| Background: Gastric cancer is one of the most common digestive tract tumors and ranks first among digestive system malignancies.More than 1 million people are diagnosed with gastric cancer each year.The occurrence of gastric cancer is closely related to environmental factors and genetic factors and closely related to tumor metabolism,especially glycolysis.Glycolysis provides the energy needed for the growth of gastric cancer cells and accelerates the progression of gastric cancer.At the same time,lactate,the product of glycolysis,affects the tumor microenvironment to a certain extent and participates in various signaling pathways.The role of glycolysis in tumors is of greater interest in the context of tumor microenvironment reprogramming and immunotherapy.As most studies have focused on a single glycolysis-related gene,the overall tumor microenvironment cell infiltration signature mediated by multiple glycolysis-related genes has not been fully understood.Therefore,this study intends to construct a glycolysis-related prognostic risk model that can evaluate the prognosis of gastric cancer patients,identify potential therapeutic targets for gastric cancer,and provide new ideas for the diagnosis and treatment of gastric cancer.Methods:1.Download and extract the transcriptome data and clinical data of gastric adenocarcinoma patients from The Cancer Genome Atlas(TCGA)database.2.The GSE84437 dataset and GSE13763 dataset and the corresponding platform files GPL6947 and GPL570 can be downloaded from the Gene Expression Omnibus(GEO).3.Five datasets related to glycolysis were obtained from the Molecular Signatures Database(MSig DB).4.Identify all differentially expressed genes using the Wilcoxon test,and extract the differential expression matrix of glycolysis-related genes(GRGs)from them.5.Apply consensus clustering to classify gastric cancer patients according to glycolysisrelated genes.6.Univariate Cox regression analysis was used to identify glycolysis-related genes associated with prognosis;LASSO regression analysis was used to construct a prognostic risk model and calculated the risk coefficients of genes involved in model construction.7.Use Kaplan-Meier survival analysis and ROC curve(Receiver Operating Characteristic curve)to verify the accuracy and reliability of the model.8.Use correlation analysis to investigate the association between risk scores and clinicopathological characteristics,the tumor microenvironment score(TME scores),the degree of immune cell infiltration,tumor mutation burden(TMB),the expression level of immune checkpoints,and clinical immunotherapy efficacy.9.Knockdown the target gene by transfecting lentivirus in a gastric cancer cell line,and use Real-time PCR technology(q PCR)to verify the interference efficiency.10.Identify the glycolytic capacity of gastric cancer cells by Glucose Uptake Assay Kit(glucose consumption level),Picoprobe L-Lactate kit(lactate production)and Glycolysis Assay [Extracellular acidification] kit(extracellular acidification rate).11.Apply MTT assay to study whether the expression level of the target gene affects the proliferation ability of gastric cancer cells.12.Apply scratch assay to study whether the expression level of the target gene affects the migration ability of gastric cancer cells.13.Transwell invasion assay was used to study whether the expression levels of target genes affect the invasion ability of gastric cancer cells.14.The nude mouse xenograft models was used to study whether the expression level of the target gene affects the growth of the transplanted tumor.Results: According to the expression of glycolysis-related genes(GRGs)in the samples,the patients were divided into Cluster A and Cluster B by the method of consistent clustering.The prognosis of patients in Cluster B was better than that of patients in Cluster A.There were significant differences in tumor microenvironment scores(Estimate Score,Immune Score,Stromal Score,and Tumor Purity)and the degree of immune cell infiltration between patients in Cluster A and Cluster B.Univariate Cox regression analysis was used to identify 10 glycolysis-related genes associated with prognosis.LASSO regression analysis was used to construct a prognostic risk model based on 6 GRGs(CXCR4,PLOD2,SLC35A2,ME1,SRD5A3 and NUP50),in which the expression level of CXCR4 was most significantly correlated with patient prognosis.Patients in the high-risk group had shorter overall survival(OS).There was a significant difference in the tumor microenvironment score,clinicopathological characteristics,the TME scores,the degree of immune cell infiltration,TMB,the expression level of immune checkpoints,and clinical immunotherapy efficacy.between the high-risk group and the low-risk group.There is a correlation between the risk score calculated according to the prognostic model and the degree of immune cell infiltration,tumor mutation burden,the expression level of immune checkpoints,and clinical immunotherapy efficacy.Knockdown of CXCR4 in gastric cancer cell line BGC-823 can significantly facilitate the proliferation,migration,invasion,and glycolytic capacity of gastric cancer cells.Conclusion: This study found that the expression level of GRGs is closely related to the prognosis of gastric cancer patients and the tumor microenvironment.By constructing a prognostic model based on the expression level of GRGs,gastric cancer patients with different prognoses can be screened,which may be helpful for the evaluation of the prognosis of gastric cancer patients and the formulation of clinical treatment plans.Importantly,CXCR4,as a glycolysis-related gene,maybe a potential target for gastric cancer therapy. |
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