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

Area of Science:

• Neuroscience
• Immunology
• Genetics

Background:

• Nerve injury involves complex immune responses and neuronal damage, with key regulatory genes poorly understood.
• Identifying novel biomarkers is crucial for understanding nerve injury mechanisms.

Purpose of the Study:

• To identify and validate novel gene biomarkers for nerve injury.
• To elucidate the functional roles of these biomarkers in nerve injury and repair.

Main Methods:

• Integrated multi-transcriptomic datasets (RNA-seq, scRNA-seq) with machine learning (WGCNA, differential expression analysis).
• Validated biomarker diagnostic performance in independent datasets.
• Analyzed immune cell infiltration (CIBERSORT) and cell-specific expression patterns (scRNA-seq).

Main Results:

• Identified four key nerve injury genes: Atf3, Bin2, Fcgr2b, and Ucn, with robust diagnostic performance (AUC > 0.7).
• These genes are implicated in neuroinflammation, neuronal fate, and JAK-STAT/NF-κB signaling.
• Expression correlated with M2 macrophages and CD4+ T cell depletion; Fcgr2b promoted neurite outgrowth.

Conclusions:

• Atf3, Bin2, Fcgr2b, and Ucn are critical nerve injury biomarkers involved in neuroimmune crosstalk.
• These findings offer insights into therapeutic targets for nerve repair.
• Fcgr2b may play a role in promoting neurite outgrowth post-injury.