To predict the potential targets and mechanisms of lidocaine in breast cancer using network pharmacology and bioinformatics, and to experimentally validate its modulation of SCN4A/SLC7A5-related metabolic and immune pathways. Lidocaine targets were obtained from PubChem, SwissTargetPrediction, CTD, SEA and BATMAN-TCM. Breast cancer targets were retrieved from GeneCards, OMIM and TTD. Differentially expressed genes and WGCNA module genes were analyzed from GSE22820. Core genes were identified by Lasso regression, SVM-RFE and random forest. A 4T1-luc xenograft model was established. SCN4A and SLC7A5 expression and macrophage markers were detected by immunohistochemistry. RAW264.7 macrophages with SLC7A5 overexpression were used to assess M2 polarization by flow cytometry and glutamine concentration. A total of 868 lidocaine targets and 1,682 disease targets were obtained. Combined with 2,432 DEGs, 36 core genes were identified, and ADRB2, MME, FOS and SCN4A were identified by machine learning. SCN4A was downregulated in breast cancer (AUC = 0.955). Molecular docking showed good binding between lidocaine and SCN4A (-5.9 kcal/mol). Lidocaine inhibited tumor growth, reduced SCN4A and SLC7A5 expression, increased M1 macrophages (iNOS) and decreased M2 macrophages (CD206) ( P < 0.05). SLC7A5 overexpression induced M2 polarization and increased glutamine concentration, which were reversed by lidocaine ( P < 0.05). Lidocaine reverses M2 macrophage polarization and remodels the immunosuppressive microenvironment, accompanied by reduced SCN4A and SLC7A5 expression and decreased glutamine uptake. These findings provide mechanistic insights into the immunomodulatory role of lidocaine and support further investigation of its clinical relevance at achievable concentrations. However, the extent of immune microenvironment remodeling requires validation in immunocompetent models.
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Li et al. (2026) studied this question.
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