A prognostic model based on platelet-related genes accurately predicts survival in patients with gastric cancer

Background Platelets contribute to gastric cancer (GC) progression and metastasis. In this study, we aimed to construct a prognostic model based on platelet-related genes (PRGs) to predict outcomes in GC. Methods RNA sequencing data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were analyzed to identify differentially expressed genes, from which PRGs were selected as candidates. A prognostic model was built using machine learning. Immune infiltration was evaluated with CIBERSORT, and Spearman correlation was used to assess the relationships between prognostic PRGs and immune cells. Single-cell transcriptomics was used to explore cellular expression patterns, and molecular regulatory networks were constructed. The expression of candidate genes was validated in cell lines by reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Results A total of 8,387 differentially expressed genes, including 123 PRGs, were identified. An eight-gene prognostic model (THPO, MMRN1, EGF, SPARC, GNAI1, CD36, SERPINE1, and ADAMTS18) was established. Distinct immune infiltration patterns were observed between risk groups, with differences in M2 macrophages, monocytes, neutrophils, activated memory CD4+ T cells, regulatory T cells, and follicular helper T cells. MMRN1 expression correlated positively with monocytes ( r = 0.37), whereas GNAI1 expression was negatively associated with activated memory CD4+ T cells ( r = − 0.30). Single-cell analysis showed that prognostic genes were mainly expressed in epithelial cells and fibroblasts. Two regulatory axes, SPARC–hsa-miR-494-3p–NEAT1 and GNAI1–hsa-miR-4262–NEAT1, were implicated in GC progression. RT-qPCR confirmed downregulation of CD36 and upregulation of SERPINE1 in GC tissues. Conclusion This platelet-related gene model predicts survival and immune infiltration in GC and may support the development of personalized therapies.