. 2021 Jul-Aug;26(4):1252-1259.
To identify some key prognosis-related metabolic genes (PRMG) and establish a clinical prognosis model for colon adenocarcinoma (COAD) patients.
We used The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) to obtain gene expression profiles of COAD, and then identified differentially expressed prognostic-related metabolic genes through R language and Perl software, Through univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) Cox analysis to obtain target genes, established metabolic genes prognostic models and risk scores. Through Cox regression analysis, independent risk factors affecting the prognosis of COAD were analyzed, and receiver operating characteristics (ROC) curve analysis of independent prognostic factors was performed and a nomogram for predicting overall survival was constructed. We performed the consistency index (C-index) test and decision curve analysis (DCA) on the nomogram, and used gene set enrichment analysis (GSEA) to identify the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway of model genes. We selected PRMG based on the expression of metabolic genes, and used LASSO Cox regression to construct 16 metabolic gene models (SEPHS1, P4HA1, ENPP2, PTGDS, GPX3, CP, ASPA, POLR3A, PKM, POLR2D , XDH, EPHX2, ADH1B, HMGCL, GPD1L and MAOA).
The risk score generated from our model can well predict the survival prognosis of COAD. A nomogram based on the clinicopathological characteristics and risk scores of COAD can personally predict the overall survival rate of COAD patients.
The risk score based on the expression of 16 metabolic genes can effectively predict the prognosis of patients with COAD.
鉴定一些关键的与预后相关的代谢基因（PRMG），并建立用于结直肠腺癌（COAD）患者的临床预后模型。
我们使用癌症基因组图谱（TCGA）和基因表达综合数据库（GEO）获取 COAD 的基因表达谱，然后通过 R 语言和 Perl 软件鉴定差异表达的与预后相关的代谢基因，通过单因素 Cox 分析和最小绝对值收缩和选择算子（LASSO）Cox 分析获取目标基因，建立代谢基因预后模型和风险评分。通过 Cox 回归分析，分析影响 COAD 预后的独立危险因素，并对独立预后因素进行接收者操作特征（ROC）曲线分析，构建预测总生存期的列线图。我们对列线图进行一致性指数（C-index）检验和决策曲线分析（DCA），并使用基因集富集分析（GSEA）鉴定模型基因的京都基因与基因组百科全书（KEGG）通路。我们基于代谢基因的表达选择 PRMG，并使用 LASSO Cox 回归构建 16 个代谢基因模型（SEPHS1、P4HA1、ENPP2、PTGDS、GPX3、CP、ASPA、POLR3A、PKM、POLR2D、XDH、EPHX2、ADH1B、HMGCL、GPD1L 和 MAOA）。
我们的模型生成的风险评分可以很好地预测 COAD 的生存预后。基于 COAD 的临床病理特征和风险评分的列线图可以个性化预测 COAD 患者的总生存率。
基于 16 个代谢基因表达的风险评分可以有效地预测 COAD 患者的预后。
