AI-driven multi-omics integration and virtual screening identify UFC1 as a druggable effector in chemotherapy-stressed colorectal cancer

Chemoresistance remains a major obstacle in colorectal cancer (CRC) treatment. In this study, we performed an integrative multi-omics analysis of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets, combined with machine learning approaches, to systematically investigate the UFMylation pathway in CRC. The E2 conjugating enzyme UFC1 was identified as a key determinant of chemotherapy sensitivity, with copy number amplification and elevated expression in tumors, particularly in chemotherapy-responsive patients. Single-cell transcriptomics revealed UFC1 enrichment in malignant epithelial cells, and its expression correlated with DNA damage response and apoptosis pathways. A LASSO-Cox risk model incorporating UFM1 pathway genes effectively stratified patient prognosis. Based on the UFC1 crystal structure, structure-based virtual screening was performed using TransformerCPI 2.0 against a million-scale compound libraries, followed by molecular docking, molecular dynamics simulations, and in vitro validation. The lead compound AK968 exhibited stable binding to UFC1 (ΔG = –25.07 kcal/mol), suppressed CRC cell proliferation, and synergistically enhanced 5-fluorouracil efficacy in a UFC1-dependent manner, with increased γ-H2AX foci formation indicating enhanced DNA damage accumulation. These findings identify UFC1 as a druggable effector of the UFMylation pathway in CRC chemotherapy stress and provide a framework integrating computational target identification, structure-based drug screening, and experimental validation for precision oncology.