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ORIGINAL RESEARCH article

Front. Neurosci.

Sec. Neurogenomics

Volume 19 - 2025 | doi: 10.3389/fnins.2025.1684297

This article is part of the Research TopicAdvances in Explainable Analysis Methods for Cognitive and Computational NeuroscienceView all articles

Inflammation-Related Biomarkers and Berberine Therapy in Post-Stroke Depression: Evidence from Bioinformatics, Machine Learning, and Experimental Validation

Provisionally accepted
Wei  LiuWei Liu1Ruheng  WeiRuheng Wei1,2Jingya  XuJingya Xu3Zhilong  LiuZhilong Liu4Yulai  LiYulai Li2*
  • 1Hebei North University, Zhangjiakou, China
  • 2Langfang Hospital of Traditional Chinese Medicine, Langfang, China
  • 3Jingxing County Hospital, Shijiazhuang, China
  • 4Cangzhou Integrated Traditional Chinese and Western Medicine Hospital, Cangzhou, China

The final, formatted version of the article will be published soon.

Objective: Post-stroke depression (PSD), a common neuropsychiatric complication, significantly hinders stroke recovery and quality of life. Given the established role of inflammation in PSD pathogenesis, this study aimed to identify key inflammation-related genes and pathways using bioinformatics and machine learning, and further evaluate the protective effects of traditional Chinese medicine (TCM) monomer compounds. Methods: PSD-related datasets (GSE16561, GSE98793) were obtained from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were identified using the limma package, followed by functional enrichment analysis with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Three machine learning algorithms—random forest, support vector machine-recursive feature elimination (SVM-RFE), and least absolute shrinkage and selection operator (LASSO)—were applied to screen inflammation-related hub genes. Immune cell infiltration was analyzed using single-sample gene set enrichment analysis (ssGSEA). Candidate traditional Chinese medicine compounds were explored via the Coremine Medical database. A PSD rat model was established to validate hub gene expression and to assess the efficacy of berberine. Results: Analysis identified 35 inflammation-related DEGs (IDEGs) significantly enriched in immunological processes including malaria pathogenesis, NETosis, innate immune deficiencies, Rap1 signaling, and IL-17 cascades. Integration of machine learning pinpointed TLR2 and CYP1B1 as core hub genes, demonstrating robust diagnostic performance in external validation. Molecular docking suggested strong binding affinity between the TCM compound berberine (BBR) and TLR2/CYP1B1 proteins. PSD rats exhibited prolonged immobility in forced swim/tail suspension tests and decreased sucrose preference versus controls, alongside neuronal damage, edema, and inflammatory infiltration (HE staining). BBR treatment reversed these behavioral deficits and pathological changes. Western blot confirmed elevated TLR2 and CYP1B1 expression in PSD rats, significantly downregulated by BBR. ELISA showed increased serum IL-1β, IL-6, and TNF-α levels in PSD, which BBR effectively reduced. Conclusion: This study identifies TLR2 and CYP1B1 as core inflammation-related genes in PSD. BBR demonstrates therapeutic efficacy as an active monomer compound against PSD, likely mediated through downregulating TLR2 and CYP1B1 expression, consequently diminishing concentrations of pro-inflammatory mediators (IL-1β, IL-6, TNF-α) to mediate cerebroprotective actions.

Keywords: post-stroke depression, Inflammation, Traditional Chinese Medicine, bioinformatics, machine learning

Received: 12 Aug 2025; Accepted: 25 Sep 2025.

Copyright: © 2025 Liu, Wei, Xu, Liu and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Yulai Li, 13931627195@163.com

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