Construction of a Neoantigen Prognostic Model for Gastric Adenocarcinoma Based on Multi-Omics Data Mining and the Design of mRNA Vaccines and Targeted Drugs.

This study systematically explored immune targets in gastric adenocarcinoma (GAC) suitable for mRNA vaccine development. Based on multi-omics data from public databases, we first screened a set of potential tumor-associated antigen genes. Subsequently, using ten machine learning algorithms, we constructed 101 prognostic models and, through optimization and comparison, selected the Random Survival Forest (RSF) method to establish a clinical prognostic model for GAC consisting of seven genes (TYMP, IFGN, ITGAX, GBP5, GBP4, STAT1, CD84). At both the genetic and protein levels, these genes were closely associated with the antigen presentation process, suggesting the potential functional role of this model in antigen presentation. Further analysis of the immune infiltration characteristics in GAC preliminarily revealed its possible immune evasion mechanisms. Building on this, we designed candidate mRNA vaccine templates for GAC using the mRNAdesigner platform. Additionally, this study investigated the potential roles of the above seven genes in GAC progression and screened small-molecule compounds targeting these genes. Molecular dynamics simulations (MD) were performed to verify the binding stability between these compounds and their corresponding proteins. This study comprehensively simulated the tumor microenvironment (TME) and antigen presentation process in GAC, evaluated the clinical translation potential of the neoantigen prognostic model and its predictive value for immunotherapy, and provided a preliminary design scheme for an mRNA vaccine against GAC. The findings offer new evidence for identifying immune therapy targets in GAC and are expected to advance the development of immunotherapy strategies for GAC.