Construction of a prognostic model for gastric cancer patients based on NAD+ metabolism-related genes and tumor microenvironment analysis

Background: Nicotinamide adenine dinucleotide (NAD+) metabolism is important in the regulation of tumor immune escape. This study endeavored to develop a NAD + metabolism-related signature in gastric cancer (GC), which could provide a theoretical foundation for prognosis and therapy of GC patients. Methods First, differentially expressed genes (DEGs) between GC and paraneoplastic tissues were intersected with NAD + metabolism-related genes (NMRGs) to obtain differentially expressed NMRGs (DE NMRGs). Then, based on the transcript levels of NMRGs, GC patients were classified into high and low scoring groups using the Gene set variation analysis (GSVA) algorithm. Next, the DEGs between the high and low scoring groups were intersected with DEGs between GC and paraneoplastic tissues to obtain the GC-NM DEGs. Additionally, univariate Cox analysis and Least absolute shrinkage and selection operator (LASSO) regression analysis of GC-NM DEGs were performed to obtain prognostic biomarkers, which were used to construct a risk model. In addition, independent prognostic factors were obtained by Cox analysis based on risk scores and clinicopathological factors. Gene set enrichment analysis (GSEA) enrichment analysis and immune infiltration analysis were performed for the high- and low-risk groups. Finally, the mRNA expression of prognostic related genes was verified by experiment. Results 10 DE NMRGs were obtained and they were involved in the biological process of NAD biosynthetic process, nicotinamide nucleotide, and biosynthetic process. Further 7 biomarkers, including DNAJB13, CST2, THPO, CIDEA, ONECUT1, UPK1B, and SNCG, were obtained through univariate Cox and LASSO analyses of 1001 GC-NM DEGs. In addition, risk score and gender were demonstrated as credible independent prognostic factors for GC. Moreover, GSEA showed that the high-risk group was associated with bile secretion, intrinsic component of synaptic membrane and other pathways, while the low-risk group was associated with CMG complex. In addition, T cells, B cells, and natural killer cells were positively correlated with risk scores, and plasmacytoid dendritic cells were negatively correlated with risk scores. By QRT-PCR, the expression of prognostic genes in GC tissues was significantly up-regulated compared with paraneoplastic tissues. Conclusion This study established a NAD + metabolism-related signature based on DNAJB13, CST2, THPO, CIDEA, ONECUT1, UPK1B, and SNCG, which is of great significance in developing prognostic molecular biomarkers, clinical prognosis prediction, and treatment strategy decision for GC patients.