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

Abstract

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.