Identification of shared genes of atherosclerosis and Crohn's disease based on bioinformatics and machine learning algorithm

doi:10.21203/rs.3.rs-2977130/v1

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Identification of shared genes of atherosclerosis and Crohn's disease based on bioinformatics and machine learning algorithm

https://doi.org/10.21203/rs.3.rs-2977130/v1

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Background: Atherosclerosis (AS) is a major contributor to cardiovascular mortality and morbidity globally. There is currently a dearth of information about Crohn's disease (CD) and its relation to the risk of atherosclerotic cardiovascular disease. However, recent studies were unable to identify the biological mechanism at the genetic level.

Methods: Four microarray datasets (GSE43292, GSE28829, GSE186582, and GSE102133) were downloaded from the Gene Expression Omnibus database. The Limma package was used to identify differentially expressed genes (DEGs) in AS and CD. Functional enrichment analysis, protein-protein interaction (PPI) network construction, and machine learning algorithms were applied to identify hub genes. Then the hub genes were calculated based on the receiver operating characteristic (ROC) curve. Subsequently, we conducted single-gene GSEA analysis and immune infiltration analysis to further investigated the possible mechanism of the hub genes.

Results: A total of 24 common upregulated DEGs and 10 common downregulated DEGs were observed in AS and CD. According to enrichment analyses, these genes were connected with immune-related and inflammation-related signaling pathways. Three hub genes (IL1RN, TNFSF13B, PRDM1) were identified. The AUC of hub genes was higher than 0.7 and the AUC of the hub genes-based logistic regression model was 0.873. Single-gene GSEA analysis and immune infiltration analysis found the hub genes were associated with AS in CD patients, and there were some associations between hub genes and 22 immune cells.

Conclusion: We identified and validated 3 hub genes (IL1RN, TNFSF13B, PRDM1) as biomarkers in AS and CD. This study may provide a new perspective on the pathogenesis of AS and CD comorbidity.

atherosclerosis

Crohn's disease

inflammation

bioinformatics

machine learning algorithm

Atherosclerosis is a major contributor to cardiovascular mortality and morbidity globally. It is caused by hyperlipidemia and lipid oxidation, and it affects the whole vascular system. It is distinguished by intimal plaques, which cause ischemic heart disease, peripheral artery disease, and cerebrovascular disease(1, 2). There is mounting proof that atherosclerosis is associated with platelet and endothelial dysfunction, as well as dysfunction of the innate and adaptive immune systems. Inflammatory bowel disease (IBD) refers to a set of chronic disorders that include Crohn's disease(CD), ulcerative colitis(UC), and indeterminate colitis. These conditions all involve chronic intestinal inflammation and also exhibit signs of systemic inflammation and other extraintestinal symptoms(3). IBD is hypothesized to develop in those with genetic susceptibility as a result of intricate interactions between the environment, microbes, and immune system.

IBD has been linked to an increased risk of atherosclerosis and cardiovascular events. (4–6). CD is one of the main types of IBD and is distinguished by transmural inflammation that can be localized or patchy, and disease segments that are discontinuous (7). There is currently a dearth of information about CD and its relation to the risk of atherosclerotic cardiovascular disease. A meta-analysis of ten cohort studies in 2017 found a multifactor-adjusted link between CD and the incidence of CHD (RR 1.24; 95% CI, 1.04–1.48) (8). Moreover, a larger meta-analysis in 2018 found associations between CD and coronary heart diseases(RR 1.08; 95% CI, 1.03–1.12), myocardial infarction(RR 1.14; 95% CI, 1.03–1.26), and incident cerebrovascular disease(RR 1.27; 95% CI, 1.07–1.51)(9).

The pathogenesis of these two diseases has not been fully clarified. Chronic inflammation reactions and immune cell agglomeration may be regulated by hereditary and environmental variables(10–12). The epidemiological connection and the function of the immune system in both illnesses imply that they share pathophysiology mechanisms(13). Notably, although accumulating evidence that AS and CD are linked, the investigations took a clinical approach and were unable to identify the biological mechanism at the level of genetics.

The gene microarray technology offers fresh insights into the pathogenesis of AS and CD, and bioinformatics analysis aids in understanding the etiology from a genetic standpoint. In this study, we used integrative bioinformatics analysis combined with a machine learning algorithm to uncover common hub genes in AS and CD. This may be the initial study to identify the common biomarkers of AS and CD, which could lead to further research into the genetic basis and therapeutic strategy of AS and CD. Figure 1 illustrates the specific analysis workflow.

Data acquisition

The keywords "Crohn's disease" or "atherosclerosis" were used to locate four microarray datasets (GSE43292, GSE28829, GSE186582, and GSE102133) in the Gene Expression Omnibus (GEO) database(14). GSE43292 dataset included 32 human carotid arteries with AS and control arteries. GSE186582 included 147 intestinal mucosae with CD and 10 normal controls. GSE28829 and GSE102133 were used as the validation dataset. Table 1 summarized the information on these four datasets generated from the GEO database.

Table 1

The information of the 4 microarray datasets obtained from the GEO database.
Data source	Disease	Sample	Sample source	Group
GSE43292	AS	32 carotid plaque and 32 macroscopically intact tissue	Carotid artery	Discovery cohort
GSE28829	AS	16 advanced carotid plaque and 13 early carotid plaque patients	Carotid artery	Validation cohort
GSE186582	CD	147 Inflamed Ileum mucosa and 10 control Ileum mucosa	Ileum mucosa	Discovery cohort
GSE102133	CD	65 Inflamed Ileum mucosa and 12 control Ileum mucosa	Ileum mucosa	Validation cohort

Identification of differentially expressed genes (DEGs)

The GSE43292 and GSE186582 datasets were normalized and analyzed using the limma R package. We used adjusted p < 0.05 and |log₂FC| ≥0.58 as our screening parameters for DEGs. To further depict these DEGs, heatmaps and volcano plots were created using the R software. After obtaining the DEGs of AS and CD, the common DEGs are obtained by taking the intersection.

Functional enrichment analysis

Gene Ontology(GO)(15) provides organized, computable information concerning gene products and functions. Kyoto Encyclopedia of Genes and Genomes (KEGG)(16) is a database that has been widely used for the systematic analysis and prediction of gene functions. The GO enrichment and KEGG pathway analysis of common DEGs were conducted using the R package ‘clusterProfiler’, with q-value < 0.05 as the criteria.

Protein-protein interaction (PPI) network construction and candidate genes selection

To uncover the overarching organizing principle of the functional cell system and forecast protein-protein interaction, a PPI network is built using the string database(17).

The results of the PPI network were modularly evaluated using Cytoscape's molecular complex detection (MCODE) tool, and candidate genes were obtained. The parameters of MCODE were degree cutoff ≥ 2, node score cut-off ≥ 2, K nucleus ≥ 2, and maximum depth = 100.

Identification of potentially shared hub genes

To reduce the dimensionality of the data, the least absolute shrinkage and selection operator (LASSO) algorithm with the R package "glmnet" was used. Then the candidate genes were selected using LASSO algorithms. Simultaneously, a support vector machine-recursive feature elimination (SVM-RFE) model was constructed using the R package ‘SVM’, and the mean false positive rate was compared by 10-fold cross-validations(18). In addition, overlapping genes obtained according to both algorithms were used to determine the potentially shared hub genes. The receiver operating characteristic (ROC) curves and area under the curve(AUC) were used to determine the diagnostic potency of the hub genes. Accordingly, a hub genes-based logistic regression model was further constructed with the R package ‘glm’. Similarly, ROC curves were employed to evaluate the diagnostic potency of the logistic regression model.

Single-gene Gene Set Enrichment Analysis (GSEA) of hub genes

To further probe the possible functions of hub genes, a single-gene GSEA analysis was conducted(19). In the GSE43292 dataset, the median expression level of hub genes was used to separate the 32 AS samples into two groups. GSEA was performed using the R package 'clusterProfiler'. The KEGG signaling pathway was further regarded as a predefined gene set to examine its enrichment significance, with q-value < 0.05 as the criteria.

Immune infiltration analysis and correlation with hub genes

To quantitatively calculate the relative proportion of immune cell infiltrations, the CIBERSORT algorithm (with 1000 permutations) was applied to compute 22 immune cell composition types of AS and CD samples(20). Violin diagrams were drawn by the R package ‘ggplot2’ to visualize the differences. Moreover, we conducted a correlation analysis of immune cells and the hub genes.

Validation of hub genes in external datasets

To observe the kinetic changes of the hub genes in external datasets, expression levels of hub genes were analyzed in the GSE28829 and GSE102133 datasets.

Statistics

The statistical analysis was carried out using R software version 4.2.3 and Cytoscape version 3.8.0.

Continuous variables were compared between two groups using the Student's t-test. A p-value of less than 0.05 was considered statistically significant.

Differential expression genes identification in AS and CD

Based on the AS dataset GSE43292, 727 differential genes (DEGs) were found using the limma R package. The volcano plot depicts the detected DEGs, of which 418 are upregulated and 309 are downregulated (Fig. 2A). The heatmap demonstrates DEGs (Fig. 2C). Besides, the CD dataset GSE186582 obtained a total of 1294 DEGs, with 651 genes identified to be upregulated and 643 identified to be downregulated (Fig. 2B). Heatmaps of DEGs are shown in Fig. 2D. A total of 24 common upregulated DEGs and 10 common downregulated DEGs of GSE43292 and GSE186582 were observed (Fig. 2E-F).

Functional enrichment analysis

To further clarify the biological mechanisms and signaling pathways associated with common genes, GO and KEGG enrichment analyses were conducted. According to the GO analysis (Fig. 3A), common DEGs were considerably enriched in the following biological processes (BP): leukocyte chemotaxis(GO:0030595), positive regulation of vasculature development(GO:1904018), and leukocyte migration(GO:0050900). The cell component (CC) analysis revealed that DEGs were predominantly located in the collagen-containing extracellular matrix(GO:0062023), clathrin-coated vesicle membrane(GO:0030665), and glycoprotein complex(GO:0090665). In the molecular function(MF) category, the DEGs were mainly enriched in cytokine receptor binding(GO:0005126), signaling receptor activator activity(GO:0030546), and cytokine activity(GO:0005125). KEGG analysis(Fig. 3B) results revealed that common genes were mainly associated with cytokine-cytokine receptor interaction(hsa04060), IL-17 signaling pathway(hsa04657), Wnt signaling pathway(hsa04310), and PI3K-Akt signaling pathway(hsa04151).

PPI network construction and candidate genes selection

Using the string database, PPI analysis was conducted on these 34 common DEGs. After the removal of DEGs with poor interaction (n = 17), 17 DEGs were maintained (Fig. 4A). In addition, two important modules were selected through MCODE(Fig. 4B). CXCL8, AQP9, CSF3R, S100A8, TREM1, IL1RN, and S100A9 were the hub nodes in module A. TNFSF13B, CD69, PRDM1 ,and IL7R were the hub nodes in module B. Finally, 11 DEGs were selected using MCODE as candidate genes for further machine-learning analysis.

Identification of potentially common hub genes

Two distinct machine learning algorithms were used to select DEGs for ROC assessment. Figure 5A-B depicts the Lasso regression results. From the 11 DEGs, 3 DEGs with the lowest binomial deviation were identified. According to the SVM-RFE results, the top 7 DEGs had the best accuracy and lowest error in choosing potential biomarkers (Fig. 5C-D). Then, a Venn plot showed that 3 hub DEGs (IL1RN, PRDM1, TNFSF13B) were intersected(Fig. 5E). The details of hub genes are shown in Table 2. Next, ROC curves were prepared for 3 hub genes to evaluate the capability of individual genes to distinguish AS samples from controls. As indicated in Fig. 5F, the AUC of all hub genes was ༞0.7. Accordingly, a logistic regression model was established based on the 3 hub genes, and subsequent ROC curve analysis results showed that the AUC of the model was 0.873 (Fig. 5G).

Table 2

Functions of hub genes.
Gene symbol	Official full name	Function
IL1RN	Interleukin 1 Receptor Antagonist	The protein encoded by this gene is a member of the interleukin 1 cytokine family. This protein inhibits the activities of IL1α and IL1β and modulates a variety of IL1-related immune and inflammatory responses, particularly in the acute phase of infection and inflammation.
TNFSF13B	TNF Superfamily Member 13B	The protein encoded by this gene is a cytokine that belongs to the tumor necrosis factor (TNF) ligand family. This cytokine is expressed in B cell lineage cells and acts as a potent B cell activator. It has been also shown to play an important role in the proliferation and differentiation of B cells.
PRDM1	PR/SET Domain 1	This gene encodes a protein that acts as a repressor of beta-interferon gene expression. The protein binds specifically to the PRDI (positive regulatory domain I element) of the beta-IFN gene promoter.

Single-gene GSEA of hub genes

A single-gene GSEA analysis was carried out to further probe the possible mechanism of the hub genes. Figure 6A-C depicts the top 6 pathways that were enriched for each hub gene. The GSEA analysis showed that the 3 hub genes were found most significantly enriched in “CYTOKINE CYTOKINE RECEPTOR INTERACTION”, “T CELL RECEPTOR SIGNALING PATHWAY”, “TOLL LIKE RECEPTOR SIGNALING PATHWAY”, and “CHEMOKINE SIGNALING PATHWAY” in AS. These signature gene sets were identified as being close to the occurrence and development of CD. This showed that the 3 hub genes were linked to AS in CD patients.

Immune infiltration analysis and correlation with hub genes

As the enrichment analysis suggested that immunity plays an important role in the development of AS and CD, we evaluated whether unique patterns of immune infiltration could be detected using the CIBERSORT approach based on the 22 categories of immune cells. First, we examined the immune cell infiltration composition in the AS dataset (GSE43292) and the CD dataset (GSE186582). The Violin diagram revealed substantial changes in AS and control samples in plasma cells, CD8⁺ T cells, activated NK cells, M0 macrophages, and M2 macrophages (Fig. 7A). In the AS sample, CD8⁺ T cells and activated NK cells were much lower than in the normal sample, but plasma cells, M0 macrophages, and M2 macrophages were significantly higher. In addition, we conducted CIBERSORT in the CD dataset. Memory B cells, follicular helper T cells, M0 macrophages, and M2 macrophages were found to be decreased in patients, whereas resting memory CD4⁺ T cells, activated dendritic cells, and neutrophils were found to be increased (Fig. 7B).

However, changes in immune cell composition proportions are only one part of AS and CD's shared pathogenesis. We still need to see if these 3 hub genes are linked to immunity. As a result, Pearson correlation analysis was employed to evaluate the relationships between hub genes and immune cells in AS(Fig. 8A). We observed that IL1RN had a significant positive correlation with M0 macrophages and activated mast cells and a significant negative correlation with activated dendritic cells, resting dendritic cells, resting mast cells, monocytes, resting memory CD4 + T cells, and CD8⁺ T cells. PRDM1 had a significant positive correlation with M0 macrophages and a significant negative correlation with resting dendritic cells, and activated NK cells. TNFSF13B had a significant positive correlation with resting memory CD4 + T cells and gamma delta T cells and a significant negative correlation with regulatory T cells.

We also discovered some links between hub genes and immune cells in CD (Fig. 8B), indicating that immune cells are involved in the pathogenesis of CD.

Validation of hub genes in external datasets

Finally, we validated the expressions of the hub genes in the GSE28829 and GSE102133 datasets. The results showed that the expression tendencies of 3 hub genes were consistent with the GSE28829 dataset and GSE102133 datasets in AS and CD(Fig. 9).

It is widely documented that the etiology of AS involves an unbalanced lipid metabolism and a maladaptive immunological response resulting in chronic arterial wall inflammation(21). Growing evidence has shown that AS is related to chronic inflammation and an imbalanced immune system(1, 22, 23). Interestingly, Chronic inflammatory illnesses, such as rheumatoid arthritis, systemic lupus erythematosus, and systemic sclerosis, increase the risk of atherosclerotic events(24). CD is defined by chronic inflammation of any region of the gastrointestinal tract, as well as non-infectious systemic inflammation and hypercoagulability. Several factors, including an abnormal immune system, modified microbiota, genetic susceptibility, and environmental factors, have been implicated in the cause of CD(25). Recent research has suggested that CD patients might also be at an elevated risk of cardiovascular events(8, 9, 12, 26). Potential factors underpinning the increased risk of CVDs in IBD patients include elevated levels of inflammatory cytokines, hypercoagulability, and endothelial dysfunction(27, 28). However, these features have only been studied at the clinical or serological levels, the genetic level remains a mystery. This is the initial research to use bioinformatics analytic methodologies and machine learning algorithms to these two illnesses at the genetic level.

In this work, we utilized integrative bioinformatics analysis along with algorithms based on machine learning to locate shared hub genes in AS and CD to evaluate whether they share genetic and molecular pathways. First, we looked at common DEGs between AS and CD and discovered 34 shared potential genes. These potential genes were connected with immune-related and inflammation-related signaling pathways, according to enrichment analyses. Next, machine learning algorithms were used to further screen common hub genes. The common hub genes of AS and CD were identified as the intersection of SVM-RFE and LASSO, and the diagnostic effectiveness of each shared hub gene was further validated. IL1RN, TNFSF13B, and PRDM1 were finally screened out as hub genes and the AUC of hub genes was ༞0.7. In addition, we developed an extensive prognostic model to increase the accuracy of illness diagnostic prediction.

Moreover, to further investigated the possible mechanism of the hub genes and the correlation between hub genes with immune cells in AS and CD, we employed single-gene GSEA analysis and immune infiltration analysis and found hub genes were associated with AS in CD patients, and there were some associations between hub genes and 22 immune cells.

IL1RN is a natural IL-1 inhibitor that modulates several IL-1-related immunological and inflammatory responses(29). Interestingly, the IL1RN polymorphisms are associated with CD and coronary artery disease(30). IL-1 is mostly expressed in the endothelium of AS plaques and is controlled by IL1RN, which plays a role in the inflammatory process that leads to AS development(31). A recent study revealed that carotid intima-media thickness has been associated with IL1RN genetic variants(32). Moreover, a study found that IL1RN can be used as a biomarker of CD(33). IL1RN genetic variants were linked to anti-TNF medication responsiveness in CD patients(34).

TNFSF13B is a ligand of the TNF family that is primarily generated by adaptive immune cells, specifically B and T lymphocytes and plays a crucial role in B cell development(35). A recent study found that The high expression of TNFSF13B was also in the arteriosclerotic plaque(36). Moreover, TNFSF13B overexpression contributes to the pathogenesis of several autoimmune diseases, such as systemic sclerosis, rheumatoid arthritis, and systemic lupus erythematosus(37–39). Interstingly, TNFSF13 can be used as an inflammatory indicator for infectious diseases(40).

PRDM1 encodes a protein that functions as a regulator of beta-interferon gene expression and regulates B-cell and T-cell development(41). Furthermore, variants in the PRDM1 gene have been related to a variety of chronic inflammatory disorders in humans, including lupus erythematosus, rheumatoid arthritis, and CD(42). In a contact dermatitis illness model, the deletion of PRDM1 in the epidermis of adult mice resulted in greater inflammatory responses(43). We speculate that PRDM1 may participate in the inflammatory response in AS and CD.

Immune cells play a central role in the pathogenesis of AS and CD(44, 45). Our immune infiltrate analysis showed that a variety of immune cells were different in AS and CD, but the cell types were not consistent between the two diseases. This may be due to the different types of immune cells in different disease states. However, this does not prevent the immune cells from playing an important role in its pathogenesis.

It should be noted that our study still has some limitations. First, the sample size in our research was comparatively small, although we have compensated for this limitation by using an external validation dataset. Second, gene expression is not equal to protein expression, hence further studies are urgently required to validate the genes currently screened out in vivo and in vitro.

In summary, here we identified and validated 3 hub genes (IL1RN, TNFSF13B, PRDM1) as biomarkers in AS and CD. This study may provide a new perspective on the pathogenesis of AS and CD comorbidity.

1 Data availability statement

The datasets GSE43292, GSE28829, GSE186582, and GSE102133 for this study can be found in the Gene Expression Omnibus database.

2 Author contributions

Ye Cao: design, data analysis, and manuscript writing. Nan Xia and Bo Li: reviewing and visualization. Jian-Cheng Zhang: supervision, reviewing, and editing. All authors contributed to the article and approved the submitted version.

3 Funding

This study was supported by the National Natural Science Foundation of China (grant no. 82070341) and the Natural Science Foundation of Fujian Province of China(grant no. 2019J01189).

4 Acknowledgments

We sincerely thank the researchers for sharing their GEO database information available publicly; we are very proud to recognize their achievements.

5 Ethics approval and consent to participate

This study did not require ethical approval as publicly available data were analyzed.

6 Consent for publication

Not applicable.

7 Competing interests

The authors declare no competing interests.

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Identification of shared genes of atherosclerosis and Crohn's disease based on bioinformatics and machine learning algorithm

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2 Materials and methods

3 Results

4 Discussion

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1 Data availability statement

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4 Acknowledgments

5 Ethics approval and consent to participate

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7 Competing interests

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Additional Declarations

Status:

Version 1