Potential Novel Therapeutic Targets In Epilepsy From The View of Intestinal Comorbidities - Inflammatory Bowel Disease

doi:10.21203/rs.3.rs-960451/v1

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Potential Novel Therapeutic Targets In Epilepsy From The View of Intestinal Comorbidities - Inflammatory Bowel Disease

https://doi.org/10.21203/rs.3.rs-960451/v1

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Background: Epilepsy is a complicated neurological disorder with almost 30% refractory. Recent years, several studies showed that epilepsy is associated with its comorbidities by shared molecular mechanisms. However, the association of epilepsy and digestive comorbidities are still unclear. In this study, we aim to explore the association between inflammatory bowel disease (IBD) and epilepsy, and to find promising therapeutic targets for refractory epilepsy.

Methods: Two gene expression profiles (GSE134697 and GSE59071) were selected from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by GEO2R and the DESeq2 package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of intersection DEGs and Gene Set Enrichment Analysis (GSEA) were conducted by clusterProfiler package. The protein-protein interactions (PPI) network was established by using STRING and visualized by Cytoscape. Genes in the most significant module identified by MCODE plug-in were considered as candidate hub genes. Validation of hub genes were performed by using the GSE143272 dataset.

Results: Cytokine-cytokine receptor interaction pathway is identified as a communal pathway between IBD and epilepsy. CXCL8, CXCR4 and ITGAX were identified as the hub genes.

Conclusions: The identification of the communal pathway and hub genes in this study contributes to a potential novel therapeutic target in refractory epilepsy.

Neurology

epilepsy

inflammatory bowel disease

inflammation

CXCL8

transcriptome

Epilepsy is a brain disorder characterized by an enduring predisposition to generate epileptic seizures [1]. Over 70 million people worldwide are affected by epilepsy and suffering various negative consequences from epileptic seizures, including disability and mortality [2, 3]. Although the current antiseizure medications (ASMs) may control 70% of seizures, 30% of seizures were refractory that make epilepsy a burdensome disease [2–4]. Thus, it is essential to explore diverse molecular targets and to develop novel treatments for epilepsy, especially for drug-resistant epilepsy, but remaining a major challenge in academia [3, 5].

With the intensive study of comorbidities of epilepsy, it is revealed that epilepsy and its comorbidities are associated by many shared risk factors, including generic, environmental, structural, or physiological [6, 7]. There also several studies showed that epileptic seizures could be controlled by treating comorbidities [7–9]. Thus, it is undoubtfully a fast and effective method to develop novel therapeutic targets by exploring shared molecular mechanisms of epilepsy and comorbid conditions [7]. There have been several studies show the shared molecular mechanisms of epilepsy and neuropsychiatric comorbidities, such as GABRA4, FZD7, Dlg4, and Vamp2 [10–12]. However, the association of epilepsy and digestive comorbidities are still unclear.

Inflammatory bowel disease (IBD) is a group of autoimmune disorders, which is a common intestinal comorbid condition of epilepsy [7, 13, 14]. IBD and epilepsy are both involved in the mechanisms of inflammatory, hypercoagulable, and genetic factors [13, 15]. However, what molecules are acting in these shared mechanisms are unknown. In this study, we conducted a combined transcriptome analysis of epilepsy and IBD patients from Gene Expression Omnibus (GEO) database to identify the shared molecules mechanisms and determine the potential pathway by which develop novel therapeutic targets for epilepsy.

2.1. Data Collection

All microarray datasets were downloaded from the GEO database. We searched the GEO database for microarray datasets using the keywords “epilepsy” and “IBD” {including “Crohn’s disease (CD)” and “ulcerative colitis (UC)”}. Datasets were recruited by the following criteria: (1) transcriptome data; (2) samples were derived from homo sapiens; (3) the count of samples was > 15; (4) including control groups. Finally, gene expression microarray datasets GSE134697 and GSE59071 were qualified to be selected. According to the IBD classification, GSE59071 was separated into two parts: GSE59071-UC and GSE59071-CD. The two datasets were based on the platforms of GPL16791 [Illumina HiSeq 2500 (Homo sapiens)], and GPL6244 {[HuGene-1_0-st] Affymetrix Human Gene 1.0 ST Array [transcript (gene) version]}, respectively.

The dataset GSE134697 contained 36 samples, including 17 hippocampal samples obtained from patients with drug-resistant temporal lobe epilepsy in the case group, and 19 neocortex samples in the control group that obtained from patients with drug-resistant temporal lobe epilepsy (17 samples) and healthy individuals (2 samples), respectively. The dataset GSE59071 contained 116 samples, including 105 affected intestinal mucosal samples obtained from patients with IBD (97 UC and 8 CD), and 11 intestinal mucosal samples obtained from healthy individuals in the control group.

2.2. Identification of Differentially Expressed Genes (DEGs)

DEGs screenings of the two datasets GSE134697 and GSE59071 were conducted by the GEO2R online analysis tool (https://www.ncbi.nlm.nih.gov/geo/geo2r/) and the R package “DESeq2” [16], respectively. P-values were adjusted by using the false discovery rate (FDR) method. Genes with adjusted P < 0.05 and |log2 fold change (FC)| >1 were considered as DEGs. We used Venn diagram web tool (http://bioinformatics.psb.ugent.be/webtools/Venn/) to find the DEGs shared by both datasets.

2.3. Function Enrichment Analysis of DEGs

To explore the enriched biological pathways and annotations, we utilized the RStudio v4.0.5 to conduct the Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis by clusterProfiler package [17]. Adjust P-values < 0 .05 were regarded as the cut-off criteria.

2.4. Gene Set Enrichment Analysis (GSEA) of Two Expression Profiles

To further reveal the pathway of epilepsy and IBD and find the common pathway between them, GSEA was performed based on each single expression matrix. R package “clusterProfiler” [17] was utilized to perform GSEA [18]. Enrichment analyses were conducted to determine whether a series of a priori-defined biological processes were enriched. The genes of two datasets were ranked according to the false discovery rate (FDR). P < 0.05 were statistically significant.

2.5. Protein-protein interaction Network Construction, Significant Module Analysis and Candidate Hub Gene Identification

The protein-protein interactions (PPI) network of DEGs was established by using STRING v11.0 (https://string-db.org) [19]. DEGs interactions with combined score > 0.4 were considered significant and were visualized by Cytoscape software v3.7.2 [20]. The significant modules from the PPI network were identified by the molecular complex detection technology (MCODE), which is a Cytoscape’s plug-in [21]. The GO enrichment analysis of the most significant module genes were conducted by using the Database for Annotation, Visualization and Integrated Discovery (DAVID) v6.8 (https://david.abcc.ncifcrf.gov) [22]. Genes in the most significant module identified by MCODE plug-in were selected as candidate hub genes.

2.6. Validation of Candidate Hub Genes

We used GEO2R to identify the DEGs in the GSE143272 dataset [epilepsy response to valproate (VPA) treatment vs. healthy control]. The candidate hub genes, which were downregulated in this dataset were identified as hub genes.

3.1. Identification of DEGs in Epilepsy and IBD

In the GSE59071 dataset, 1243 DEGs (UC tissues vs. control) and 697 DEGs (CD tissues vs. control) were identified. In the GSE134697 dataset, 1846 DEGs (hippocampus vs. control) were identified. The shared DEGs of datasets GSE134697 and GSE59071 were 78 genes, including 56 upregulated and 22 downregulated genes (Figure 1, Table 1).

3.2. GO Enrichment Analysis of DEGs

GO enrichment analysis showed various upregulated and downregulated DEGs in biological processes (BP), molecular functions (MF), and cellular components (CC), respectively. In BP, upregulated DEGs were significantly enriched in regulation of vasculature development, response to molecule of bacterial origin, response to lipopolysaccharide, positive regulation of cytokine production, and positive regulation of cell-cell adhesion (Figure 2a, Table 2). In MF, DEGs were enriched in glycosaminoglycan binding, cytokine activity, receptor ligand activity, and signaling receptor activator activity (Figure 2b, Table 2). In CC, upregulated DEGs were significantly enriched in secretory granule membrane and collagen-containing extracellular (Figure 2c, Table 2). Downregulated DEGs were only enriched in MF (Figure 2d, Table 2). More detailed GO enrichment results are shown in Table 2. The amounts of upregulated genes involved in the different processes of BP, CC, and MF were much higher than that of downregulated genes. These results suggest that comorbidity of epilepsy and IBD may undergo complex metabolic activities involving cytokine associated inflammatory response and other complex biological process.

3.3. KEGG Enrichment Analysis of DEGs

KEGG enrichment analysis showed that upregulated genes were enriched in 26 pathways, while the downregulated genes were not enriched in any pathway (Table 3). Upregulated DEGs were enriched in pathways of rheumatoid arthritis, graft-versus-host disease, type I diabetes mellitus, hematopoietic cell lineage, leishmaniasis, intestinal immune network for IgA production, toll-like receptor signaling pathway, inflammatory bowel disease, and cytokine-cytokine receptor interaction pathway and so on (Figure 3). These pathways are mainly involved in inflammatory or proinflammatory response.

3.4. GSEA

GSEA showed that the dataset GSE134697 is enriched in these pathways: antigen processing and presentation, cytokine-cytokine receptor interaction, graft-versus-host disease, hematopoietic cell lineage, and leishmaniasis infection. As for dataset GSE59071, the expression profiles of both UC and CD samples were enriched in cell adhesion molecules cams, chemokine signaling pathway, complement and coagulation cascades, cytokine-cytokine receptor interaction, and ECM receptor interaction. The results indicated that the cytokine-cytokine receptor interaction pathway is the communal pathway between epilepsy and IBD (Figure 4a, 4b).

3.5. PPI Network Construction, Significant Module Analysis and Candidate Hub Gene Identification

We developed a PPI network containing 51 nodes and 162 edges and visualized using Cytoscape software (Figure 5a). Nodes are drawn in different colors, representing the node degree and control (up or down), there were 56 upregulated genes and 22 downregulated genes. The most significant module with the highest score (module score: 9.111) was shown in Figure 5b, containing 10 genes and 41 edges. GO analysis revealed that these genes are involved in chemokine-mediated signaling pathway, inflammatory response, and the other biological process are showed in Table 4. Genes in the most significant module were selected as candidate hub genes. These genes were all upregulated genes, as follows: CXCL8, CXCR4, ITGAX, CD80, SELL, CD44, CD69, IL1B, CD38, and CXCL2.

3.6. Validation of Candidate Hub genes

We finally identified three hub genes: CXCL8, CXCR4 and ITGAX. CXCL8 is significantly downregulated (adjusted P <0.05 and log2FC =-1.2911696) and the other two genes are slightly downregulated in epilepsy patients with response to VPA treatment, while these three genes are significantly upregulated in GSE134697 and GSE59071 datasets.

There is an association between epilepsy and IBD; however, the possible factors connecting epilepsy and IBD have not been completely addressed [23–25]. In this study, we conducted a transcriptome analysis to explore the association between IBD and epilepsy. And also, we aim to find promising therapeutic targets for refractory from the view of intestinal comorbidities. A total of 78 overlapping DEGs were identified, of which 56 were upregulated and 22 were downregulated genes. GO analysis revealed that DEGs were enriched in regulation of vasculature development, response to molecule of bacterial origin, response to lipopolysaccharide, and positive regulation of cytokine production. KEGG analysis showed that the DEGs enriched in rheumatoid arthritis, intestinal immune network for IgA production, and toll-like receptor signaling pathway. GSEA showed that the gene expression profile of epilepsy is enriched in cytokine-cytokine receptor interaction pathway, same as IBD. Previous study showed that central or peripheral inflammation has play an important role in triggering or sustaining epileptic activity [8, 26–29]. Therefore, IBD may induce or aggravate seizures through the communal pathway and other inflammation/proinflammatory process. We identified 3 hub genes: CXCL8, CXCR4, and ITGAX, which are all upregulated in both datasets. However, CXCL8 is significant downregulated, and the other two genes were slightly downregulated in epilepsy patients with response to VPA treatment (dataset GSE143272). These findings suggest that the three hub genes may be promising therapeutic targets for epilepsy and IBD, especially CXCL8.

Interleukin-8 (IL-8: CXCL8) as a chemotactic and inflammatory cytokine plays a key role in central nervous system (CNS) inflammation [30, 31], such as guiding leukocyte to move to inflammatory sites [32]. High levels of CXCL8 have been discovered in the CNS, when the brain was affected by viral [33]. Elevated levels for CXCL8/IL-8 were also reported in febrile infection-related epilepsy syndrome and epileptic temporal lobe and so on [34, 35]. In epileptic patients, the relative levels of CXCL8 transcripts were much higher than healthy subjects [36]. Besides, it is well known that upregulated of CXCL8 concentration is associated with inflammation, and increased CXCL8 expression in UC colonic mucosa comparing with normal controls [32]. Thus, combing our discoveries in this study, we supposed that CXCL8 may have an immunoregulatory role in the pathogenesis of epilepsy and IBD.

The chemokine CXC motif receptor4 (CXCR4), which is cognate with chemokine CXCL12, also known as stromal cell derivedfactor-1 (SDF-1), is highly expressed in the CNS and play a crucial role in brain architecture [37–39]. A lot of research indicated that the expression of chemokine receptors was upregulated in epilepsy and the active stage of UC [40–42], which is consistent with this study. Besides CXCR4 antagonist AMD3100 reverses the neurogenesis promoted by enriched environment and suppresses long-term seizure activity in adult rats of temporal lobe epilepsy [43, 44]. And animal experiments also showed that AMD-3100 can reduced colon injury in mice, and blocking of CXCL12/CXCR4 was beneficial in improving experimental colitis in rodent models [45–48].

ITGAX encodes the integrin alpha X chain protein, also name as CD11c, which is a type I transmembrane protein. On most dendritic cells (DCs), CD11c is discovered at high levels. Normally, DCs are located in the meninges and choroid plexus, but perivascular and parenchymal DCs can also be found in the brain under a variety of inflammatory conditions. CD11c protein expression was increased after epileptic persistent state (SE), but was not detected in the control group [49].

Previous study indicated that intestinal inflammation in animal epilepsy models lowered seizure threshold, most likely through increased circulating levels of some cytokines or other inflammatory mediators, such as tumor necrosis factor α (TNFα). [50–52]. In this study, we have identified three hub genes. These hub genes that are inflammatory factors may be the core factors of the molecular mechanism of epilepsy and IBD. The increased circulating levels of the three hub genes may also lower seizure threshold and trigger seizures, which warrants further verification. In the end, we concluded that the three hub genes (CXCL8, CXCR4, and ITGAX) may be promising targets of epilepsy, which is potential novel therapeutic targets in refractory epilepsy.

Author Contributions

Conceptualization, M.J. and Z.S.; Methodology, M.J. and J.L.; Software, M.J.; Validation, M.J., Z.S., and J.L.; Investigation, M.J. and Z.S.; Writing – Original Draft Preparation, M.J.; Writing – Review & Editing, M.J., Z.S., and J.L.; Visualization, M.J.; Supervision, Z.S.; Project Administration, Z.S.; Funding Acquisition, Z.S.

Funding

This research was funded by the National Natural Science Foundation of China grant number 81671296.

Availability of data and materials

The datasets analyzed during the current study are available in the National Center for Biotechnology Information-Gene Expression Omnibus (NCBI-GEO) database (https://www.ncbi.nlm.nih.gov/geo/). We downloaded two datasets: GSE134697 and GSE59071.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests for this research.

Acknowledgments

None.

Contributor Information

Mi Jiang, Email: [email protected].

Jia Li, Email: [email protected].

Zhi Song, Email: [email protected].

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Table 1. Co-expression of differential genes

Group	Gene symbol
Upregulated	PADI2 IFI44L CAPN13 FPR1 HLA-DPB1 THBS2 ADGRE2 EGR1 HLA-DPA1 TREM1 STX11 PRRX1 ITGAX CXCL2 PDK4 ABCA12 ANGPTL2 ADM SCIN MT1A CXCR4 VCAN RASGRP3 ENPP2 THEMIS2 IL1A FGF7 SGK2 PDPN ZBP1 ADAMTS9 SPP1 DMBT1 SELL NPSR1 APLNR BCL2A1 SPARC SLC51A SLC11A1 CHI3L2 FOS CD80 CD38 PCK1 CD69 GBA3 IL1B SERPINE1 LIPG CD44 CXCL8 RGS1 TNC OSM SERPINA3
Downregulated	HSD17B2 SATB2-AS1 MUC5B MT1H SLC38A4 PTGDR MEDAG FAM20A GLRA2 TRHDE CEMIP SLCO4C1 CNTN3 COL3A1 OLFM4 CNTN4 ADGRL2 STRIP2 SLC9A2 NOS2 THRB MGP

Table 2. GO enrichment analysis of DEGs

Category		Term	Description		Gene ID	Count	P-value
Upregulated	BP	GO:1901342	regulation of vasculature development	CXCR4/IL1A/ENPP2/ITGAX/ADM/IL1B/ADAMTS9/APLNR/CXCL8/EGR1/THBS2/SERPINE1/SPARC		13	4.30E-10
		GO:0002237	response to molecule of bacterial origin	IL1A/SLC11A1/CXCL2/ADM/IL1B/CXCL8/PCK1/CD80/DMBT1/FOS/SERPINE1/SPARC		12	4.49E-10
		GO:0045765	regulation of angiogenesis	CXCR4/IL1A/ENPP2/ITGAX/ADM/IL1B/ADAMTS9/APLNR/CXCL8/THBS2/SERPINE1/SPARC		12	1.83E-09
		GO:0032496	response to lipopolysaccharide	IL1A/SLC11A1/CXCL2/ADM/IL1B/CXCL8/PCK1/CD80/FOS/SERPINE1/SPARC		11	3.32E-09
		GO:1904018	positive regulation of vasculature development	CXCR4/IL1A/ITGAX/ADM/IL1B/APLNR/CXCL8/EGR1/SERPINE1		9	2.82E-08
		GO:0045766	positive regulation of angiogenesis	CXCR4/IL1A/ITGAX/ADM/IL1B/APLNR/CXCL8/SERPINE1		8	1.72E-07
		GO:0001819	positive regulation of cytokine production	IL1A/OSM/SLC11A1/HLA-DPB1/IL1B/EGR1/HLA-DPA1/ZBP1/CD80/SERPINE1		10	6.77E-07
		GO:0022409	positive regulation of cell-cell adhesion	CD44/IL1A/HLA-DPB1/IL1B/HLA-DPA1/PCK1/PDPN/CD80		8	1.59E-06
		GO:0030198	extracellular matrix organization	CD44/SPP1/ITGAX/VCAN/ADAMTS9/PDPN/TNC/SERPINE1/SPARC		9	2.24E-06
		GO:0043062	extracellular structure organization	CD44/SPP1/ITGAX/VCAN/ADAMTS9/PDPN/TNC/SERPINE1/SPARC		9	2.29E-06
	MF	GO:0005539	glycosaminoglycan binding	CD44/SELL/ADGRE2/LIPG/VCAN/FGF7/THBS2		7	6.45E-06
		GO:0005125	cytokine activity	IL1A/SPP1/OSM/CXCL2/IL1B/CXCL8		6	7.86E-05
		GO:0048018	receptor ligand activity	IL1A/SPP1/OSM/CXCL2/ADM/IL1B/CXCL8/FGF7		8	1.08E-04
		GO:0030546	signaling receptor activator activity	IL1A/SPP1/OSM/CXCL2/ADM/IL1B/CXCL8/FGF7		8	1.16E-04
		GO:0050840	extracellular matrix binding	SPP1/DMBT1/SPARC		3	7.01E-04
		GO:0140375	immune receptor activity	CD44/CXCR4/FPR1/HLA-DPA1		4	7.65E-04
		GO:0005149	interleukin-1 receptor binding	IL1A/IL1B		2	1.07E-03
		GO:0008528	G protein-coupled peptide receptor activity	CXCR4/FPR1/APLNR/NPSR1		4	1.08E-03
		GO:0001653	peptide receptor activity	CXCR4/FPR1/APLNR/NPSR1		4	1.19E-03
		GO:1901681	sulfur compound binding	SELL/ADGRE2/LIPG/FGF7/THBS2		5	1.20E-03
	CC	GO:0030667	secretory granule membrane	CD44/SELL/CD38/SLC11A1/FPR1/ITGAX/DMBT1/SPARC		8	2.42E-06
	CC	GO:0062023	collagen-containing extracellular matrix	VCAN/SERPINA3/ADAMTS9/TNC/THBS2/SERPINE1/ANGPTL2/SPARC		8	2.74E-05

Table 2. GO enrichment analysis of DEGs (continued)

Category		Term	Description	Gene ID	Count	P-value
Upregulated	CC	GO:0031091	platelet alpha granule	SERPINA3/THBS2/SERPINE1/SPARC	4	1.34E-04
		GO:0101003	ficolin-1-rich granule membrane	SLC11A1/FPR1/ITGAX	3	7.11E-04
		GO:0031093	platelet alpha granule lumen	SERPINA3/SERPINE1/SPARC	3	9.35E-04
		GO:0042613	MHC class II protein complex	HLA-DPB1/HLA-DPA1	2	9.42E-04
		GO:0030666	endocytic vesicle membrane	SLC11A1/HLA-DPB1/HLA-DPA1/DMBT1	4	1.25E-03
		GO:0031258	lamellipodium membrane	CD44/PDPN	2	1.79E-03
		GO:0030139	endocytic vesicle	SLC11A1/HLA-DPB1/HLA-DPA1/DMBT1/SPARC	5	1.99E-03
		GO:0042611	MHC protein complex	HLA-DPB1/HLA-DPA1	2	2.32E-03
Downregulated	MF	GO:0008509	anion transmembrane transporter activity	SLC38A4/SLC9A2/GLRA2/SLCO4C1	4	5.33E-04
		GO:0031406	carboxylic acid binding	NOS2/CEMIP/GLRA2	3	1.50E-03
		GO:0043177	organic acid binding	NOS2/CEMIP/GLRA2	3	1.75E-03
		GO:0016597	amino acid binding	NOS2/GLRA2	2	1.86E-03
		GO:0015291	secondary active transmembrane transporter activity	SLC38A4/SLC9A2/SLCO4C1	3	2.04E-03
		GO:0008509	anion transmembrane transporter activity	SLC38A4/SLC9A2/GLRA2/SLCO4C1	4	5.33E-04

note:GO, Gene Ontology; DEGs, differentially expressed genes; BP, biological processes; MF, molecular functions; CC, cellular components

Table 3. The KEGG enrichment analysis of DEGs (top 10 terms according to P.value)

ID	Description	Genes	Count	P.value
hsa05323	Rheumatoid arthritis	IL1A/CXCL2/HLA-DPB1/IL1B/CXCL8/HLA-DPA1/CD80/FOS	8	1.26E-08
hsa05332	Graft-versus-host disease	IL1A/HLA-DPB1/IL1B/HLA-DPA1/CD80	5	1.68E-06
hsa04940	Type I diabetes mellitus	IL1A/HLA-DPB1/IL1B/HLA-DPA1/CD80	5	1.89E-06
hsa04640	Hematopoietic cell lineage	CD44/CD38/HLA-DPA1/IL1A/HLA-DPB1	6	7.81E-06
hsa05140	Leishmaniasis	IL1A/HLA-DPB1/IL1B/HLA-DPA1/FOS	5	3.44E-05
hsa04672	Intestinal immune network for IgA production	CXCR4/HLA-DPB1/HLA-DPA1	4	9.17E-05
hsa04933	AGE-RAGE signaling pathway in diabetic complications	IL1A/IL1B/CXCL8/EGR1/SERPINE1	5	1.21E-04
hsa04620	Toll-like receptor signaling pathway	SPP1/IL1B/CXCL8/HLA-DPA1/FOS	5	1.45E-04
hsa05321	Inflammatory bowel disease	IL1A/HLA-DPB1/IL1B/HLA-DPA1	4	2.77E-04
hsa05133	Pertussis	IL1A/IL1B/CXCL8/FOS	4	5.05E-04
note: KEGG, Kyoto Encyclopedia of Genes and Genomes; DEGs, differentially expressed genes

Table 4. The GO analysis of genes in the significant module (top 5 terms according to P-value)

Category	Term	Description	Gene ID	Count	P-value
BP	GO:0070098	chemokine-mediated signaling pathway	CXCL12, CXCL8, CXCR4	3	6.23E-04
	GO:0006954	inflammatory response	CXCL12, CXCL8, IL1B, CXCR4	4	8.66E-04
	GO:0007155	cell adhesion	CXCL12, SELL, ITGAX, CD44	3	0.001507293
CC	GO:0009897	external side of plasma membrane	CXCL12, SELL, CD80, ITGAX, CD69	5	2.18E-06
CC	GO:0009986	cell surface	CD80, ITGAX, CD38, CXCR4, CD44	5	8.66E-05
note: GO, Gene Ontology; BP, biological processes; CC, cellular components

No competing interests reported.

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Potential Novel Therapeutic Targets In Epilepsy From The View of Intestinal Comorbidities - Inflammatory Bowel Disease

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Abstract

Figures

1. Introduction

2. Materials And Methods

2.1. Data Collection

2.2. Identification of Differentially Expressed Genes (DEGs)

2.3. Function Enrichment Analysis of DEGs

2.4. Gene Set Enrichment Analysis (GSEA) of Two Expression Profiles

2.5. Protein-protein interaction Network Construction, Significant Module Analysis and Candidate Hub Gene Identification

2.6. Validation of Candidate Hub Genes

3. Results

4. Discussion

Declarations

References

Tables

Additional Declarations

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