| Background:Gastric adenocarcinoma(GC)is one of the five most common cancers and the third leading cause of cancer-related deaths,accounting for nearly 800,000 deaths globally.Treatment for gastric cancer typically involves surgery(D2 and laparoscopic resection);however,despite recent advancements,the results remain unsatisfactory.The 5-year survival rate of patients with advanced gastric cancer is still below 50%.Immune checkpoint inhibitors(ICIs)can help break the cycle of immune tolerance,allowing immune cells to respond more robustly to cancer and thwarting the immune escape strategies employed by cancer cells.Metabolic reprogramming in tumor cells is intricately linked to the level of nicotinamide adenine dinucleotide(NAD+)that powers various essential cellular processes in tumors.Previous research has demonstrated that targeting NAD+ synthesis in GC cells can successfully impede the generation of energy and metabolites.Objective:This study aims to explore the potential role of NAD+ metabolism-related genes(NMRGs)in gastric cancer,with the ultimate goal of helping to identify NAD+ metabolism-related treatments.Research method:1.Data acquisition and download: Complete gene expression data,clinical information,and mutation data of 375 GC samples and 32 normal gastric tissues were downloaded from the TCGA online database(https://portal.gdc.cancer.gov/).Additionally,the GSE84437 dataset from the Gene Expression Omnibus(GEO,https://www.ncbi.nlm.nih.gov/geo/database)online database was downloaded,containing433 GC sample sequencing data.The GSE163558 single-cell dataset included primary gastric cancer tissues from 6 patients and metastatic cancer tissues from 6 different organs or tissues(liver,peritoneum,ovary,lymph node),with a total of 96810 cells.Human gastric mucosal epithelial cells GES-1 and five gastric cancer cell lines(N87,SGC7901,MKN45,AGS,MGC803)were obtained from the Cell Culture Center of the Chinese Academy of Medical Sciences.2.Consensus Clustering and Gene Set Variation Analysis(GSVA): Based on the screened NMRGs,the "Consensus Cluster Plus" package was used to cluster and group gastric cancer samples from TCGA and GEO databases.c2.cp.kegg.v7.4 was obtained from the Molecular Signature Database(MSig DB,https://www.gsea-msigdb.org/gsea/index.jsp)to perform the GSVA.The differences in clinical information,survival curves,and signaling pathways of patients between different cluster samples were compared.3.Least absolute shrinkage and selection operator(LASSO)regression analysis: we employed LASSO Regression Analysis to construct a prognostic model based on NMRGs.Under this model,the associations between risk scores and tumor tissue immune cell infiltration,gene mutations,and tumor stem cell scores were explored.4.Single-cell sequencing data analysis: Perform quality control on gastric cancer single-cell data,remove low-quality cells,and verify the expression model of the marker gene in the cell for constructing the prognosis model.5.Using real-time quantitative PCR(qRT-PCR)technology,the marker genes for constructing the prognosis model were detected in human gastric mucosal epithelial cell GES-1 and five gastric cancer cell lines(N87,SGC7901,MKN45,AGS,MGC803).The expression levels in were compared.Result:1.We clustered 808 GC samples into 3 clusters based on 33 NMRGs.Survival analysis among clusters of different samples showed that GC patients from clusters with lower expression of NMRGs had better survival time(P=0.017).The enrichment of glycosaminoglycan biosynthesis of chondroitin sulfate,extracellular matrix receptor interaction,and focal adhesion signaling pathways in the group with high NMRGs expression was significantly greater than that in the group with low NMRGs expression(P<0.05).2.LASSO regression analysis developed a prognostic model comprising of SGCE、APOD and PPP1R14 A.The overall survival(OS)of GC patients in the low-risk group was superior to that in the high-risk group,and risk score,age,and N stage were found to be valuable for the prognosis of GC patients.The immune score and matrix score of patients in the low-risk group were lower than those in the high-risk group.Specifically,follicular helper T cells and CD4 memory activated T cells were negatively correlated to the expression levels of the three marker genes,while CD4 memory resting T cells,monocytes,and resting mast cells were positively correlated.Survival analysis demonstrated that lower expression of SGCE、APOD and PPP1R14 A was associated with better prognosis of patients.3.The correlation between risk score and GC mutation status as well as microsatellite instability was evaluated,revealing that the low-risk group had a higher mutation rate than the high-risk group.Additionally,among the top 20 driver mutation genes,TTN and TP53 had the highest mutation rates.The high-risk group had lower TMB scores than the low-risk group,and stemness scores based on m RNA expression(RNAs)were inversely correlated with risk scores.The risk scores of different microsatellite stability status groups were significantly different(P<0.05).The proportion of microsatellite instability-high(MSI-H)and microsatellite instability-low(MSI-L)in the low-risk subgroup was higher(42%)than in the high-risk subgroup(22%).4.Quantitative Real-Time PCR(qRT-PCR)verification of the expression levels of SGCE、APOD and PPP1R14 A.revealed that the expression levels of SGCE、APOD and PPP1R14 A.in GC cell lines were3-fold,7-fold,and 5-fold higher,respectively,than those of normal human gastric mucosal epithelial cells on average(P < 0.05).5.Analysis of gastric cancer single-cell data: SGCE 、 APOD and PPP1R14 A.are highly consistent with the expressions of CAFs classic markers in cells(CAFs markers used for identification are ACTA2,FAP,PDGFRA,PDGFRB,PDPN,THY1,and COL1A1).We quantified and scored transcriptome samples by CAFs markers to verify the relationship between SGCE、APOD and PPP1R14 A.and CAFs.The results show that the high-risk group samples have higher CAFs scores,and similarly,the correlation heatmap also shows that they have a very obvious positive correlation.Finally,the results of the survival curves demonstrated that gastric cancer patients with fewer Cancer-Associated Fibroblasts(CAFs)had a more favorable prognosis.Combined with genetic risk scores,it was found that patients with fewer CAFs and lower risk scores had the best prognosis.Conclusion:For the first time,we preliminarily revealed the association of NMRGs with the prognosis of GC patients,and identified genes SGCE 、 APOD and PPP1R14 A.that synergized with NMRGs.NAD metabolism may regulate CAFs through SGCE 、APOD and PPP1R14 A.,which collectively affect the prognosis,immune cell infiltration,and immunotherapy effects of GC patients,which may provide new insights into immune biomarkers and the underlying mechanisms of GC. |
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