# Integrative Bioinformatics Analysis Reveals Potential Gene Biomarkers and Analysis of Function in Human Degenerative Disc Annulus Fibrosus Cells

Authors: Yin Xunlu; Feng Minshan; Zhu Liguo; Zhan Jiawen; Wei Xu; Yu Jie; Wang Shangquan; Yin He; Liang Long; Han Tao; Li Xuepeng
Journal: BioMed Research International
 (2019)

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## Abstract

Low back pain is a major cause of disability worldwide. Although numerous potential biomarkers for the early diagnosis or treatment of intervertebral disc degeneration (IDD) have been identified subsequent to the development of molecular biology technologies, the mechanisms of IDD remain unknown. Published studies found the unbalance of anabolism and catabolism of annulus fibrosus (AF) played an important role in it. The present study was aimed to identify the potential targets and signaling pathways of IDD, through the combined analysis of differential expression and based on the Gene Expression Omnibus (GEO) dataset from NCBI. PPI Networks Analysis indicated that MMP2 and AGE-RAGE signaling pathway and estrogen signaling pathway may play important roles in initiation and development of IDD. This study forecasted the pathogenesis molecular mechanism of IDD and the potential prognostic and diagnostic biomarkers, but we need to make further molecular biological experiments to confirm our assumptions.


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## Body

## 1. Introduction

As a major cause of disability worldwide, low back pain (LBP) has increased the social and economic burden significantly [1]. According to the US national health interview survey, 28% of all people had experienced LBP that lasted one day or more during the past three months [2]. Almost half of them suffered from LBP over one year, with a quarter reporting “frequent” pain. In the USA, LBP has become the second frequent reason for visits to the physician and the fifth-ranking cause of admission to hospital and the third most common cause of surgical [3–5]. The total costs of LBP have exceed $100 billion per year in the United States alone according to WHO, which take the number one spot in all healthcare problems [6].Several published studies have confirmed that LBP was commonly relevant to the intervertebral disc degeneration (IDD) [7, 8]. Although the etiology (ageing, living conditions, biomechanical loading, and genetic factors) [9] and biological process (apoptosis, inflammation) of IDD are difficult to define precisely, there will be similar phenomenon, including the unbalance of anabolism and catabolism of annulus fibrosus (AF) [10], which leads to decreased ability of resisting tension [11] and thus accelerating the process of IDD finally.Removal of the degenerated or herniated tissue or even the partial or complete replacement of the disc with an artificial substitute is the current popular surgical therapy for IDD [7, 12]. However, this kind of surgery can bring about adjacent disc degeneration or the failure of intervertebral fusion [13, 14]. Therapeutic intervention (recovery the biomechanical and structural properties and restoration the biological behaviors of healthy AF cells) [10] could avoid highly invasive procedures at an early stage of IDD. Now, the multifactorial mechanisms of IDD have achieved substantial advancement, but its initiation and progression are still limited. So it is our key point to find signaling pathways to better understand the cascades of disc degeneration.Our present study aimed to identify the potential targets and signaling pathways of IDD, through the combined analysis of differential expression and based on the Gene Expression Omnibus (GEO) dataset from NCBI. This may be helpful for the precise treatment of LBP, as well as early diagnosis.


## 2. Materials and Methods

### 2.1. Microarray Date

The gene expression (GSE34095) in the present study was deposited in GEO (http://www.ncbi.nlm.nih.gov/geo/), which was accumulated by Kazezian et al. [7]. GSE34095 datasets were based on Affymetrix GeneChip Human Genome U133A Array. A total of 6 samples were included, containing three nondegenerative disc annulus fibrosus (AF) cells samples and three degenerative disc AF cells samples. Pathway Analysis software system (IPA®, QIAGEN Redwood City) was used for the sequencing process.


### 2.2. Differential Expression Analysis

The GEO2R (http://www.ncbi.nlm.nih.gov/geo/geo2r/) is used to identify differentially expressed genes (DEGs) between degenerative disc AF cells and normal samples. GEO2R is based on R that comes with the GEO databases. Genes with fold change (logFC) >0.5 (upregulated) or <-0.5 (downregulated) and false discovery rate (FDR) adjusted for P<0.1 were considered to be differentially expressed. The Heml software was used to generate the heat maps.


### 2.3. Functional Enrichment Analysis

The differential expression genes were submitted to the Database for Annotation, Visualization, and Integrated Discovery (DAVID, Version6.8,https://david.ncifcrf.gov/) [15] for the analysis of enrichment of gene ontology (GO) terms [16], Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways [17], and Reactome pathways [18]. P<0.05 was considered to indicate a statistically significant difference for the screening of significant GO terms and KEGG pathways.


### 2.4. Protein-Protein Interaction (PPI) Networks Analysis

The differential expression genes were submitted to the Search Tool for the Retrieval of Interacting Genes/Proteins (String, Version 10.0,http://www.string-db.org/) for the analysis of differential protein. PPI score =0.4 was considered that the interacted protein nodes were all transcribed by differentially expressed genes. PPI networks were represented by Cytoscape Software (Version 3.2.0) [19–21].


### 2.5. Subnetworks Analysis

The biological process may interact with multiple genes to play a regulatory role. Generally, those genes perform the same or similar biological function in the subnetworks. The method of MCODE [22] was used to analyze the interacted gene of the significant clustering modules.


## 2.1. Microarray Date

The gene expression (GSE34095) in the present study was deposited in GEO (http://www.ncbi.nlm.nih.gov/geo/), which was accumulated by Kazezian et al. [7]. GSE34095 datasets were based on Affymetrix GeneChip Human Genome U133A Array. A total of 6 samples were included, containing three nondegenerative disc annulus fibrosus (AF) cells samples and three degenerative disc AF cells samples. Pathway Analysis software system (IPA®, QIAGEN Redwood City) was used for the sequencing process.


## 2.2. Differential Expression Analysis

The GEO2R (http://www.ncbi.nlm.nih.gov/geo/geo2r/) is used to identify differentially expressed genes (DEGs) between degenerative disc AF cells and normal samples. GEO2R is based on R that comes with the GEO databases. Genes with fold change (logFC) >0.5 (upregulated) or <-0.5 (downregulated) and false discovery rate (FDR) adjusted for P<0.1 were considered to be differentially expressed. The Heml software was used to generate the heat maps.


## 2.3. Functional Enrichment Analysis

The differential expression genes were submitted to the Database for Annotation, Visualization, and Integrated Discovery (DAVID, Version6.8,https://david.ncifcrf.gov/) [15] for the analysis of enrichment of gene ontology (GO) terms [16], Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways [17], and Reactome pathways [18]. P<0.05 was considered to indicate a statistically significant difference for the screening of significant GO terms and KEGG pathways.


## 2.4. Protein-Protein Interaction (PPI) Networks Analysis

The differential expression genes were submitted to the Search Tool for the Retrieval of Interacting Genes/Proteins (String, Version 10.0,http://www.string-db.org/) for the analysis of differential protein. PPI score =0.4 was considered that the interacted protein nodes were all transcribed by differentially expressed genes. PPI networks were represented by Cytoscape Software (Version 3.2.0) [19–21].


## 2.5. Subnetworks Analysis

The biological process may interact with multiple genes to play a regulatory role. Generally, those genes perform the same or similar biological function in the subnetworks. The method of MCODE [22] was used to analyze the interacted gene of the significant clustering modules.


## 3. Results

### 3.1. Differentially Expressed Gene in Degenerative Intervertebral Discs Patients

Raw read counts for a total of 22,215 genes were obtained for gene expression analysis. The date normalization of gene expression had no significant difference in 6 samples (Figure1). Based on the criteria of |logFC| >0.5 and adj. P<0.1, numerous genes were revealed to be differential expression in degenerative samples compared with normal samples (Figure 2). Among them, 42 genes were downregulated and 78 genes were upregulated in degenerative disc AF cells compared to nondegenerative disc AF cells (Figure 2, Table 1).Table 1
Top 10 differentially expressed genes in datasets GSE34095 according to degree.
Node HSP90AA1 MMP2 XPO1 HSPD1 COL3A1 FN1 POSTN EIF2S2 HSPA2 TARS Description Up Up Up Up Up Up Up Up Down Up Degree 15 10 9 7 7 6 6 5 5 5Figure 1
Box plot of GSE34095. Blue: nondegenerative samples; red: degenerative samples.Figure 2
Heat map of differentially expressed in GSE34095 datasets. Yellow: upregulated; purple: downregulated; red: degenerative samples; Blue: nondegenerative samples.


### 3.2. Functional and Pathway Enrichment Analysis

To learn more about the function of identified intersection DEGs, functional and pathway enrichment analysis was carried out using DAVID [15]. These DEGs were mainly enriched in pathway associated with endocytosis, influenza A, legionellosis, RNA transport, NOD-like receptor signaling pathway, aminoacyl-tRNA biosynthesis, metabolism of xenobiotics by cytochrome P450, antigen processing and presentation, chemical carcinogenesis, TGF-beta signaling pathway, and AGE-RAGE signaling pathway (Table 2, Figure 3).Table 2
Functional and pathway enrichment analysis of target genes in degenerative disc AF cells.
Category Term Description Count P value KEGG_PATHWAY hsa04144 Endocytosis 5 0.0223 KEGG_PATHWAY hsa05164 Influenza A 5 0.0752 KEGG_PATHWAY hsa05134 Legionellosis 4 0.0045 KEGG_PATHWAY hsa03013 RNA transport 4 0.0908 KEGG_PATHWAY hsa04621 NOD-like receptor signaling pathway 3 0.0456 KEGG_PATHWAY hsa00970 Aminoacyl-tRNA biosynthesis 3 0.0632 KEGG_PATHWAY hsa00980 Metabolism of xenobiotics by cytochrome P450 3 0.0771 KEGG_PATHWAY hsa04612 Antigen processing and presentation 3 0.0807 KEGG_PATHWAY hsa05204 Chemical carcinogenesis 3 0.0882 KEGG_PATHWAY hsa04350 TGF-beta signaling pathway 3 0.0958 KEGG_PATHWAY hsa04933 AGE-RAGE signaling pathway 2 0.0801 REACTOME_PATHWAY R-HAS-2022870 R-HAS-2022870 3 0.0051 REACTOME_PATHWAY R-HAS-379716 R-HAS-379716 3 0.0073 REACTOME_PATHWAY R-HAS-2129379 R-HAS-2129379 3 0.0177 REACTOME_PATHWAY R-HAS-1442490 R-HAS-1442490 3 0.0466 REACTOME_PATHWAY R-HAS-300178 R-HAS-300178 3 0.0618 GOTERM_CC_DIRECT GO:0005737 Cytoplasm 45 0.0001 GOTERM_CC_DIRECT GO:0070062 Extracellular exosome 37 5.6552E-08 GOTERM_CC_DIRECT GO:0005634 Nucleus 37 0.0457 GOTERM_MF_DIRECT GO:0005515 Protein binding 63 0.0005 GOTERM_MF_DIRECT GO:0005524 ATP binding 14 0.0493 GOTERM_MF_DIRECT GO:0044822 Poly(A) RNA binding 13 0.0152 GOTERM_MF_DIRECT GO:009864 Cadherin binding involved in cell-cell adhesion 8 0.0009 GOTERM_MF_DIRECT GO:0042803 Protein homodimerization activity 8 0.0904 GOTERM_MF_DIRECT GO:0005201 Extracellular matrix structural constituent 4 0.0053 GOTERM_MF_DIRECT GO:0051059 NF-kappaB binding 3 0.0109 GOTERM_BP_DIRECT GO:0007165 Signal transduction 12 0.0457 GOTERM_BP_DIRECT GO:0045944 Positive regulation of transcription from RNA polymerase II promoter 10 0.0795 GOTERM_BP_DIRECT GO:0098609 Cell-cell adhesion 9 0.0001 Note: BP, biological process; MF, molecular function; CC, cellular component; GO, gene ontology.Figure 3
Significantly enriched KEGG pathways of DEGs in degenerative disc AF cells.


### 3.3. PPI Networks Analysis

Cytoscape software was used to determine biological relationships among the 120 differentially expressed genes (Figure4(a)). The top three module networks (Figure 4(b), Table 3), based on Fisher’s exact test, were associated with immunological disease, cell signaling, and protein generation pathways. According to the value of degree (degree⩾5), we find that the key nodes of the PPI networks included HSP90AA1, COL3A1, MMP2, POSTN, and FN1.Table 3
KEGG pathway enrichment analysis of target genes in the top three module networks.
Term Description Count P value Module A hsa05134 Legionellosis 2 0.0156 hsa04612 Antigen processing and presentation 2 0.0219 hsa04915 Estrogen signaling pathway 2 0.0285 hsa04141 Protein processing in endoplasmic reticulum 2 0.0483  Module B hsa05134 Legionellosis 2 0.0156 hsa04612 Antigen processing and presentation 2 0.0219 hsa04915 Estrogen signaling pathway 2 0.0285 hsa04141 Protein processing in endoplasmic reticulum 2 0.0483  Module C hsa05205 Proteoglycans in cancer 3 0.0025 hsa04512 ECM-receptor interaction 2 0.0373 hsa05146 Amoebiasis 2 0.0453 hsa04510 Focal adhesion 2 0.0868Figure 4
(a) PPI Networks Analysis with Cytoscape for the 120 genes that are differentially expressed between nondegenerative disc AF cells and degenerative disc AF cells samples; (b) module networks of Module A. The red color and triangle in network indicate a gene that is upregulated in degenerative disc AF cells compared to the nondegenerative disc AF cells; blue color indicates the genes that are downregulated in degenerative disc AF cells compared to the nondegenerative disc AF cells samples.
(a) (b)Functional enrichment analysis based on the DAVID identified a number of significantly enriched GO terms and KEGG pathways in module networks DEGs. The significant KEGG pathways of the top three module networks DEGs of degenerative disc AF cells samples are listed in Table3. As shown in Table 3, KEGG pathways associated with protein processing in endoplasmic reticulum and estrogen signaling pathway were significantly enriched (P<0.05). The top five most significant GOTERM_BP of the module networks DEGs are listed in Table 4. Similar to the KEGG pathways, those GOTERM_BP were mainly involved in the protein processing (assembly, refolding, and stabilization) and degradation (extracellular matrix disassembly).Table 4
Top five most significantly enriched GOTERM_BP enrichment analyses of target genes in the top three module networks.
Term Description Count P value Module A GO:0042026 protein refolding 3 2.23E-6 GO:0009409 response to cold 3 1.34E-5 GO:0006986 response to unfolded protein 3 1.83E-5 GO:0009408 response to heat 3 2.40E-5 GO:0051131 chaperone-mediated protein complex assembly 2 0.0023  Module B GO:0042026 protein refolding 3 4.46E-6 GO:0009409 response to cold 3 2.67E-5 GO:0006986 response to unfolded protein 3 3.65E-5 GO:0009408 response to heat 3 4.78E-5 GO:0050821 protein stabilization 3 3.87E-4  Module C GO:0030198 extracellular matrix organization 4 3.05E-5 GO:0022617 extracellular matrix disassembly 3 3.00E-4 GO:0001501 skeletal system development 3 9.70E-4 GO:0048050 post-embryonic eye morphogenesis 2 0.0014 GO:0048048 embryonic eye morphogenesis 2 0.0036


## 3.1. Differentially Expressed Gene in Degenerative Intervertebral Discs Patients

Raw read counts for a total of 22,215 genes were obtained for gene expression analysis. The date normalization of gene expression had no significant difference in 6 samples (Figure1). Based on the criteria of |logFC| >0.5 and adj. P<0.1, numerous genes were revealed to be differential expression in degenerative samples compared with normal samples (Figure 2). Among them, 42 genes were downregulated and 78 genes were upregulated in degenerative disc AF cells compared to nondegenerative disc AF cells (Figure 2, Table 1).Table 1
Top 10 differentially expressed genes in datasets GSE34095 according to degree.
Node HSP90AA1 MMP2 XPO1 HSPD1 COL3A1 FN1 POSTN EIF2S2 HSPA2 TARS Description Up Up Up Up Up Up Up Up Down Up Degree 15 10 9 7 7 6 6 5 5 5Figure 1
Box plot of GSE34095. Blue: nondegenerative samples; red: degenerative samples.Figure 2
Heat map of differentially expressed in GSE34095 datasets. Yellow: upregulated; purple: downregulated; red: degenerative samples; Blue: nondegenerative samples.


## 3.2. Functional and Pathway Enrichment Analysis

To learn more about the function of identified intersection DEGs, functional and pathway enrichment analysis was carried out using DAVID [15]. These DEGs were mainly enriched in pathway associated with endocytosis, influenza A, legionellosis, RNA transport, NOD-like receptor signaling pathway, aminoacyl-tRNA biosynthesis, metabolism of xenobiotics by cytochrome P450, antigen processing and presentation, chemical carcinogenesis, TGF-beta signaling pathway, and AGE-RAGE signaling pathway (Table 2, Figure 3).Table 2
Functional and pathway enrichment analysis of target genes in degenerative disc AF cells.
Category Term Description Count P value KEGG_PATHWAY hsa04144 Endocytosis 5 0.0223 KEGG_PATHWAY hsa05164 Influenza A 5 0.0752 KEGG_PATHWAY hsa05134 Legionellosis 4 0.0045 KEGG_PATHWAY hsa03013 RNA transport 4 0.0908 KEGG_PATHWAY hsa04621 NOD-like receptor signaling pathway 3 0.0456 KEGG_PATHWAY hsa00970 Aminoacyl-tRNA biosynthesis 3 0.0632 KEGG_PATHWAY hsa00980 Metabolism of xenobiotics by cytochrome P450 3 0.0771 KEGG_PATHWAY hsa04612 Antigen processing and presentation 3 0.0807 KEGG_PATHWAY hsa05204 Chemical carcinogenesis 3 0.0882 KEGG_PATHWAY hsa04350 TGF-beta signaling pathway 3 0.0958 KEGG_PATHWAY hsa04933 AGE-RAGE signaling pathway 2 0.0801 REACTOME_PATHWAY R-HAS-2022870 R-HAS-2022870 3 0.0051 REACTOME_PATHWAY R-HAS-379716 R-HAS-379716 3 0.0073 REACTOME_PATHWAY R-HAS-2129379 R-HAS-2129379 3 0.0177 REACTOME_PATHWAY R-HAS-1442490 R-HAS-1442490 3 0.0466 REACTOME_PATHWAY R-HAS-300178 R-HAS-300178 3 0.0618 GOTERM_CC_DIRECT GO:0005737 Cytoplasm 45 0.0001 GOTERM_CC_DIRECT GO:0070062 Extracellular exosome 37 5.6552E-08 GOTERM_CC_DIRECT GO:0005634 Nucleus 37 0.0457 GOTERM_MF_DIRECT GO:0005515 Protein binding 63 0.0005 GOTERM_MF_DIRECT GO:0005524 ATP binding 14 0.0493 GOTERM_MF_DIRECT GO:0044822 Poly(A) RNA binding 13 0.0152 GOTERM_MF_DIRECT GO:009864 Cadherin binding involved in cell-cell adhesion 8 0.0009 GOTERM_MF_DIRECT GO:0042803 Protein homodimerization activity 8 0.0904 GOTERM_MF_DIRECT GO:0005201 Extracellular matrix structural constituent 4 0.0053 GOTERM_MF_DIRECT GO:0051059 NF-kappaB binding 3 0.0109 GOTERM_BP_DIRECT GO:0007165 Signal transduction 12 0.0457 GOTERM_BP_DIRECT GO:0045944 Positive regulation of transcription from RNA polymerase II promoter 10 0.0795 GOTERM_BP_DIRECT GO:0098609 Cell-cell adhesion 9 0.0001 Note: BP, biological process; MF, molecular function; CC, cellular component; GO, gene ontology.Figure 3
Significantly enriched KEGG pathways of DEGs in degenerative disc AF cells.


## 3.3. PPI Networks Analysis

Cytoscape software was used to determine biological relationships among the 120 differentially expressed genes (Figure4(a)). The top three module networks (Figure 4(b), Table 3), based on Fisher’s exact test, were associated with immunological disease, cell signaling, and protein generation pathways. According to the value of degree (degree⩾5), we find that the key nodes of the PPI networks included HSP90AA1, COL3A1, MMP2, POSTN, and FN1.Table 3
KEGG pathway enrichment analysis of target genes in the top three module networks.
Term Description Count P value Module A hsa05134 Legionellosis 2 0.0156 hsa04612 Antigen processing and presentation 2 0.0219 hsa04915 Estrogen signaling pathway 2 0.0285 hsa04141 Protein processing in endoplasmic reticulum 2 0.0483  Module B hsa05134 Legionellosis 2 0.0156 hsa04612 Antigen processing and presentation 2 0.0219 hsa04915 Estrogen signaling pathway 2 0.0285 hsa04141 Protein processing in endoplasmic reticulum 2 0.0483  Module C hsa05205 Proteoglycans in cancer 3 0.0025 hsa04512 ECM-receptor interaction 2 0.0373 hsa05146 Amoebiasis 2 0.0453 hsa04510 Focal adhesion 2 0.0868Figure 4
(a) PPI Networks Analysis with Cytoscape for the 120 genes that are differentially expressed between nondegenerative disc AF cells and degenerative disc AF cells samples; (b) module networks of Module A. The red color and triangle in network indicate a gene that is upregulated in degenerative disc AF cells compared to the nondegenerative disc AF cells; blue color indicates the genes that are downregulated in degenerative disc AF cells compared to the nondegenerative disc AF cells samples.
(a) (b)Functional enrichment analysis based on the DAVID identified a number of significantly enriched GO terms and KEGG pathways in module networks DEGs. The significant KEGG pathways of the top three module networks DEGs of degenerative disc AF cells samples are listed in Table3. As shown in Table 3, KEGG pathways associated with protein processing in endoplasmic reticulum and estrogen signaling pathway were significantly enriched (P<0.05). The top five most significant GOTERM_BP of the module networks DEGs are listed in Table 4. Similar to the KEGG pathways, those GOTERM_BP were mainly involved in the protein processing (assembly, refolding, and stabilization) and degradation (extracellular matrix disassembly).Table 4
Top five most significantly enriched GOTERM_BP enrichment analyses of target genes in the top three module networks.
Term Description Count P value Module A GO:0042026 protein refolding 3 2.23E-6 GO:0009409 response to cold 3 1.34E-5 GO:0006986 response to unfolded protein 3 1.83E-5 GO:0009408 response to heat 3 2.40E-5 GO:0051131 chaperone-mediated protein complex assembly 2 0.0023  Module B GO:0042026 protein refolding 3 4.46E-6 GO:0009409 response to cold 3 2.67E-5 GO:0006986 response to unfolded protein 3 3.65E-5 GO:0009408 response to heat 3 4.78E-5 GO:0050821 protein stabilization 3 3.87E-4  Module C GO:0030198 extracellular matrix organization 4 3.05E-5 GO:0022617 extracellular matrix disassembly 3 3.00E-4 GO:0001501 skeletal system development 3 9.70E-4 GO:0048050 post-embryonic eye morphogenesis 2 0.0014 GO:0048048 embryonic eye morphogenesis 2 0.0036


## 4. Discussion

The tear or partial injury of the intervertebral disc AF is one of the important factors leading to low back pain [23]. With the emerging of IDD studies, numerous potential biomarkers for the early diagnosis or treatment of IDD have been identified subsequent to the development of molecular biology technologies. However, the mechanisms of IDD remain unknown.In this study, we analyzed the microarray data of degenerative disc AF cells from GEO database under the accession number GSE34095 by GEO2R to obtain DEGs and obtained their enriched GO terms and KEGG pathways. This study aimed to provide important clues for exploring the key genes and associated regulatory network in mechanisms of IDD resulted from AF. Based on the DEGs functional enrichment analysis, potential mechanisms and target gene for disc degeneration caused by AF were suggested as below.The abnormal expression of MMP2 might cause disc degeneration by accelerating the matrix degradation. According to previous results, a total of 120 DEGs including 42 downregulated and 78 upregulated genes were identified. We selected 10 genes according to the value of degree. Among them, HSP90AA1, MMP2, XPO1, HSPD1, COL3A1, FN1, POSTN, EIF2S2, and TARS were significantly upregulated, and HSPA2 was significantly downregulated in degenerative disc AF cells samples in this study. Recent epidemiologic studies indicated that the key factor for disc degeneration was heredity [24]. The typical character of disc degeneration was the matrix degradation in the early. As an important member of matrix metalloproteinases (MMPs) family, MMP2 plays a critical role in the excessive breakdown of the extracellular matrix (ECM) during disc degeneration [25–27]. The increased expression and activity of MMP2 was responded for degenerative lesions in disc tissue. We reasonably surmise that MMP2 aberrantly expressed plays important roles in initiation and development of IDD.The activation of AGE-RAGE signaling pathway might cause disc degeneration by accelerating the expression of MMP2. Multiple complexity factors, including age, injury, inflammation, and immunity, activate AGE-RAGE signaling pathway. Based on functional enrichment analysis, two genes (MMP2, FN1) were upregulated in AGE-RAGE signaling pathway. Notably, the two upregulated genes are downstream targets of AGE-RAGE signaling pathway [28]. What is more, AGE-RAGE signaling pathway was found over-expression in degenerative disc AF cells compared with nondegenerative disc AF cells. Taken together, it suggested that multiple complexity factors might lead to IDD by activating AGE-RAGE signaling pathway and then accelerating the expression of downstream targets-MMP2.Estrogen signaling pathway plays an important role in the process of disc degeneration. Estrogen signaling pathway is ubiquitous in different tissues throughout the body, which participates in many pathological process, such as osteoporosis and osteoarthritis [29]. Recent evidence suggests that 17-beta-estradiol (E2) can promote the proliferation of AF cells by activating estrogen beta receptor [30]. Bai et al. [31] found that estrogen could delay the development of ovariectomized rabbit’s IDD by reducing the expression of interleukin and MMPs, which could inhibit the degradation of matrix. Kato et al. [32] confirmed that E2 could stimulate the expression of COL2A1. However, estrogen secretion will decrease gradually with the increasing of age. It will lead to the activation of estrogen signaling pathway, the expression of inflammatory factors, and the degradation of matrix, which can accelerate the process of IDD.


## 5. Conclusion

Though we identified aberrantly expressed key gene (MMP2) from the GEO database and found the AGE-RAGE signaling pathway and estrogen signaling pathway in degenerative disc AF cells for IDD, which may benefit us in understanding the molecular mechanism of the pathogenesis of IDD and detecting potential prognostic and diagnostic biomarkers, it is still needed that we perform further molecular biological experiments to confirm our assumptions.



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*Source: 9890279-2019-02-21.xml*