Machine Learning Identifies FLNA as a Key Molecular Target Regulating Neuronal Apoptosis after Spinal Cord Injury.

Area of Science:

• Neuroscience
• Cell Biology
• Biochemistry

Background:

• Spinal cord injury (SCI) involves neuronal apoptosis, but its biomarkers and regulatory mechanisms are unclear.
• Identifying these mechanisms is crucial for developing diagnostic and therapeutic strategies for SCI.

Purpose of the Study:

• To identify differentially expressed proteins (DEPs) regulating neuronal apoptosis post-SCI.
• To uncover potential diagnostic and therapeutic targets for SCI.

Main Methods:

• LC-MS/MS analysis of spinal cord tissues at five time points post-injury.
• Bioinformatic analyses including WGCNA, random forest, SVM-RFE, and ROC curve analysis.
• Validation in a rat SCI model and PC12 cells, including Western blot, qRT-PCR, and siRNA knockdown.

Main Results:

• 351 DEPs were identified; Filamin A (FLNA) was pinpointed as a key apoptosis-related protein.
• FLNA upregulation was confirmed in SCI models; FLNA knockdown reduced apoptosis and ROS production in PC12 cells.
• FLNA knockdown inhibited the PI3K/AKT signaling pathway, and associated proteins were enriched in mitochondrial oxidative phosphorylation.

Conclusions:

• FLNA is a critical molecular target for neuronal apoptosis following SCI.
• FLNA plays a significant role in SCI-induced neuronal cell death.
• Targeting FLNA may offer a novel therapeutic approach for SCI treatment.