Evidence map›Paper›PMID 41826854›Full record

ArticleBMC neurology2026

Screening of signaling pathways and hub genes in Guillain-Barré syndrome based on bioinformatics and machine learning.

Denger Zhang, Zhao Wang, Wei Ma, Zhenhai Wang

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Article in BMC neurology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Denger ZhangNing Xia Medical University, Yinchuan, 750004, China.
Zhao WangDepartment of Geriatric Medicine, Shanxi Bethune Hospital, Shanxi Academy of Medical Science, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital, Taiyuan, 030032, China.
Wei MaGeneral Hospital of Ningxia Medical University, Yinchuan, 750004, China.
Zhenhai WangInstitute of Medical Sciences, General Hospital of Ningxia Medical University, Yinchuan, 750004, China. nyfywzh@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGuillain-Barré syndrome (GBS) is a acute immune-mediated peripheral neuropathy with scarce incidence, yet its molecular mechanisms remain incompletely elucidated. This study aims to systematically identify key hub genes and signaling pathways in GBS to uncover novel insights into its immunopathology and potential therapeutic targets.

methodsThe transcriptomic dataset GSE31014, comprising peripheral blood leukocytes from 7 GBS patients and 7 healthy controls, was analyzed. Differentially expressed genes (DEGs) were identified using the Limma package. Functional enrichment was assessed via Gene Set Enrichment Analysis (GSEA). A protein-protein interaction (PPI) network was constructed to identify core modules, from which hub genes were pinpointed through the cross-validation of two machine learning algorithms: the Least Absolute Shrinkage and Selection Operator (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE). Immune cell infiltration was evaluated using single-sample GSEA (ssGSEA). Furthermore, upstream regulatory networks and potential therapeutic small molecules were predicted. The hub gene upreguation was validated by laboratory qRT-PCR and independent public datasets.

resultsWe identified 885 DEGs in GBS, which were significantly enriched in immune-related pathways such as chemokine signaling, B/T cell receptor signaling, and oxidative phosphorylation. PPI network and machine learning algorithms converged on two robust hub genes: HSP90AA1 and CAMP. Immune infiltration analysis revealed widespread activation of both innate and adaptive immune cells in GBS. HSP90AA1 expression correlated with B-cell and type 2 immune responses, while CAMP displayed a dual role, associated with both innate immune killing and the regulation of specific T-cell and NK-cell subsets. Both genes were co-enriched in the chemokine signaling pathway. FOXC1 was predicted as a common upstream transcriptional regulator. Additionally, five promising small-molecule therapeutics were screened. The significant upregulation of HSP90AA1 and CAMP in GBS was consistently validated.

conclusionThis integrated bioinformatics study identifies HSP90AA1 and CAMP as key hub genes in GBS, potentially driving neuroinflammation through the dysregulation of chemokine-mediated immune recruitment and activation. The findings offer novel insights into the immunopathogenesis of GBS and propose candidate molecules for future therapeutic development.

Indexed as

Computational BiologyGuillain-Barre SyndromeMachine LearningSignal TransductionGene Expression ProfilingGene Regulatory NetworksHumansProtein Interaction MapsTranscriptomeBioinformaticsCAMPGuillain - Barré syndromeHSP90AA1Immune infiltrationMachine learning

Identifiers

PMID41826854
PMCPMC13067632

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.