MAPK8 and CAPN1 as potential biomarkers of intervertebral disc degeneration overlapping immune infiltration, autophagy, and ceRNA.

Background: Intervertebral disc degeneration (IDD) is one of the most common health problems in the elderly and a major causative factor in low back pain (LBP). An increasing number of studies have shown that IDD is closely associated with autophagy and immune dysregulation. Therefore, the aim of this study was to identify autophagy-related biomarkers and gene regulatory networks in IDD and potential therapeutic targets.

Methods: We obtained the gene expression profiles of IDD by downloading the datasets GSE176205 and GSE167931 from the Gene Expression Omnibus (GEO) public database. Subsequently, differentially expressed genes (DEGs) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, gene ontology (GO), and gene set enrichment analysis (GSEA) were performed to explore the biological functions of DEGs. Differentially expressed autophagy-related genes (DE-ARGs) were then crossed with the autophagy gene database. The hub genes were screened using the DE-ARGs protein-protein interaction (PPI) network. The correlation between the hub genes and immune infiltration and the construction of the gene regulatory network of the hub genes were confirmed. Finally, quantitative PCR (qPCR) was used to validate the correlation of hub genes in a rat IDD model.

Results: We obtained 636 DEGs enriched in the autophagy pathway. Our analysis revealed 30 DE-ARGs, of which six hub genes (, , , , , and ) were identified using the MCODE plugin. Immune cell infiltration analysis revealed that there was an increased proportion of CD8 T cells and M0 macrophages in IDD, whereas CD4 memory T cells, neutrophils, resting dendritic cells, follicular helper T cells, and monocytes were much less abundant. Subsequently, the competitive endogenous RNA (ceRNA) network was constructed using 15 long non-coding RNAs (lncRNAs) and 21 microRNAs (miRNAs). In quantitative PCR (qPCR) validation, two hub genes, and , were shown to be consistent with the bioinformatic analysis results.

Conclusion: Our study identified and as key biomarkers of IDD. These key hub genes may be potential therapeutic targets for IDD.