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pmid:41164411
Inflammation-related biomarkers and berberine therapy in post-stroke depression: evidence from bioinformatics, machine learning, and experimental validation.
Wei Liu · Ruheng Wei · Jingya Xu · Zhilong Liu · Yulai Li
Frontiers in neuroscience · 2025 doi:10.3389/fnins.2025.1684297
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Complete file
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Given the established role of inflammation in the pathogenesis of PSD, 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.\nMethods: 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 TCM 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 (BBR).\nResults: 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. The integration of machine learning pinpointed TLR2 and CYP1B1 as core hub genes, demonstrating robust diagnostic performance in external validation. Molecular docking suggested a strong binding affinity between the TCM compound 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 analysis confirmed elevated TLR2 and CYP1B1 expression in PSD rats, significantly downregulated by BBR. Enzyme-linked immunosorbent assay (ELISA) showed increased serum IL-1β, IL-6, and TNF-α levels in PSD, which BBR effectively reduced.\nConclusion: 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 the concentrations of pro-inflammatory mediators (IL-1β, IL-6, TNF-α) that mediate cerebroprotective actions.", "claims": [{"id": "claim-method", "statement": "Statistical analyses were performed using one-way analysis of variance (ANOVA) followed by Tukey’s post hoc test for multiple group comparisons.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41164411", "locator": "sections[18] \"Statistical analysis\" paragraph 2", "quote": "Statistical analyses were performed using one-way analysis of variance (ANOVA) followed by Tukey’s post hoc test for multiple group comparisons."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. This data can be found here: GSE16561, GSE98793, GSE38206, and GSE58294.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41164411", "locator": "sections[34] \"Data availability statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. This data can be found here: GSE16561, GSE98793, GSE38206, and GSE58294."}], "state": "complete"}, {"id": "claim-target-result", "statement": "TLR2 and CYP1B1 demonstrated good diagnostic performance in the ischemic stroke and major depressive disorder validation datasets, with AUC values of 0.815 and 0.831 and 0.796 and 0.802, respectively.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:41164411", "locator": "sections[23] \"Diagnostic value and validation of core biomarkers\" paragraph 2", "quote": "In the IS validation set (GSE58294), the AUC values for TLR2 and CYP1B1 were 0.815 and 0.831, respectively (Figure 4B)."}, {"asset": "source:pmid:41164411", "locator": "sections[23] \"Diagnostic value and validation of core biomarkers\" paragraph 2", "quote": "In the MDD validation set (GSE38206), the AUC values were 0.796 and 0.802, respectively (Figure 4D)."}, {"asset": "source:pmid:41164411", "locator": "sections[23] \"Diagnostic value and validation of core biomarkers\" paragraph 2", "quote": "These results indicate that both TLR2 and CYP1B1 demonstrated good diagnostic performance, suggesting their potential as diagnostic biomarkers for PSD."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "TLR2 and CYP1B1 demonstrated good diagnostic performance in the ischemic stroke and major depressive disorder validation datasets, with AUC values of 0.815 and 0.831 and 0.796 and 0.802, respectively.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:41164411"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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