Identification of key candidate genes in local dorsal root ganglion inflammation by integrated bioinformatics analysis

The purpose of the present study was to identify potential markers of local dorsal root ganglion (DRG) inflammation to aid diagnosis, treatment and prognosis evaluation of DRG pain. A localized inflammation of the DRG (LID) rat model was used to study the contribution of inflammation to pain. The dataset GSE38859 was obtained from the Gene Expression Omnibus database. Pre-treatment standardization of gene expression data for each experiment was performed using the R/Bioconductor Limma package. Differentially expressed genes (DEGs) were identified between a LID model and a sham surgery control group. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of DEGs and gene set enrichment analysis (GSEA) were carried out using the ‘clusterProfiler’ package in R. Using the Search Tool for Retrieval of Interacting Genes, a protein-protein interaction network was constructed and visualized. Candidate genes with the highest potential validity were validated using reverse transcription-quantitative PCR and western blotting. In total, 66 DEGs were enriched in GO terms related to inflammation and the immune response processes. KEGG analysis revealed 14 associated signaling pathway terms. Protein-protein interaction network analysis revealed 9 node genes, 3 of which were among the top 10 DEGs. Matrix metallopeptidase 9, chemokine CXCL9, and complement component 3 were identified as key regulators of DRG inflammatory pain progression.