Abstract
BACKGROUND: Gastric cancer (GC) remains a lethal malignancy with limited prognostic biomarkers. Dysregulated polyamine metabolism promotes tumor progression and immune evasion, yet its clinical implications in GC are poorly characterized. METHODS: We conducted an integrative analysis using bulk RNA-seq and single-cell RNA-seq data to investigate the prognostic significance of polyamine metabolism-related genes (PMRGs) in GC. A total of 59 PMRGs were curated and used to score cells via AUCell. High- and low-scoring cells were subjected to differential gene expression, enrichment, and pseudotime trajectory analyses. Prognostic modeling was performed using 10 machine learning algorithms across multiple combinations, followed by validation and nomogram construction. Immune infiltration, immune checkpoint expression, cell-cell communication, and immunotherapy response were evaluated. Drug sensitivity and tumor mutational burden (TMB) were analyzed using public pharmacogenomic datasets. RESULTS: Single-cell analysis identified PMRGs-driven heterogeneity across 11 cell types, with fibroblasts and macrophages showing enhanced signaling in high-risk populations. A 13-gene signature was constructed using StepCox and elastic net, achieving robust prognostic performance (Train dataset AUCs: 0.67-0.70; Validation dataset AUCs: 0.64-0.67). High-risk patients exhibited enriched stromal-immune interactions, elevated immune infiltration, higher Tumor Immune Dysfunction and Exclusion (TIDE) scores, and poorer immunotherapy response. Low-risk patients had higher TMB and sensitivity to 5-Fluorouracil, Docetaxel, Doxorubicin and Paclitaxel. CONCLUSION: Polyamine metabolism shapes both cellular heterogeneity and the immune microenvironment in gastric cancer. Our integrated model may provide potential guidance for prognostic stratification and therapeutic decision-making in clinical oncology.