Colorectal cancer (CRC) is a prevalent digestive tract malignancy, and 5-fluorouracil (5-Fu) remains a chemotherapy cornerstone. However, drug resistance markedly compromises efficacy, highlighting the need to identify novel 5-Fu resistance biomarkers. We retrieved the GSE104645 dataset from the Gene Expression Omnibus (GEO) database. We first identified differentially expressed genes (DEGs) between 5-Fu-resistant and sensitive groups. These DEGs were then used to construct a protein-protein interaction network. To refine the biomarker candidates, least absolute shrinkage and selection operator (LASSO) regression was applied for feature selection. The association of shortlisted genes with drug sensitivity was assessed via correlation analysis with predicted 5-Fu IC50 values, and their relationship with the tumor immune microenvironment was evaluated using immune cell infiltration analysis. Finally, the expression trends of the key candidate genes were validated in human CRC cell lines using quantitative real-time PCR. Three genes— CASP1, ENG, and H6PD —emerged as convergent candidates. They occupied central positions in the PPI network, showed significant associations with 5-Fu response, and correlated with immune-cell infiltration. GSEA implicated pathways related to proliferation, apoptosis, metabolism/redox, and immune modulation, outlining a coherent mechanistic context for chemoresistance. A focused expression check in NCM460, SW1116, and Caco-2 cells was consistent with the predicted trends, supporting the robustness of the multi-layer analytic findings. Our framework identifies CASP1, ENG, H6PD as plausible 5-Fu resistance biomarkers in CRC, supporting risk stratification and targeted therapy development. Further clinical and functional studies are needed to validate their translational value.
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Xiang Huang
Long Hua
Junhao Ye
Letters in Drug Design & Discovery
Wannan Medical College
First Affiliated Hospital of Wannan Medical College
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Huang et al. (Sun,) studied this question.
www.synapsesocial.com/papers/69be37726e48c4981c67721f — DOI: https://doi.org/10.1016/j.lddd.2026.100375