基于生物信息学筛选影响胃癌患者预后的糖酵解相关基因

doi:10.3969/j.issn.1004-583X.2024.01.003

摘要/Abstract

摘要：

目的通过利用生物信息学开发糖酵解相关基因以预测胃癌(gastric cancer, GC)患者预后。方法使用癌症基因组图谱数据库中GC患者信使核糖核酸表达谱数据, 通过进行基因集富集分析以鉴定GC组织和正常组织间显著差异的基因集。通过最小绝对收缩和选择算子回归分析构建糖酵解相关基因预测GC患者预后的模型,并使用 Kaplan-Meier 分析、受试者工作特征曲线、单因素及多因素 Cox 回归分析验证模型预测性能。采用基因集变异分析分析高低风险组间生物途径状态的差异。结果获得15个糖酵解相关基因(PFKFB2、UHRF1、ACYP1、CLDN9、STC1、EFNA3、NUP50、ADH4、ANGPTL4、PKP2、VCAN、HIF 1A、LHX9、ANKZF1、ALDH3A2)与GC患者预后相关。根据15个基因特征风险评分,通过Cox回归分析将患者分为高风险组和低风险组。这15个基因标记是GC患者预后的独立生物标志物,低风险评分的GC患者预后更好。结合基因标记和临床预后因素的列线图可有效预测总生存期及无疾病生存期。结论建立的15个糖酵解相关基因标记可作为预测GC患者预后的可靠工具,可能为GC提供潜在的糖酵解治疗靶点。

关键词: 胃肿瘤, 预后, 预测模型, 糖酵解

Abstract:

Objective To construct a glycolysis-related gene model for predicting the prognosis of gastric cancer (GC) patients based on bioinformatics. Methods The messenger RNA expression profiles of GC patients were analyzed in The Cancer Genome Atlas program, and gene sets with significant differences between GC tissues and normal tissues were verified using gene set enrichment analysis. A glycolysis-related genes model for predicting the prognosis of GC patients was constructed using least absolute shrinkage and selection operator regression analysis, and the predictive performance of the model was validated using Kaplan-Meier survival analysis, receiver operating characteristic curve, and univariate and multivariate Cox regression analysis. Gene set variation analysis was performed to analyze the differences in biological pathway states between high-risk and low-risk groups. Results Fourteen glycolysis-related genes (PFKFB2、UHRF1、ACYP1、CLDN9、STC1、EFNA3、NUP50、ADH4、ANGPTL4、PKP2、VCAN、HIF 1A、LHX9、ANKZF1、ALDH3A2) were identified as prognostic markers for GC patients. Based on a risk score derived from these 15 gene features using Cox regression analysis, patients were classified into high-risk and low-risk groups. These 15 gene markers were independent biomarkers for predicting the prognosis, and patients with a low-risk score had a better prognosis. The combination of gene markers and clinical prognostic factors in a Nomogram effectively predicted overall survival and disease-free survival. Conclusion The established panel of 15 glycolysis-related gene markers can serve as reliable tools for predicting the prognosis of GC patients and may provide potential targets for glycolysis-targeted therapy in GC.

Key words: stomach neoplasms, prognostic, predictive models, glycolysis

中图分类号:

R735.2

赵旭辉, 黄小敏, 达德转, 许焱, 崔晓东, 李红玲. 基于生物信息学筛选影响胃癌患者预后的糖酵解相关基因[J]. 临床荟萃, 2024, 39(1): 20-29.

Zhao Xuhui, Huang Xiaomin, Da Dezhuan, Xu Yan, Cui Xiaodong, Li Hongling. Screening of glycolysis-related genes for predicting the prognosis of patients with gastric cancer: Based on bioinformatics[J]. Clinical Focus, 2024, 39(1): 20-29.

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链接本文: https://huicui.hebmu.edu.cn/CN/10.3969/j.issn.1004-583X.2024.01.003

https://huicui.hebmu.edu.cn/CN/Y2024/V39/I1/20

图/表 10

图1 糖酵解相关差异基因与患者OS的单变量Cox回归分析

Fig.1 Univariate Cox regression analysis of glycolysis-related differential genes and OS of patients

表1 通过LASSO回归分析得到与GC患者预后相关的15个糖酵解相关基因

Tab.1 Screened 15 glycolysis-related genes for predicting the prognosis of GC patients using LASSO regression analysis

序号	基因	离散系数
1	$P F K F B 2$	-0.0962952004774155
2	$U H R F 1$	-0.129632577184322
3	$A C Y P 1$	-0.0519404248232357
4	$C L D N 9$	0.239072070967019
5	$S T C 1$	0.147337518442243
6	$E F N A 3$	-0.0836373085400017
7	$N U P 50$	-0.0370752619271387
8	$A D H 4$	0.121593689211912
9	$A N G P T L 4$	0.0135857127689336
10	$P K P 2$	-0.0113107594377815
11	$V C A N$	0.0872242032080347
12	$H I F 1 A$	0.114552783094288
13	$L H X 9$	0.891795946885758
14	$A N K Z F 1$	-0.0217432055166958
15	$A L D H 3 A 2$	-0.093175891304856

表1 通过LASSO回归分析得到与GC患者预后相关的15个糖酵解相关基因

Tab.1 Screened 15 glycolysis-related genes for predicting the prognosis of GC patients using LASSO regression analysis

序号	基因	离散系数
1	$P F K F B 2$	-0.0962952004774155
2	$U H R F 1$	-0.129632577184322
3	$A C Y P 1$	-0.0519404248232357
4	$C L D N 9$	0.239072070967019
5	$S T C 1$	0.147337518442243
6	$E F N A 3$	-0.0836373085400017
7	$N U P 50$	-0.0370752619271387
8	$A D H 4$	0.121593689211912
9	$A N G P T L 4$	0.0135857127689336
10	$P K P 2$	-0.0113107594377815
11	$V C A N$	0.0872242032080347
12	$H I F 1 A$	0.114552783094288
13	$L H X 9$	0.891795946885758
14	$A N K Z F 1$	-0.0217432055166958
15	$A L D H 3 A 2$	-0.093175891304856

图2 用于构建最终预测模型的 LASSO 回归分析 a.选择LASSO模型中的最优参数(λ);使用最小标准在最佳值绘制垂直虚线; b. LASSO 分布的部分似然偏差

Fig. 2 LASSO regression analysis for construction of the final prediction model a.Determine the optimal parameter(λ) for the LASSO model; plot a vertical dashed line at the optimal value using the minimum criterion; b.Partial likelihood deviance of the LASSO distribution

图3 高风险组和低风险组的OS和PFS

Fig.3 OS and FPS of patients in the high- and low-risk groups

图4 1年、3年和5年OS的ROC曲线

Fig.4 ROC curves of 1-year, 3-year, and 5-year OS

图5 基于糖酵解和临床因素的列线图 a.包含年龄和风险评分的列线图,以预测1年、3年和5年的OS; b.列线图的校准图; c.风险、列线图和其他临床特征的ROC曲线

Fig.5 Nomogram considering glycolysis and clinical factors a.Nomogram illustrating age and risk score for predicting 1-year, 3-year, and 5-year OS; b.Colibration plot of nomogram; c.ROC curves of risk, nomogram and other clinical features

图6 临床特征的单因素、多因素分析及ROC曲线 a.单变量分析; b.多变量分析; c.ROC曲线

Fig.6 Univariate and multivariate analysis and ROC cure for clinical characteristics a.Univariate analysis; b.Multivariate analysis; c.ROC curve

图7 基于糖酵解相关基因的临床相关性分析 a.年龄; b.性别; c.分级; d.分期; e-g.TNM分期

Fig.7 Correlation analysis based on glycolysis-related genes a.Age; b.Gender; c.Grade; d.staging; e-g.TNM staging

图8 箱线图分析 a.两组免疫细胞浸润分布; b.免疫相关功能

Fig.8 Box plot a.Distribution of immune cell infiltration; b.Immune-related function

图9 热图注:红色表示激活途径,蓝色表示抑制途径

Fig.9 Heat map Note: red representing activation pathways and blue representing inhibition pathways

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