KEGG-expressed genes and pathways in intervertebral disc degeneration: Protocol for a systematic review and data mining

miRNAs and genes play significant roles in the etiology and pathogenesis of intervertebral disc degeneration (IDD). This study aimed to identify aberrantly expressed miRNAs, genes, and pathways in IDD through a comprehensive bioinformatics analysis. Data of miRNAs expression microarrays (GSE63492) and genes microarrays (GSE23130) were obtained from GEO database. Similarly, aberrantly expressed miRNAs and genes were obtained using GEO2R. In addition, functional and enrichment analyses of selected miRNAs and genes were performed using the DAVID database. Meanwhile, protein–protein interaction (PPI) network was constructed using STRING, and then visualized in Cytoscape. A total of 98 upregulated miRNAs were identified. They were enriched in biological processes of response to organelle, ion binding, cellular nitrogen compound metabolic process, biosynthetic process, small molecule metabolic process, cellular protein modification process, catabolic process, molecular function, neurotrophin TRK receptor signaling pathway, and protein complex. In addition, 1405 high expression protein genes were detected. It indicated enrichment in biological processes, such as translational initiation, nonsense-mediated decay, viral transcription, cell–cell adhesion, rRNA processing, translation, RP-dependent cotranslational protein targeting to membrane, nuclear-transcribed mRNA catabolic process, regulation of mRNA stability, and mRNA splicing via spliceosome and extracellular matrix organization. In addition, pathway analysis exhibited the common enrichment in focal adhesion, Hippo signaling pathway, ECM-receptor interaction, Wnt signaling pathway, PI3K-Akt signaling pathway, endocytosis, proteoglycans in cancer, and so on. The top 10 central genes of PPI network were POTEE, PPP2CA, RPL17, HSP90AA1, POTEF, RPL13A, ACTB, RPL18, RPS24, and HSPA1A. In conclusion, our research proposed abnormally expressed miRNAs, genes, and pathways in IDD through bioinformatics methods, which may provide new insights into the pathogenesis of IDD. Thus, the Hub gene involving POTEE, PPP2CA, RPL17, HSP90AA1, POTEF, RPL13A, ACTB, RPL18, RPS24, and HSPA1A may be biomarkers for accurate diagnosis and treatment of IDD in the future.