TRIM29 may facilitate progression of pancreatic ductal adenocarcinoma through immune remodeling.

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy characterized by late diagnosis, aggressive progression, and an immunosuppressive tumor microenvironment. Biomarkers with both diagnostic value and biological relevance remain urgently needed.

METHODS

Five Gene Expression Omnibus cohorts comprising 548 pancreatic samples were analyzed using differential expression analysis, weighted gene co-expression network analysis, and machine learning. Spatial and single-cell transcriptomics, immune infiltration estimates, and survival analyses complemented biomarker screening. Tripartite motif-containing 29 (TRIM29) was evaluated in 80 paired clinical specimens, knockdown and re-expression experiments, and subcutaneous and orthotopic KPC tumor models.

RESULTS

Machine learning identified eight hub genes, including S100P, SFN, TMPRSS4, GJB2, GABRP, TRIM29, SERPINB5, and LAMB3, which showed favorable diagnostic performance across cohorts. Spatial transcriptomics revealed region-specific enrichment of these genes in PDAC tissues. Among them, TRIM29 was preferentially expressed in malignant epithelial cells, markedly upregulated in PDAC tissues, and associated with poor prognosis, reduced CD8+ T-cell infiltration, and activation of PI3K-AKT-mTOR signaling. Functionally, TRIM29/Trim29 knockdown suppressed PDAC cell proliferation, migration, and invasion, reduced AKT and mTOR phosphorylation, and increased CXCL10 transcript levels and extracellular protein concentrations. TRIM29 re-expression restored AKT and mTOR phosphorylation and reversed the increase in extracellular CXCL10 concentrations. In vivo, Trim29 knockdown suppressed tumor growth and increased CD8+ T-cell infiltration in orthotopic tumors.

CONCLUSION

This study identifies TRIM29 as a machine learning-prioritized diagnostic biomarker and functional regulator of PDAC progression and immune remodeling, suggesting its potential value for biomarker-guided risk assessment and therapeutic exploration in PDAC.