Uncovering Potential Novel Biomarkers In Crohn's Disease Using Integrated Bioinformatics Analysis

doi:10.21203/rs.3.rs-1301164/v1

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Uncovering Potential Novel Biomarkers In Crohn's Disease Using Integrated Bioinformatics Analysis

https://doi.org/10.21203/rs.3.rs-1301164/v1

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Background: Crohn's disease is a chronic nonspecific intestinal inflammatory disease with unknown etiology. This study aimed to predict potential novel biomarkers of Crohn's disease.

Methods: Gene expression datasets of Crohn's disease and normal samples were downloaded from the Gene Expression Omnibus (GEO) database. First, differential gene expression analysis and weighted gene coexpression network analysis (WGCNA) were performed. Common genes (CGs) were obtained by the intersection of differentially expressed genes (DEGs) and optimal modal genes of WGCNA. Subsequently, a protein-protein interaction (PPI) network was established to screen hub genes and then establish a Crohn's disease risk prediction model based on hub genes. A receiver operating characteristic (ROC) curve, and area under the curve (AUC) were used to evaluate the prediction ability of the model. Finally, the mirTarBase database, starBase database, and TargetScan database were used to predict microRNAs (miRNAs) and transcription factors (TFs) that cause Crohn's disease.

Results: A total of 74 DEGs were identified. WGCNA showed that the signature gene in the blue module was significantly associated with Crohn's disease (p=4e-6) and obtained 32 CGs. Five hub genes (CDH17, CSF1R, CXCL10, CXCL9, COL3A1) were identified and well predicted Crohn's disease risk (AUC=0.853). Meanwhile, in the miRNA-mRNA regulatory network and TF-mRNA regulatory network, we found that 3 miRNAs (hsa-miR-29a-3p, hsa-miR-29b-3p, hsa-miR-29c-3p) and 2 TFs (TCF4, HINFP) regulate multiple CGs.

Conclusions: Five genes (CDH17, CSF1R, CXCL10, CXCL9 and COL3A1), three miRNAs (hsa-miR-29a-3p, hsa-miR-29b-3p, hsa-miR-29c-3p) and two TFs (TCF4, HINFP) may be involved in the pathogenesis of Crohn's disease.

Crohn's disease

mRNA

MicroRNAs

Transcription factors

Crohn's disease is a chronic nonspecific inflammatory disease of the gastrointestinal tract with unknown etiology, usually occurring in the distal ileum and right colon. The main clinical symptoms of the disease include abdominal pain, diarrhea, nutritional disorders, complications including intestinal obstruction, intestinal perforation and gastrointestinal bleeding, and even induced cancer(1). There is currently no cure. Traditional treatment mainly includes salicylic acid drugs, adrenal cortex hormones and immunosuppressive agents(2). However, approximately 60% of patients require surgery for refractory Crohn's disease or its complications, and recurrence rates are up to 50% within 5 years and up to 30% within 10 years(3, 4). Given the intractable nature of the disease and its serious impact on quality of life, further research has become a top priority.

MicroRNAs (miRNAs) are noncoding single-stranded RNA molecules. Through selective pairing of coding genes with miRNAs, degradation of target mRNAs or inhibition of mRNA translation can be induced to regulate gene expression(5). At present, there are few studies on the role of miRNAs in the pathogenesis of Crohn's disease. Recent studies have confirmed that miRNAs play an important role in regulating intestinal barrier homeostasis, intestinal epithelial autophagy, and inflammatory response signaling pathways by targeting certain molecules(6). In addition, Muthusami et al. clarified that miRNAs can promote various inflammatory factors leading to the carcinogenesis of inflammatory bowel disease(7). Therefore, miRNAs may play an important role in the pathogenesis of Crohn's disease. Transcription factor (TF) is a protein that binds to a particular DNA sequence and regulates gene expression by increasing or blocking the recruitment of a particular gene to RNA polymers(8). Malregulated transcription factors are involved in the occurrence of many diseases, from cardiovascular diseases, diabetes, and inflammatory diseases to many cancers(9). Hence, it is of great significance to study the relationship between transcription factors and Crohn's disease.

Until now, no comprehensive analysis of target genes, miRNAs, and TFs in Crohn's disease has been reported. This study downloaded pathological bowel mucosal tissue of Crohn's disease and normal intestinal tissue mRNA data sets from the Gene Expression Omnibus (GEO) database to explore the pathogenesis of Crohn’s disease through comprehensive bioinformatics analysis, and screened for biomarkers that may have diagnostic and therapeutic value.

Identify differentially expressed genes

The statistical analysis of this study was completed by R version 4.1.0 (http://www.r-project.org). P <0.05 on both sides was considered statistically significant. The GEO database (https://www.ncbi.nlm.nih.gov/geo/) was used to download samples of diseased intestinal mucosa from Crohn's disease and normal intestinal mucosa sample gene expression data sets for data analysis(10). Data preprocessing was performed on each dataset using the "limma" and "impute" packages, including conversion of probe names to gene names, missing value filling, data normalization, and log2 transformation(11). When the same gene corresponds to multiple probes, the average value is taken as the gene expression value. Combine preprocessed data sets into a data set. The "sva" package was used to eliminate batch effects between data sets(12). The genes satisfying FDR <0.05 and |log2 fold change (FC)| >0.5 were selected through the "limma" package, and these genes were designated differentially expressed genes (DEGs)(13).

Identify common genes

Weighted gene coexpression network analysis (WGCNA) was used to explore the interaction between DEGs in the integrated data set. The gene coexpression network was constructed by the "WGCNA" package. First, genes with more than 25% variation between samples were introduced into WGCNA, and then outliers were eliminated. The second step is to use the pickSoftThreshold function to calculate the soft threshold, and RsquaredCut is set to 0.9. Choose the best soft threshold, calculate the adjacency matrix, convert the adjacency matrix into a topological overlap matrix (TOM) and calculate the degree of dissimilarity between genes. The third step was to divide the gene modules using the dynamic shear tree, set the minimum gene module size to 50, and then merge the modules with dissimilarity coefficients less than 0.2(14). The fourth step is to select modules related to clinical traits, calculate the relationship between genes and traits and modules, and then visualize the characteristic gene network. The intersection of genes between DEGs in the integrated data set and genes in important modules are defined as common genes (CGs).

Function analysis and prediction model establishment

CGs were uploaded to the DAVID database (https://david.ncifcrf.gov/tools.jsp) and the KOBAS database (http://kobas.cbi.pku.edu.cn/genelist/), the significantly enriched Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis results were exported, and the results were visualized(15, 16). In addition, CGs were imported into the STRING database (https://www.string-db.org/)(17), protein-protein interaction (PPI) networks were built, the minimum required interaction score was set to 0.4, the results were exported, and Cytoscape Version 3.8.2 was used to visualize the PPI network(18). The CytoHubba plug-in in Cytoscape was used to calculate the degree of each CG node and screen out the top 5 hub genes. The "rms" package was used to draw the nomogram of the model for predicting Crohn's disease risk based on 5 hub genes. Receiver operating characteristic (ROC) curves were used to evaluate the prediction accuracy of the model. In addition, the association between CGs of the PPI network and immune cells and immune pathways was visualized using the "psych" package and "ggcorrplot" package.

Prediction of miRNA and transcription factors

The mirTarBase, starBase and TargetScan databases were used to predict the miRNAs of CGs(19-21). The miRNAs obtained from the three databases were intersected to obtain the target miRNAs, and the miRNA-mRNA regulatory network was obtained. On the other hand, CGs were imported into the Enrichr database (https://maayanlab.cloud/Enrichr/), and transcription factors (TFs) targeting CGs with p<0.05 were screened out to obtain the TF-mRNA regulatory network. Cytoscape was used to visualize the above two regulatory networks.

Identification of DEGs

We downloaded a total of 9 datasets (GSE1710, GSE6731, GSE20881, GSE26305, GSE36807, GSE59071, GSE69762, GSE75214, GSE179285) from the GEO database. By analyzing the expression levels of genes in the above data sets, 74 DEGs were obtained, of which 34 were upregulated and 40 were downregulated.

Identification of gene coexpression networks and modules

First, 25% (1076) of the genes with the largest variance were extracted for subsequent analysis. Second, the threshold was set to 50 for cluster analysis, and 4 abnormal samples were deleted. Third, R2 was set to 0.9, and the best soft threshold was 6. Fourth, genes with a dissimilarity coefficient less than 0.2 were combined to obtain 5 modules, and the genes in each module had similar coexpression traits (Figure 1A). The 5 modules were randomly distinguished by color. The characteristic gene (ME) in the blue module (r = 0.36; p=4e-6) showed the highest positive correlation and the most significant correlation with Crohn's disease (Figure 1B). Meanwhile, we found that there was a significant positive relationship between the module members of the genes (MMs, the correlation between specific genes and the characteristic genes of the module) in the blue module and the gene significance (GS, the correlation between specific genes and clinical variables). Significant correlation (cor=0.46, P= 3.5e-32), as shown in Figure 1C. Finally, the DEGs of the integrated data set and the genes in the blue module intersected to obtain CGs (Figure 1D). Furthermore, the blue module was determined to be the key module of Crohn's disease, and the 589 genes contained in the module were used for the next analysis.

Enrichment analyses and PPI network

In biological processes (BP), CGs were mainly enriched in "immune response", "positive regulation of cAMP metabolic process", "transforming growth factor beta receptor signaling pathway", "positive regulation of cAMP-mediated signaling" and "positive regulation of monocyte chemotaxis". CGs are mainly involved in the cell component (CC) ontology, including "proteinaceous extracellular matrix", "extracellular space" and "extracellular region". In molecular function (MF), CGs were mainly enriched in "CXCR3 chemokine receptor binding" and "protein homodimerization activity". In addition, the enrichment pathways of KEGG included "Viral protein interaction with cytokine and cytokine receptor", "Cytokine-cytokine receptor interaction", "Protein digestion and absorption", "Hematopoietic cell lineage" and "Toll-like receptor signaling pathway". All enrichment pathways are shown in Figure 2A. After removing isolated nodes, a PPI network consisting of 13 CGs was constructed, as shown Figure 2B. Compared with the degree of CG nodes in the PPI network, CDH17, CSF1R, CXCL10, CXCL9 and COL3A1 were identified as hub genes.

Immune characteristics of CGs in the PPI network and prediction model

Figure 3 shows the correlation between CGs of the PPI network and immune cells and immune pathways. The expression levels of CXCL9, MMP12 and MNDA were positively correlated with the infiltration of activated dendritic cells. MNDA expression was positively correlated with CCR pathway activation. The expression levels of CXCL9, MMP12, MNDA and OLFM4 were positively correlated with HLA pathway activation, while CDH17, SLC4A4 and COL17A1 were negatively correlated. Meanwhile, OLFM4 expression promotes the activation of the MHC-class-I pathway. In addition, CXCL10 expression can accelerate the activation of the inflammatory promoting pathway and parainflammation pathway. Figure 4A shows the nomogram of the model for Crohn's disease risk prediction based on hub genes. The AUC curve (AUC=0.853) indicated that the model had good prediction accuracy (Figure 4B).

TF‐mRNA and miRNA‐mRNA regulatory network analysis

The constructed miRNA-mRNA regulatory network contains 12 miRNAs and 11 CGs (Figure 5A). Among them, hsa-miR-29a-3p, hsa-miR-29b-3p, and hsa-miR-29c-3p regulate multiple CGs. The TF-mRNA regulatory network contains 6 TFs, and 25 CGs (Figure 5B). Among them, TCF4 and HINFP regulate multiple CGs.

This study combines microarray technology with comprehensive bioinformatics analysis to explore the possible pathogenesis and biomarkers of Crohn's disease. After comparing the gene expression levels of the Crohn's disease group and the control group, we predicted 74 possible EDGs for Crohn's disease. WGCNA shows that the characteristic genes in the blue module are significantly related to Crohn’s disease. By intersecting the genes in the blue module with DEGs, 32 CGs were obtained. To further evaluate the diagnostic value of these CGs, five hub genes (CDH17, CSF1R, CXCL10, CXCL9 and COL3A1) were identified according to the degree of nodes in the PPI network, and a prediction model of Crohn's disease risk based on HUB genes was established. The ROC curve indicates that the model has good prediction accuracy. Meanwhile, this study predicted that 3 miRNAs (hsa-miR-29a-3p, hsa-miR-29b-3p, hsa-miR-29c-3p) and 2 TFs(TCF4, HINFP) might be involved in the pathogenesis of Crohn's disease.

Hub genes have received wide attention as possible targets of potential drug action. In this study, 5 hub genes (CXCL9, CXCL10, CSF1R, CDH17, COL3A1) may play an important role in the pathogenesis of Crohn’s disease. C-X-C motif chemokine ligand 9 (CXCL9) belongs to the T cell chemoattractant in the CXC chemokine family. It is involved in the growth, movement and activation of immune and inflammatory cells, as well as the chemotaxis of activated T cells(22). A study by Elia et al. showed that the recruitment of monocytes and granulocytes by CXCL9 plays an important role in maintaining the inflammatory state of ulcerative colitis(23). A recent study(24) suggests that CXCL9 overexpression may be related to the inflammatory state of Crohn’s disease, which is the same as our findings. CXC motif chemokine ligand 10 (CXCL10) is induced by interferon γ to stimulate the secretion of monocytes, fibroblasts, natural killer cells, endothelial cells and dendritic cells(25). It mainly binds specifically to Th1 cells to chemoattract inflammatory factors, and mediate the migration and aggregation of immune cells(26). This research suggests that CXCL10 is significantly expressed at lower levels in Crohn's disease tissues. It is worth noting that the expression of CXCL10 in the diseased tissues of patients who fail to respond to treatment of Crohn’s disease with monoclonal antibodies against tumor necrosis factor-α (TNFα) is elevated(27). Therefore, the high expression of CXCL10 may be related to the drug resistance mechanism of Crohn's disease. The protein encoded by colony stimulating factor 1 receptor (CSF1R) is the colony stimulating factor 1 receptor, which regulates the proliferation, differentiation and function of macrophages(28). Previous studies confirmed that CSF1R was highly expressed in inflammatory bowel disease, which is consistent with our research results(29, 30). Zapata-Velandia et al. speculated that CSF1R may be a susceptibility gene for Crohn's disease(31). The latest research shows that patients with inflammatory bowel disease may benefit from CSF1R inhibitors(32). Cadherin-17(CDH17) is a calcium-dependent transmembrane glycoprotein involved in cell-to-cell adhesion in the intestinal epithelium(33). Currently, CDH17 has been confirmed as a diagnostic marker for gastrointestinal adenocarcinoma(34, 35). However, whether the expression of CDH17 is related to Crohn's disease has not been reported. Fortunately, a previous study demonstrated that CDH17 is involved in pathways associated with antigen presentation and immune response(36). This is also reflected in the results of this study. Therefore, CDH17 may be a potential marker of Crohn's disease. Collagen type III alpha 1 chain (COL3A1) encodes a type of fibrous collagen that is widely found in expandable connective tissues such as the skin, gastrointestinal tract and vascular system(37). Chokr et al. found that COL3A1 is involved in intestinal fibrosis leading to the progression of Crohn's disease(38). Therefore, COL3A1 may be a therapeutic target to delay the progression of Crohn's disease.

To date, the relationship between hsa-miR-29a-3p, hsa-miR-29b-3p, hsa-miR-29c-3p and inflammatory bowel disease has not been reported. Notably, hsa-miR-29a-3p and hsa-miR-29b-3p are considered to be important predictors of miRNA in osteoarthritis(39). Moniri et al. speculated that hsa-miR-29b-3p and hsa-miR-29c-3p might play an important role in wound healing(40). The circulating base level of hsa-miR-29b-3p was significantly associated with metastasis and prognosis of colorectal cancer(41, 42). In addition, circulating hsa-miR-29c-3p can be used as a miRNA to predict acute cell rejection after cardiac transplantation(43).

Crohn's disease has been associated with reduced expression of the transcription factor T cell factor 4 (TCF4) in the Wnt signaling pathway in several studies(44-46). Histone nuclear factor P (HINFP) is a transcription factor that regulates the expression of the histone H4 gene and plays an important role in normal cell cycle control and proliferation(47). This study suggests that HINFP may be involved in the pathogenesis of Crohn's disease.

This study provides a reasonable prediction of possible biomarkers for Crohn's disease. However, there are still shortcomings in this study; for example, the above research results have not been further verified in this study. It is well known that inflammatory cell infiltration and abnormal activation of immune pathways are the pathological features of Crohn's disease. It is necessary to further explore the relationship between the biomarkers predicted in this study and the immune characteristics of Crohn's disease.

Five genes (CDH17, CSF1R, CXCL10, CXCL9 and COL3A1), three miRNAs (hsa-miR-29a-3p, hsa-miR-29b-3p, hsa-miR-29c-3p) and two TFs (TCF4, HINFP) may be involved in the pathogenesis of Crohn's disease. The prediction model based on five hub genes can well predict the risk of Crohn's disease.

Ethical Approval and Consent to participate

The Ethics Committee of The Central Hospital of Yongzhou reviewed the study and deemed ethics approval was not necessary.

Consent for publication

All authors agree to publish.

Availability of supporting data

GEO database belong to public databases. The patients involved in the database have obtained ethical approval. Users can download relevant data for free for research and publish relevant articles. Our study is based on open-source data, so there are no ethical issues and other conflicts of interest.

Competing interests

No rights disclosure.

Funding

No funding was received.

Authors' contributions

Jinggao Feng participates in concept and design, data screening and analysis, manuscript drafting

Acknowledgments

We acknowledge GEO database for providing their platforms and contributors for uploading their meaningful datasets.

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No competing interests reported.

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Uncovering Potential Novel Biomarkers In Crohn's Disease Using Integrated Bioinformatics Analysis

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