Screening of glycolysis-related genes for predicting the prognosis of patients with gastric cancer: Based on bioinformatics

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

Abstract

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

CLC Number:

R735.2

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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URL: https://huicui.hebmu.edu.cn/EN/10.3969/j.issn.1004-583X.2024.01.003

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

Figures/Tables 10

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

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

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

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

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

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

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

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

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

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

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

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