Evidence map›Paper›PMID 41733593›Full record

ArticleBrain and behavior2026

Screening Biomarkers for Nerve Injury Using Weighted Gene Co-Expression Network Analysis and Machine Learning.

Shuming Cao, Chengyue Yu, Nana Wang, Jianhua Xu, Weiguo Xu

Abstract read
In one paragraph

Article in Brain and behavior, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Shuming CaoClinical School/College of Orthopedics, Tianjin Medical University, Tianjin, China.ORCID https://orcid.org/0009-0002-9986-6754
Chengyue YuTianjin University Tianjin Hospital, Tianjin, China.
Nana WangDepartment of General Internal Medicine, Tianjin Hospital, Tianjin, China.
Jianhua XuDepartment of Hand Surgery, Tianjin Hospital, Tianjin, China.
Weiguo XuClinical School/College of Orthopedics, Tianjin Medical University, Tianjin, China.ORCID https://orcid.org/0000-0003-0386-7753

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNerve injury triggers complex molecular responses involving immune activation and neuronal damage, yet the key regulatory genes and their mechanisms remain poorly understood. Here, we integrated multi-transcriptomic datasets and machine learning to identify and validate novel biomarkers of nerve injury and elucidate their functional roles.

methodsThe RNA-seq data and the single-cell transcriptome data of nerve injury and sham-surgery samples were sourced from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA), differential expression analysis, and three machine learning algorithms were used to identify hub genes associated with nerve injury. The expression patterns and diagnostic value of these hub genes were validated in independent datasets. The correlation between these genes and immune cell infiltration was analyzed using the CIBERSORT algorithm. Finally, single-cell RNA sequencing (scRNA-seq) data were used to investigate the cell-specific expression patterns of the hub genes in neural cells.

resultsSeven nerve injury-related genes were identified via WGCNA and three machine learning methods, of which Atf3, Bin2, Fcgr2b, and Ucn exhibited robust diagnostic performance (AUC > 0.7) across validation cohorts. Functional enrichment implicated these genes in neuroinflammation, neuronal fate commitment, and JAK-STAT/NF-κB signaling. Immune infiltration analysis correlated their expression with M2 macrophage polarization and CD4+ T cell depletion, while scRNA-seq highlighted cell-specific patterns: Atf3 and Ucn were neuron-enriched, whereas Fcgr2b and Bin2 predominated in macrophages/NK cells. Moreover, Fcgr2b promoted the outgrowth of neurites in PC12 cells.

conclusionOur study unveils Atf3, Bin2, Fcgr2b, and Ucn as critical nerve injury biomarkers with dual roles in neuroimmune crosstalk, offering novel insights into therapeutic targeting for nerve repair. Moreover, Fcgr2b may be involved in neurite outgrowth after nerve injury.

Indexed as

Gene Regulatory NetworksMachine LearningPeripheral Nerve InjuriesActivating Transcription Factor 3AnimalsBiomarkersGene Expression ProfilingHumansReceptors, IgGSingle-Cell Gene Expression AnalysisTranscriptomeTumor Suppressor ProteinsActivating Transcription Factor 3BiomarkersReceptors, IgGTumor Suppressor Proteinsbiomarkerimmune infiltrationnerve injurynerve regenerationneuroinflammationsingle‐cell RNA sequencing

Identifiers

PMID41733593
PMCPMC12931491

What Socratic holds

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LicenceCC BY
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Registered trials

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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 Socratic graph.