The Integrated Transcriptome Bioinformatics Analysis of Energy Metabolism-Related Profiles for Dorsal Root Ganglion of Neuropathic Pain

Abstract

Neuropathic pain (NP) is a debilitating disease and is associated with energy metabolism alterations. This study aimed to identify energy metabolism-related differentially expressed genes (EMRDEGs) in NP, construct a diagnostic model, and analyze immune cell infiltration and single-cell gene expression characteristics of NP. GSE89224, GSE123919, and GSE134003 were downloaded from the Gene Expression Omnibus. Differentially expressed genes (DEGs) analysis and an intersection with highly energy metabolism-related modules in weighted gene co-expression network analysis (WGCNA) was performed in GSE89224. Least absolute shrinkage and selection operator (LASSO), random forest, and logistic regression were used for model genes selection. NP samples were divided into high- and low-risk groups and different disease subtypes based on risk score of LASSO algorithm and consensus clustering analysis, respectively. Immune cell composition was estimated in different risk groups and NP subtypes. Datasets 134,003 were performed for identification of single-cell DEGs and functional enrichment. Cell-cell communications and pseudo-time analysis to reveal the expression profile of NP. A total of 38 EMRDEGs were obtained and are majorly enriched in metabolism about glioma and inflammation. LASSO, random forest, and logistic regression identified 6 model genes, which were Itpr1, Gng8, Socs3, Fscn1, Cckbr, and Camk1. The nomogram, based on six model genes, had a good predictive ability, concordance, and diagnostic value. The comparisons between different risk groups and NP subtypes identified important pathways and different immune cells component. The immune infiltration results majorly associated with inflammation and energy metabolism. Single-cell analysis revealed cell-cell communications and cells differentiation characteristics of NP. In conclusion, our results not only elucidate the involvement of energy metabolism in NP but also provides a robust diagnostic tool with six model genes. These findings might give insight into the pathogenesis of NP and provide effective therapeutic regimens for the treatment of NP.

Keywords: Bioinformatics; Dorsal root ganglion; Energy metabolism; Neuropathic pain; Transcriptome.