Construction of a novel prognostic model for gastric cancer based on pharmacokinetics-related genes and comprehensive prognostic analysis.

Background: Absorption, distribution, metabolism, and excretion of drugs-related genes (ADMERGs), pivotal in cancer occurrence, development, and chemotherapy resistance, lack investigation in gastric cancer (GC). Thus, this study aims to build a prognostic model for gastric cancer utilizing ADMERGs. Methods: The GC-related datasets, including TCGA-GC, GSE62254, GSE163558 and GSE13911, as well as 298 ADMERGs, were retrieved in this study. Prognostic risk models associated with ADME were developed utilizing univariate Cox analysis, followed by additional refinement using the least absolute shrinkage and selection operator (LASSO). The entire pool of gastric cancer (GC) patient samples was partitioned into high and low-risk categories, delineated by the median value of their respective risk scores. Within these two distinct groups, we conducted enrichment analysis, immune infiltration, and prognostic evaluation of ADME-related prognostic genes to uncover their molecular mechanisms in GC. The construction of ceRNA regulatory networks was undertaken to analyse the prognostic gene regulatory mechanisms. We analyzed single-cell data in GC to investigate the mechanisms driving its onset and progression at the cellular level. Additionally, we validated the expression trends of prognostic genes in clinical samples using RT-qPCR. Results: A prognostic model for GC was established and validated, comprising five genes (UGT1A1, ADH4, ADH1B, CYP19A1, and GPX3). The levels of infiltration of 21 immune cells exhibited significant disparities between the two risk groups, such as central memory CD4 T cells, activated B cells, and mast cells. There was a notable positive correlation between the risk scores and mast cells and plasmacytoid dendritic cells. In the high-risk group, the TIDE scores were heightened. The single-cell dataset showed significant under-expression of ADH1B, ADH4, CYP19A1, and GPX3 in tumor samples. Finally, RT-qPCR showed that all the prognostic genes except for ADH4 were under-expressed in tumor tissues. Conclusion: We have developed and validated an innovative prognostic risk model for GC, revealing that elevated ADMERGs risk scores are indicative of unfavorable prognosis and diminished immunotherapy response. These findings furnish molecular evidence regarding the participation of ADMERGs in modulating the immune microenvironment and therapeutic responsiveness in GC.