Single-cell transcriptomics reveals the prognostic and immune infiltration significance of FOLFOX-bevacizumab treatment and lysine crotonylation characteristics in colon cancer

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Background: Colon cancer (CC), a malignant tumor originating from the colonic mucosal epithelium, frequently exhibits limited efficacy in advanced patients undergoing first-line FOLFOX (oxaliplatin, leucovorin, and 5-fluorouracil)-bevacizumab (FOLFOX-Bev) therapy due to resistance. Lysine crotonylation (Kcr), a novel histone post-translational modification, regulates gene transcription and participates in processes such as cell proliferation, metabolic reprogramming, and epithelial-mesenchymal transition, presenting potential research value in CC. However, its impact on FOLFOX-Bev treatment response and immune microenvironment remodeling at the single-cell level remains unclear. Therefore, this study aimed to integrate single-cell transcriptomics with The Cancer Genome Atlas (TCGA) data to systematically characterize the role of Kcr modification in FOLFOX-Bev treatment response and immune microenvironment remodeling in CC, and to construct a Kcr-based prognostic risk model for guiding precision therapy. Methods: "drug-resistant & low-Kcr" cells. In TCGA-COAD, Kcr single-sample Gene Set Enrichment Analysis (ssGSEA) scores, weighted gene co-expression network analysis modules, and single-cell DEGs were intersected. Utilizing the intersecting genes, a prognostic risk model was constructed via univariate-Least Absolute Shrinkage and Selection Operator (LASSO)-multivariate Cox regression. Immune infiltration, immunotherapy response, mutational landscape, and pathway enrichment features were assessed between high-/low-risk groups. Results: ) to form the risk prognostic model, which demonstrated good predictive performance in two independent datasets. The two risk groups exhibited remarkably different immune characteristics: the high-risk group showed poorer predicted immunotherapy response and lower mutation rates. Six categories of differential candidate drugs were screened (e.g., Erlotinib_1168, AZD3759_1915, Dasatinib_1079, and Sepantronium.bromide_1941). Conclusions: ), accurately stratifies patient risk. This model provides novel insights for targeted therapy and immune-combination strategies in CC.

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