IDD is a major cause of the neck or back pain in many countries, including the United States and China, which will bring mental and physical suffering to the patient himself and a heavy financial burden to the patient’s family and the whole society (1). And but most of the studies in this field is about the management and diagnosis IDD. Risbud et al. reported in 2014 that intervertebral disc degeneration is related to cytokines such as TNF, IL-1α, IL-1β, etc. (2), But the underlying molecular mechanism, especially genetic mechanism is seldom covered.
In this study, we aimed to reveal the genetic factors and pathways that closely related to the etiology of IDD by the integrated bioinformatics analysis and provide a new idea for following investigations on the etiology of IDD.
We firstly systemically searched the GEO database (3-5) and identified profile dataset GSE34095 for expression data extraction and analysis. We assigned all the samples into two groups, one is the normal group, which contains the tissue obtained from healthy people; the other one is the IDD group, which includes the tissue obtained from patients diagnosed as IDD. After getting the expression level of all genes, we set the threshold value to screen the DEGs for the following. In this study, 61 DEGs were identified, of which 43 DEGs were upregulated, 18 DEGs were downregulated. We then constructed the PPI network (6,7) of all DEGs (Figure1). We then used the Cytoscape (version 3.7.2) (8,9) to modify the network and found two medically and statically significant modules by the applet of the Cytoscape (version 3.7.2) called MCODE (version 1.6). In the end, we applied the cytohubba (version 0.0.1) (10), an applet of the Cytoscape, to detect the top ten hub genes of the whole PPI network. They are FN1, MMP2, POSTN, COL3A1, TIMP3, FBN1, GJA1, TGFBI, EFEMP1 and ID1.
Thirdly we conducted the GO (11) and KEGG (12) pathway enrichment analysis on the DAVID (13), which enables us to investigate the relationship between the genes and biological process, cell component, molecular function and pathways of our bodies. The results are as follows: for all DEGs identified, they are mainly enriched in angiogenesis in the term of biological process, extracellular region in the term of cell component, integrin binding in the term of molecular function and PI3K-Akt signaling pathway in the term of pathway. For genes involved in module 1, they are primarily enriched in angiogenesis in the term of biological process, extracellular matrix in the term of cell component, integrin binding in the term of molecular function and proteoglycans in cancer in the term of pathway. For the genes involved in module 2, they are primarily enriched in ribosomal protein import into nucleus in the term of biological process, nuclear membrane in the term of cell component, poly(A) RNA binding in the term of molecular function and nothing could be detected for the module 2 in the term of pathway. For all top ten hub genes, they are mainly enriched in extracellular matrix organization in the term of biological process, extracellular matrix in the term of cell component, integrin binding in the term of molecular function and proteoglycans in cancer in the term of pathway.
For the sake of precision and credibility of our functional enrichment analysis and the DEGs we identified, we ought to perform an independent validation experiment. But due to the outbreak of the COVID-19 virus, we are unable to get enough biopsy samples for validation. So, we provided the primers of the mRNAs of all DEGs in this study, which could be useful for furthering validation if someone interested in this. All the primers are shown in Table S2.