Computational Prediction and Evaluation of Epitope-Based Peptides Derived from FZD7 and HNF4A as Risk Factors for Gastric Cancer Towards Vaccine Design

Introduction: Gastric cancer (GC) is a multifactorial disease influenced by both environmental and genetic factors. This study aims to identify potential vaccine candidates targeting two proteins—Frizzled Homolog 7 (FZD7) and Hepatocyte Nuclear Factor 4 Alpha (HNF4A) as risk factors in the onset and progression of GC, using a computational approach. Methods: Both proteins were subjected to epitope prediction based on consensus antigenicity scores and various physicochemical parameters. B-cell and T-cell epitopes were analyzed using the IEDB platform. Results: The FZD7 protein exhibits multiple potential CD4+ and CD8+ T-cell epitopes, along with a higher probability of B-cell epitope regions compared to the well-characterized antigen HNF4A. Among the identified epitopes, RQLKVPPYL from FZD7 demonstrated high population coverage. In contrast, the HNF4A-derived epitope EWAKYIPAF showed over 69% coverage in the Japanese population. The FZD7 epitope FMIKYLMTM exhibited a strong interaction in the TCR–pMHC complex. Promising MHC class II-binding peptides were also identified, including QSWRRFYHRLSHSSK from FZD7 and IKRLRSQVQVSLEDY from HNF4A. Conclusion: Comprehensive analysis revealed that FZD7 exhibits greater potential for experimental immunogenicity assessment compared to HNF4A; however, both proteins were selected for epitope prediction and further evaluation.