Crohn’s disease (CD) is a chronic inflammatory disease characterized by complex immune dysregulation in which the identification of key molecular drivers is critical for the advancement of diagnostic and therapeutic approaches. In this study, we integrated transcriptomic data from multiple cohorts and applied three machine learning algorithms—Random forest, support vector machine recursive feature elimination (SVM‐RFE), and Least Absolute Shrinkage and Selection Operator (LASSO)—to robustly identify key gene, converging on CSF3R as a top candidate. Mendelian randomization (MR) analysis supported a causal role of CSF3R in CD pathogenesis (OR = 1.400, 95% CI: 1.022–1.917). Enrichment analysis revealed its association with cytokine‐receptor interactions and the JAK‐STAT pathway. Single‐cell RNA sequencing and immune infiltration analyses demonstrated elevated CSF3R expression in neutrophils, implicating it in neutrophil‐mediated inflammation. Experimental validation using intestinal biopsies from CD patients and healthy controls (HCs) confirmed significantly upregulated CSF3R expression at both mRNA and protein levels, as shown by quantitative reverse transcription PCR (qPCR), western blot, and immunohistochemistry. Double immunofluorescence further revealed strong colocalization of CSF3R with the neutrophil marker CD66b, supporting its functional association with neutrophil infiltration. Moreover, molecular docking indicated high binding affinity between CSF3R and several therapeutic agents, including methotrexate and aspirin. Diagnostic performance assessments yielded high AUC values (0.823–0.938) across multiple datasets. Collectively, these findings highlight CSF3R as a robust diagnostic gene and promising therapeutic target in CD, offering mechanistic insights and opportunities for precision medicine.
Li et al. (Wed,) studied this question.