CELL INTELLIGENCE · 肿瘤细胞治疗研究

To develop a fatty acid metabolism-based deep learning model for predicting biochemical recurrence (BCR) in prostate cancer (PCa) and to identify recurrence-associated metabolic regulators.

Transcriptomic data from TCGA and GEO GSE70769 were integrated to identify fatty acid metabolism-related genes and construct a deep learning model for BCR prediction. Tumor-infiltrating lymphocytes (TILs) were quantified from H&E-stained slides using a convolutional neural network-based approach. Single-cell RNA sequencing data were analyzed to identify candidate metabolic regulators enriched in malignant epithelial cells. Immunohistochemistry was performed to examine the protein expression patterns of NUDT19 and its expression associations with key fatty acid metabolism enzymes, including FASN, ACACA, and CPT1A. Functional roles were further evaluated using in vitro assays, xenograft models, and serum metabolomics.

The model effectively stratified PCa patients into high- and low-risk groups with distinct BCR-free survival outcomes. The high-risk group showed increased TIL infiltration, suggesting a more immune-infiltrated or inflammatory tumor microenvironment. Integrated single-cell and bulk transcriptomic analyses identified NUDT19 as a candidate metabolic regulator predominantly expressed in malignant epithelial cells and positively correlated with FASN, ACACA, and CPT1A. NUDT19 knockdown suppressed PCa cell proliferation, migration, and invasion, induced apoptosis, and inhibited tumor growth in vivo. Serum metabolomics further revealed that differential metabolites were enriched in fatty acid metabolism-related pathways.

This study establishes a fatty acid metabolism-based deep learning model for BCR prediction and identifies NUDT19 as a candidate metabolic regulator associated with lipid metabolic remodeling and PCa progression. These findings suggest a metabolically active, inflammation-associated recurrence subtype and support further investigation of NUDT19 as a potential therapeutic target in PCa.