Comprehensive analysis based on machine learning algorithm: Hspb1 and Lgals3 in neurons after spinal cord injury are closely related to autophagy

Abstract

Abstract Spinal cord injury (SCI) refers to the dysfunction of sensorimotor and autonomic nerves caused by extensive and permanent loss of neurons after different degrees of damage to the spinal cord or cauda equina. The mechanism of spinal cord neuron injury after SCI has not been fully elucidated so far, although some opinions have been put forward. In this study, we extracted primary spinal neurons from neonatal rats, constructed a neuron injury model using glutamate stimulation, and performed full transcriptome sequencing analysis. We used machine learning algorithm (WGCNA, RF, and LASSO) to comprehensively and in-depth explore the important genes of spinal cord neuron injury and screen out the key genes Anxa2, Ccng1, Hspb1, Lgals3, Timp1 and S100a10, which are accompanied by the up-regulation of six expression levels of spinal cord neuron injury. Importantly, Hspb1 and Lgals3 are closely related to autophagy. To improve the reliability of our results, we downloaded the corresponding expression levels of six key genes of GSE2599, GSE20907, GSE45006, and GSE174549 to make ROC curve for verification, and then conducted RT-PCR verification of six key genes in vitro and in vivo, respectively. These findings will help us to further understand the pathogenesis of SCI, and may contribute to the screening of key targets for future clinical treatment.