Identifying potential inflammatory therapeutic targets and drug candidates in small fiber neuropathy: integrating Mendelian randomization, experimental validation, and deep learning.

Objective: This study aimed to investigate the causal associations between circulating inflammatory proteins and small fiber neuropathy (SFN) by integrating Mendelian randomization (MR) analysis with experimental validation in animal models, and to explore their potential as therapeutic targets. Methods: A two-sample bidirectional MR analysis was conducted to evaluate the genetic causal associations between 91 inflammatory proteins and SFN. A paclitaxel-induced SFN mouse model was developed to assess behavioral changes, intraepidermal nerve fiber density, and the expression levels of key inflammatory factors in serum, dorsal root ganglia, and spinal cord. Computational drug screening using deep learning (TransformerCPI 2.0) combined with molecular docking analysis screened small-molecule candidates with high predicted interaction likelihood to target proteins. Results: MR analysis nominated suggestive associations of C-C motif chemokine ligand 11 (CCL11, odds ratio (OR) = 1.460, 95% confidence interval (CI) = 1.059-2.012, Conclusion: CCL11 and IL18R1 are suggested as potential inflammatory targets in SFN. MCP2 showed discordant genetic and experimental signals, which may reflect context-dependent regulation and differences between genetically predicted long-term effects and acute injury responses. This study applies an integrative framework that integrates genetic prediction, experimental validation, and drug discovery, providing novel insights into SFN pathogenesis and generates hypotheses for future intervention.