Migrasome-Related Prognostic Genes in Gastric Cancer: A Transcriptomic and Immunotherapeutic Analysis.

Introduction: Gastric cancer (GC) remains one of the leading causes of cancer-related deaths worldwide, characterized by complex pathogenesis and poor prognosis. Migrasomes, as newly discovered organelles, play crucial roles in tumor microenvironment modulation and immune regulation. However, their specific mechanisms in GC remain largely unknown.

Methods: This study integrated GC transcriptomic data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases with 35 migrasome-related genes (MRGs) to identify differentially expressed genes through bioinformatics analysis. A prognostic model was constructed using least absolute shrinkage and selection operator (LASSO) and Cox regression, and subsequent analyses were conducted through gene set enrichment analysis (GSEA), immune infiltration assessment, and drug sensitivity evaluation. Key gene expressions were further verified in clinical samples via reverse transcription quantitative polymerase chain reaction (RT-qPCR).

Results: Eight migrasome-related prognostic genes were identified (BMP1, CPQ, PDGFD, TSPAN5, TSPAN7, TGFB2, WNT11, and LEFTY1). The developed risk-scoring model demonstrated predictive performance in both training and validation cohorts (area under the curve (AUC) > 0.6). Functional analysis revealed significant enrichment of these genes in key pathways, particularly the TGF-β signaling pathway. Immune profiling showed distinct microenvironment features in high-risk groups, along with differential sensitivity to specific chemotherapeutic agents (eg, BMS-754807). Experimental validation confirmed significant upregulation of BMP1 (p < 0.05), LEFTY1 (p < 0.05), and TGFB2 (p < 0.01), along with downregulation of TSPAN5 in GC tissues (p < 0.001).

Conclusion: This study reveals the prognostic value of eight genes related to migrators in GC. The established risk model provides novel molecular markers and potential therapeutic targets for personalized GC treatment. These findings offer critical insights for understanding GC pathogenesis and developing innovative treatment strategies.