# Identification of the Potential Molecular Mechanism ofTGFBI Gene in Persistent Atrial Fibrillation

Authors: Yao-Zong Guan; Hao Liu; Huan-Jie Huang; Dong-Yan Liang; Si-Ying Wu; Tang Zhang
Journal: Computational and Mathematical Methods in Medicine
 (2022)

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## Abstract

Background. Transforming growth factor beta-induced protein (TGFBI, encoded by TGFBI gene), is an extracellular matrix protein, widely expressed in variety of tissues. It binds to collagens type I, II, and IV and plays important roles in the interactions of cell with cell, collagen, and matrix. It has been reported to be associated with myocardial fibrosis, and the latter is an important pathophysiologyical basis of atrial fibrillation (AF). However, the mechanism of TGFBI in AF remains unclear. We aimed to detect the potential mechanism of TGFBI in AF via bioinformatics analysis. Methods. The microarray dataset of GSE115574 was examined to detect the genes coexpressed with TGFBI from 14 left atrial tissue samples of AF patients. TGFBI coexpression genes were then screened using the R package. Using online analytical tools, we determined the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, Gene Ontology (GO) annotation, and protein-protein interaction (PPI) network of TGFBI and its coexpression genes. The modules and hub genes of the PPI-network were then identified. Another dataset, GSE79768 was examined to verify the hub genes. DrugBank was used to detect the potential target drugs. Results. In GSE115574 dataset, a total of 1818 coexpression genes (769 positive and 1049 negative) were identified, enriched in 120 biological processes (BP), 38 cellular components (CC), 36 molecular functions (MF), and 39 KEGG pathways. A PPI-network with average 12.2-degree nodes was constructed. The genes clustered in the top module constructed from this network mainly play a role in PI3K-Akt signaling pathway, viral myocarditis, inflammatory bowel disease, and platelet activation. CXCL12, C3, FN1, COL1A2, ACTB, VCAM1, and MMP2 were identified and finally verified as the hub genes, mainly enriched in pathways like leukocyte transendothelial migration, PI3K-Akt signaling pathway, viral myocarditis, rheumatoid arthritis, and platelet activation. Pegcetacoplan, ocriplasmin, and carvedilol were the potential target drugs. Conclusions. We used microdataset to identify the potential functions and mechanisms of the TGFBI and its coexpression genes in AF patients. Our findings suggest that CXCL12, C3, FN1, COL1A2, ACTB, VCAM1, and MMP2 may be the hub genes.


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## Body

## 1. Introduction

Atrial fibrillation (AF) is a complex arrhythmia, which makes about 5.2 million people suffering palpitations in the US until 2010 and would increase to 12.1 million in 2030 [1], owning to 25-30% of ischemic stroke [2]. In addition, persistent atrial fibrillation leads to mitral regurgitation and decreases up to 25% of left heart ejection function, aggravating heart failure even results in death [3]. It is estimated that around 6-12 million people will suffer this condition in the US by 2050 and 17.9 million people in Europe by 2060 [4, 5]. In China, it is estimated that the lifetime risk of AF is approximately 1 to 5 [6]. Populations of AF bring great economic burden to patients and countries worldwide. Data from previous researches shows that, in 18-45 years old AF patients, the mean cost of AF management in hospitalization was $7924 in 2015 [7]. To make matters worse, the prevalence of AF and the economic burden is still at a high level. To date, AF is considered as a disorder of electrical activity in the atrium, and the drive center probably locates in the left atrium-pulmonary vein junction. Although catheter ablation and balloon cryoablation based on circumpulmonary vein electrical dissection isolation show an exciting effect, but the mechanism of AF remains unclear.Transforming growth factor beta-induced protein (TGFBI, HGNC: 11771, Ensembl: ENSG00000120708, MIM: 601692), also known as CSD, CDB1, EBMD, and CDGG1, encodes an RGD-containing protein that binds to type I, II, and IV collagens. In an early study, Li et al. [8] reported that the expression of TGFBI can be upregulated by stimulated by TGF-beta, activating the TGF-beta BMP signaling pathway and induced the differentiation of bone marrow stem cells into immature cardiomyocytes. A recent research confirmed that TGFBI is a candidate marker for human cardiac fibroblasts in vivo and in vitro [9]. In addition, Chen et al. [10] reported that TGFBI is a target of microRNA-21, which plays a role in the regulation of fibrosis. In miR-21 knockdown cells, TGFBI was significantly upregulated, which promoted the formation of fibrosis. It is well known that AF is an age-related disease, and atrial fibrosis has emerged as an important pathophysiological contributor in aging, and has been linked to recurrences and complications of AF [11]. Thus, TGFBI is likely to play a role in the process of AF. However, little is known of the clear mechanism of TGFBI in AF.In the near decades, microarray-sequencing technology has rapidly developed and has significantly promoted the improvement of basic and clinical medicine. The Gene Expression Omnibus (GEO) database is a large repository, integrated with a series of high-throughput microarray and next-generation sequence functional genomic datasets, and is free for global researchers [12]. Up to now, GEO database have helped numerous researchers to identify key mechanisms and hub targets of cardiovascular diseases, tumors, and other diseases [13–15]. The regulation of a pathway often involves several genes, and there are coexpression relationships among these genes. In the current study, we aimed to further understand the mechanism of TGFBI in AF patients by detecting the TGFBI and its coexpression genes and their pathways enriched in AF patients.


## 2. Materials and Methods

### 2.1. Study Design and Dataset Selection

Genes with similar functions or involved in the same pathways often have coexpression relationships. In order to detect the mechanism ofTGFBI in AF, we aimed to screen out the genes with coexpression relationship to TGFBI for analysis. Indeed, it was similar to the analysis of the subanalysis of the coexpression of blocks in weighted correlation network analysis (WGCNA) [16]. The difference was that WGCNA screens coexpression from differentially expressed genes or RNAs, but the central idea of our study was to take TGFBI as the central gene and combine its coexpressed genes to detect their biological functions, cellular localization, and enrichment pathways in AF. The cor function in R package can be used to detect the correlation coefficient between two variables (https://stat.ethz.ch/R-manual/R-devel/library/stats/html/cor.html). The code for the cor function is as follows: cor(x, y=NULL, use = “everything,” method=c (“pearson,” “kendall,” “spearman”)). The annotation information of the code was as follows: x: a numeric vector, matrix, or data frame; y: NULL (default) or a vector, matrix, or data frame with compatible dimensions to x. The default is equivalent to y=x (but more efficient); use an optional character string giving a method for computing covariances in the presence of missing values. This must be (an abbreviation of) one of the strings “everything,” “all.obs,” “complete.obs,” “na.or.complete,” or “pairwise.complete.obs”; method: a character string indicating which correlation coefficient (or covariance) is to be computed. One of “pearson” (default), “kendall,” or “spearman” can be abbreviated. In prior study, we used this method to obtain the mechanism of NPPB and its coexpressed genes in different patients with heart failure [17]. Zhang et al. [18] also used cor function to analysed the correlation ship between the methylation levels and expression levels of the differentially methylated protein-coding genes for construction a nomogram survival model of the lung squamous cell carcinoma.We selected one microarray dataset for analysis, and another for validation. The microarray dataset GSE115574 was retrieved from the GEO database. This dataset contained 14 left atrial tissue samples from persistent AF patients [19]. The expression of all genes in every sample is shown in Figure 1, and no significant outlier samples were found. Therefore, the included samples could be used for further analysis. Another dataset GSE79768, containing 7 left atrial specimens from persistent AF patients, was retrieved from the GEO database for validation. The flowchart is shown in Figure 1. The further analysis were performed via the software RStudio (based on R package, version 3.6.4), on the platform of Windows 10 system (64-bit).Figure 1
Flowchart of the current study. Orange box presents the expression of genes in every sample of GSE114457. DAVID, the Database for Annotation, Visualization, and Integrated Discovery.


### 2.2. Identification of TGFBI Coexpression Genes and Pathway Enrichment Analyses

A screening of coexpression genes forTGFBI from the samples was performed by the cor function in R (version 3.6.4). In our study, the code was set as: cor(x, y, method=pearson), x and y represent the expression levels of TGFBI and other genes, respectively. The strength of the correlation was represented by the calculated correlation coefficient. Screening criteria were as follows: P<0.05 and Pearsoncorrelationcoefficient≥0.6. The online database, the Database for Annotation, Visualization, and Integrated Discovery (DAVID, version 6.8), was used for GO and KEGG enrichment analyses [20–22]. P value of <0.05 was set as significance. The ggplot2 package was used for visualization of the results in R (version 3.6.4).


### 2.3. Integration of the PPI-Network

The STRING (version 10.5) database was used for evaluating the interactions among the coexpression genes, and a combined interaction scoreof>0.4 was set as significant [23]. In addition, the top 10 hub genes were identified used Cytoscape plugin cytoHubba (version 0.1) with the degree ratio ranking method. Furthermore, the MCODE and ClueGO apps in Cytoscape were used to identify the modules, namely the GO annotation and KEGG pathway enrichment analyses, respectively, of the PPI-network [24].


## 2.1. Study Design and Dataset Selection

Genes with similar functions or involved in the same pathways often have coexpression relationships. In order to detect the mechanism ofTGFBI in AF, we aimed to screen out the genes with coexpression relationship to TGFBI for analysis. Indeed, it was similar to the analysis of the subanalysis of the coexpression of blocks in weighted correlation network analysis (WGCNA) [16]. The difference was that WGCNA screens coexpression from differentially expressed genes or RNAs, but the central idea of our study was to take TGFBI as the central gene and combine its coexpressed genes to detect their biological functions, cellular localization, and enrichment pathways in AF. The cor function in R package can be used to detect the correlation coefficient between two variables (https://stat.ethz.ch/R-manual/R-devel/library/stats/html/cor.html). The code for the cor function is as follows: cor(x, y=NULL, use = “everything,” method=c (“pearson,” “kendall,” “spearman”)). The annotation information of the code was as follows: x: a numeric vector, matrix, or data frame; y: NULL (default) or a vector, matrix, or data frame with compatible dimensions to x. The default is equivalent to y=x (but more efficient); use an optional character string giving a method for computing covariances in the presence of missing values. This must be (an abbreviation of) one of the strings “everything,” “all.obs,” “complete.obs,” “na.or.complete,” or “pairwise.complete.obs”; method: a character string indicating which correlation coefficient (or covariance) is to be computed. One of “pearson” (default), “kendall,” or “spearman” can be abbreviated. In prior study, we used this method to obtain the mechanism of NPPB and its coexpressed genes in different patients with heart failure [17]. Zhang et al. [18] also used cor function to analysed the correlation ship between the methylation levels and expression levels of the differentially methylated protein-coding genes for construction a nomogram survival model of the lung squamous cell carcinoma.We selected one microarray dataset for analysis, and another for validation. The microarray dataset GSE115574 was retrieved from the GEO database. This dataset contained 14 left atrial tissue samples from persistent AF patients [19]. The expression of all genes in every sample is shown in Figure 1, and no significant outlier samples were found. Therefore, the included samples could be used for further analysis. Another dataset GSE79768, containing 7 left atrial specimens from persistent AF patients, was retrieved from the GEO database for validation. The flowchart is shown in Figure 1. The further analysis were performed via the software RStudio (based on R package, version 3.6.4), on the platform of Windows 10 system (64-bit).Figure 1
Flowchart of the current study. Orange box presents the expression of genes in every sample of GSE114457. DAVID, the Database for Annotation, Visualization, and Integrated Discovery.


## 2.2. Identification of TGFBI Coexpression Genes and Pathway Enrichment Analyses

A screening of coexpression genes forTGFBI from the samples was performed by the cor function in R (version 3.6.4). In our study, the code was set as: cor(x, y, method=pearson), x and y represent the expression levels of TGFBI and other genes, respectively. The strength of the correlation was represented by the calculated correlation coefficient. Screening criteria were as follows: P<0.05 and Pearsoncorrelationcoefficient≥0.6. The online database, the Database for Annotation, Visualization, and Integrated Discovery (DAVID, version 6.8), was used for GO and KEGG enrichment analyses [20–22]. P value of <0.05 was set as significance. The ggplot2 package was used for visualization of the results in R (version 3.6.4).


## 2.3. Integration of the PPI-Network

The STRING (version 10.5) database was used for evaluating the interactions among the coexpression genes, and a combined interaction scoreof>0.4 was set as significant [23]. In addition, the top 10 hub genes were identified used Cytoscape plugin cytoHubba (version 0.1) with the degree ratio ranking method. Furthermore, the MCODE and ClueGO apps in Cytoscape were used to identify the modules, namely the GO annotation and KEGG pathway enrichment analyses, respectively, of the PPI-network [24].


## 3. Results

### 3.1. Identification of TGFBI Coexpression Genes and Pathway Enrichment Analyses

A total of 1049 negatively coexpressed genes and 769 positively coexpressed genes were identified after cor analysis. These genes were enriched in a series of BPs, such as collagen fibril organization (GO:0030199), collagen catabolic process (GO:0030574), cell adhesion (GO:0007155), and angiogenesis (GO:0001525); 38 CCs, such as collagen trimer (GO:0005581), proteinaceous extracellular matrix (GO:0005578), and basement membrane (GO:0005604); 36 MFs, such as collagen binding (GO:0005518), receptor binding (GO:0005102), and peptide antigen binding (GO:0042605). As shown in Figure2(a), the top 30 GOs were selected for visualization, and the whole information of the GO results were shown in Table S1.Figure 2
Go annotation and KEGG pathway ofTGFBI and its coexpression genes in atrial fibrillation. (a) Go annotation: the strip represents the value of −log10P of GO term. (b) KEGG pathway: the size of ball represents the count of enriched genes, and the color of ball represents the value of −log10P of pathway.
(a)(b)In addition, a total of 39 KEGG pathways were identified, such as antigen processing and presentation (hsa04612), cell adhesion molecules (CAMs, hsa04514), viral myocarditis (hsa05416), and PI3K-Akt signaling pathway (hsa04151). The visualization of these KEGG pathways is shown in Figure2(b), and the whole information of these pathways is shown in Table 1.Table 1
Pathways enriched byTGFBI and its coexpression genes in AF patients.
TermCountP valueEnriched geneshsa05332: Graft versus host disease139.14E-08HLA-DQB1/HLA-A/HLA-C/HLA-B/HLA-DMB/HLA-E/HLA-DMA/HLA-G/HLA-F/HLA-DPA1/HLA-DPB1/CD28/HLA-DRAhsa04612: Antigen processing and presentation191.13E-07HLA-DQB1/PDIA3/KIR2DS5/HLA-A/HLA-C/HLA-B/KIR2DS3/HLA-DMB/HLA-E/HLA-DMA/RFXANK/CD74/HLA-G/HLA-F/CD4/HLA-DPA1/HLA-DPB1/KIR2DL3/HLA-DRAhsa05150:Staphylococcus aureus infection161.37E-07HLA-DQB1/C3AR1/C3/C1R/ITGB2/C1S/HLA-DMB/HLA-DMA/C1QA/C1QB/FCGR2B/HLA-DPA1/FCGR2A/CFI/HLA-DPB1/HLA-DRAhsa04514: Cell adhesion molecules (CAMs)262.13E-07CLDN8/CLDN16/HLA-DQB1/ITGB2/HLA-DMB/HLA-DMA/CDH5/CLDN14/SDC3/VCAM1/CD4/HLA-DPB1/CD28/ICAM2/HLA-A/CD99/HLA-C/CLDN20/HLA-B/HLA-E/HLA-G/HLA-F/NCAM2/VCAN/HLA-DPA1/HLA-DRAhsa04940: Type I diabetes mellitus142.35E-07HLA-DQB1/HLA-A/HLA-C/HLA-B/HLA-DMB/HLA-E/HLA-DMA/HLA-G/HLA-F/CPE/HLA-DPA1/HLA-DPB1/CD28/HLA-DRAhsa05330: Allograft rejection133.89E-07HLA-DQB1/HLA-A/HLA-C/HLA-B/HLA-DMB/HLA-E/HLA-DMA/HLA-G/HLA-F/HLA-DPA1/HLA-DPB1/CD28/HLA-DRAhsa05320: Autoimmune thyroid disease155.51E-07HLA-DQB1/HLA-A/HLA-C/HLA-B/HLA-DMB/HLA-E/HLA-DMA/HLA-G/HLA-F/IFNA4/HLA-DPA1/IFNA14/HLA-DPB1/HLA-DRA/CD28hsa04145: Phagosome266.30E-07HLA-DQB1/C3/C1R/ITGB2/HLA-DMB/HLA-DMA/STX12/HLA-DPB1/THBS2/TUBA1B/ACTB/MRC1/NCF2/HLA-A/COLEC12/HLA-C/HLA-B/HLA-E/HLA-G/HLA-F/FCGR2B/CD209/HLA-DPA1/FCGR2A/CD14/HLA-DRAhsa05416: Viral myocarditis151.85E-06ACTB/HLA-DQB1/HLA-A/ITGB2/HLA-C/HLA-B/HLA-DMB/HLA-E/HLA-DMA/HLA-G/HLA-F/HLA-DPA1/HLA-DPB1/HLA-DRA/CD28hsa04512: ECM-receptor interaction171.93E-05COL4A2/COL4A1/COL3A1/ITGA1/COL5A2/VWF/LAMA4/LAMA3/GP6/CD44/COL6A5/COL6A3/COL1A2/COL6A1/LAMB1/THBS2/FN1hsa04610: Complement and coagulation cascades148.99E-05F13B/C1QA/C7/VWF/C3AR1/C1QB/THBD/SERPINF2/C3/TFPI/SERPING1/C1R/CFI/C1Shsa05152: Tuberculosis233.06E-04MRC1/HLA-DQB1/SPHK2/C3/CEBPG/TIRAP/ITGB2/HLA-DMB/HLA-DMA/RFXANK/CD74/TNFRSF1A/FCGR2B/CD209/IFNA4/FCER1G/HLA-DPA1/IFNA14/FCGR2A/PPP3CA/HLA-DPB1/CD14/HLA-DRAhsa05133: Pertussis138.02E-04C1QA/C1QB/GNAI3/C3/IRF1/TIRAP/PYCARD/ITGB2/SERPING1/C1R/C1S/CASP1/CD14hsa05166: HTLV-I infection288.39E-04HLA-DQB1/WNT16/NRP1/ADCY8/ITGB2/HLA-DMB/HLA-DMA/VCAM1/TNFRSF1A/CDKN2A/PPP3CA/HLA-DPB1/EGR1/FZD9/IL2RB/LTBR/HLA-A/HLA-C/HLA-B/HLA-E/FZD4/HLA-G/HLA-F/ETS2/PDGFRA/PDGFRB/HLA-DPA1/HLA-DRAhsa04672: Intestinal immune network for IgA production109.65E-04HLA-DQB1/LTBR/HLA-DPA1/TNFSF13/HLA-DPB1/HLA-DMB/HLA-DMA/CXCL12/HLA-DRA/CD28hsa04974: Protein digestion and absorption141.07E-03COL4A2/COL14A1/COL4A1/COL7A1/COL6A5/COL3A1/COL6A3/PRCP/COL1A2/COL15A1/COL6A1/CPB1/COL5A2/SLC7A7hsa04670: Leukocyte transendothelial migration161.68E-03ACTB/CLDN16/CLDN8/MYL7/GNAI3/NCF2/CD99/ITGB2/CLDN20/MMP2/CXCL12/CLDN14/CDH5/VCAM1/RAP1B/MSNhsa04151: PI3K-Akt signaling pathway332.83E-03FGFR1/FGFR4/COL3A1/GNG11/LPAR1/G6PC2/COL6A5/IL4R/IFNA4/COL6A3/CREB3L2/COL6A1/LAMB1/THBS2/CSF1R/FN1/IL2RB/COL4A2/COL4A1/ITGA1/FGF21/FGF20/COL5A2/VWF/GNGT1/LAMA4/LAMA3/GNB1/LPAR6/COL1A2/PDGFRA/PDGFRB/IFNA14hsa04810: Regulation of actin cytoskeleton233.00E-03ACTB/FGFR1/MYL7/FGFR4/ARHGEF7/ITGA1/IQGAP2/ITGB2/ARPC5/FGF21/FGF20/IQGAP1/ARPC1B/PAK3/ARPC2/TIAM1/SCIN/PDGFRA/PDGFRB/CYFIP1/TMSB4X/MSN/FN1hsa05322: Systemic lupus erythematosus173.01E-03HLA-DQB1/C7/HIST1H4L/C3/GRIN2A/C1R/C1S/H2AFJ/HLA-DMB/HLA-DMA/C1QA/C1QB/HLA-DPA1/FCGR2A/HLA-DPB1/HLA-DRA/CD28hsa05200: Pathways in cancer363.61E-03DCC/FGFR1/WNT16/GNAI3/ADCY8/GNG11/LPAR1/MMP2/CXCL12/CDKN2A/LAMB1/TRAF5/TRAF4/CSF1R/FN1/FZD9/BMP4/PTGER2/COL4A2/COL4A1/PTGER4/EPAS1/LEF1/FGF21/FGF20/MECOM/FZD4/GNGT1/LAMA4/LAMA3/GNAQ/GNB1/LPAR6/RASSF1/PDGFRA/PDGFRBhsa04510: Focal adhesion225.09E-03ACTB/MYL7/COL4A2/COL4A1/COL3A1/ITGA1/FLNB/COL5A2/VWF/LAMA4/LAMA3/PAK3/COL6A5/COL6A3/COL1A2/PDGFRA/COL6A1/PDGFRB/RAP1B/LAMB1/THBS2/FN1hsa05310: Asthma75.44E-03HLA-DQB1/FCER1G/HLA-DPA1/HLA-DPB1/HLA-DMB/HLA-DMA/HLA-DRAhsa00590: Arachidonic acid metabolism106.18E-03AKR1C3/GGT5/PLA2G4A/PTGIS/ALOX12B/GGT1/ALOX5/CBR3/LTC4S/PLA2G2Fhsa04611: Platelet activation151.30E-02ACTB/GNAI3/ADCY8/COL3A1/COL5A2/VWF/PLA2G4A/GP6/GNAQ/VAMP8/COL1A2/FCER1G/RAP1B/FCGR2A/LCP2hsa05140: Leishmaniasis101.63E-02HLA-DQB1/NCF2/C3/HLA-DPA1/ITGB2/FCGR2A/HLA-DPB1/HLA-DMB/HLA-DMA/HLA-DRAhsa04666: Fc gamma R-mediated phagocytosis111.76E-02ARPC1B/FCGR2B/SPHK2/ARPC2/SCIN/ASAP2/MARCKS/FCGR2A/INPP5D/ARPC5/PLPP3hsa05169: Epstein-Barr virus infection141.77E-02HLA-DQB1/CD44/FCER2/VIM/HLA-A/HLA-C/HLA-DPA1/HLA-B/HLA-DPB1/HLA-E/TRAF5/HLA-G/HLA-DRA/HLA-Fhsa05323: Rheumatoid arthritis112.37E-02HLA-DQB1/CTSK/HLA-DPA1/ITGB2/TNFSF13/HLA-DPB1/HLA-DMB/HLA-DMA/CXCL12/HLA-DRA/CD28hsa05321: Inflammatory bowel disease (IBD)92.47E-02MAF/HLA-DQB1/IL4R/HLA-DPA1/HLA-DPB1/HLA-DMB/IL21/HLA-DMA/HLA-DRAhsa05168: Herpes simplex infection182.57E-02HLA-DQB1/C3/HLA-A/HLA-C/HLA-B/HLA-DMB/HLA-E/HLA-DMA/HLA-G/CD74/HLA-F/TNFRSF1A/IFNA4/HLA-DPA1/IFNA14/HLA-DPB1/TRAF5/HLA-DRAhsa04380: Osteoclast differentiation143.00E-02NOX3/NCF2/NOX1/TNFRSF1A/CYBB/CTSK/LILRA1/FCGR2B/OSCAR/FCGR2A/PPP3CA/LCP2/CSF1R/TYROBPhsa05146: Amoebiasis123.31E-02COL4A2/LAMA4/LAMA3/COL4A1/GNAQ/COL3A1/COL1A2/ITGB2/LAMB1/COL5A2/CD14/FN1hsa04650: Natural killer cell mediated cytotoxicity133.84E-02CD244/TNFSF10/KIR2DS5/ICAM2/IFNA4/FCER1G/IFNA14/ITGB2/PPP3CA/KIR2DS3/KIR2DL3/LCP2/TYROBPhsa04390: Hippo signaling pathway154.12E-02ACTB/BMP4/FZD9/WNT16/LEF1/ITGB2/WWTR1/FZD4/FRMD1/AFP/CRB1/ID2/ID1/RASSF1/TEAD4hsa05145: Toxoplasmosis124.18E-02HLA-DQB1/TNFRSF1A/LAMA4/LAMA3/GNAI3/HLA-DPA1/ALOX5/HLA-DPB1/HLA-DMB/LAMB1/HLA-DMA/HLA-DRAhsa04015: Rap1 signaling pathway194.43E-02ACTB/FGFR1/FGFR4/GNAI3/ADCY8/GRIN2A/ITGB2/LPAR1/FGF21/FGF20/GNAQ/ID1/TIAM1/PDGFRA/RAPGEF5/PDGFRB/RAP1B/LCP2/CSF1Rhsa04014: Ras signaling pathway204.66E-02FGFR1/FGFR4/GRIN2A/GNG11/FGF21/FGF20/RGL1/GNGT1/PLA2G4A/PAK3/GNB1/TIAM1/RASSF1/ETS2/PDGFRA/RAPGEF5/PDGFRB/RAP1B/PLA2G2F/CSF1Rhsa04360: Axon guidance134.97E-02DCC/NRP1/GNAI3/PLXNA1/EFNB2/CXCL12/EPHB1/SLIT2/SEMA5A/ROBO1/PAK3/UNC5D/PPP3CA


### 3.2. PPI-Network Construction and Hub Gene Identification

As shown in Figure3(a), the interactions between TGFBI and its coexpression genes were presented by a PPI-network with 1465 nodes. The average node degree was 12.2, and the PPI enrichment P value was <1.0E-16. This finding was saved in TSV format and then imported into Cytoscape for visualization. With a cutoff criterion of a degree that is >5 and a K-core >5, three clusters were constructed as shown in Figure S1; and it is shown in Figure 3(c) that the top 10 hub genes of this PPI-network were also identified (CXCL12, C3, FN1, COL1A2, ACTB, VCAM1, MMP2, VWF, BMP4, and CD44), with the degree ratio ranking method.Figure 3
PPI-network ofTGFBI and its coexpression genes in atrial fibrillation and hub genes. (a) PPI-network constructed based on TGFBI and its coexpression genes. (b) Cluster 1 of the PPI-network: the red ball represents the coexpression, and the orange line represents the correlation. (c) Hub genes: the color depth represents the ranking of hub genes. The sequence of colors is red-orange-yellow from high to low ranking.
(a)(b)(c)We selected the first cluster, which is descripted in Figure3(b) for GO and KEGG pathway analyses and found that the coexpression genes in this cluster enriched in 20 BPs, such as collagen catabolic process (GO:0030574), collagen fibril organization (GO:0030199), and endodermal cell differentiation (GO:0035987); 18CCs, such as collagen trimer (GO:0005581), plasma membrane (GO:0005886), and platelet alpha granule lumen (GO:0031093); 5MFs, such as collagen binding (GO:0005518), and platelet-derived growth factor binding (GO:0048407), and 47 KEGG pathways like viral myocarditis (hsa05416), PI3K-Akt signaling pathway (hsa04151), rheumatoid arthritis (hsa05323), and Inflammatory bowel disease (IBD, hsa05321). The visualization of the mentioned GOs and KEGG pathways is shown in Figure 4, and the whole information of them is shown in Table S2.Figure 4
Go annotation and KEGG pathway of TGFBI and its coexpression genes in cluster 1. (a) Go annotation: the strip represents the value of−log10P of GO term. (b) KEGG pathway: the size of ball represents the count of enriched genes, and the color of ball represents the value of −log10P of pathway.
(a)(b)


### 3.3. Verification of the Hub Gene

The correlations between the hub genes andTGFBI were verified in GSE79768 dataset. We used cor analysis to detect the correlation value between the coexpression gene and TGFBI in GSE79768. As shown in Table 2, except for VWF, BMP4, and CD44, the correlations between TGFBI with CXCL12, C3, FN1, COL1A2, ACTB, VCAM1, and MMP2 were consistent with the results in GSE115574 dataset as they were all positively correlative and the P value <0.05.Table 2
Validation of the correlations between the hub genes andTGFBI.
GeneDegree rankGSE115574GSE79768corPcorPCXCL12∗60.9154.61E-060.8391.82E-02C3∗60.7332.87E-030.8571.38E-02FN1∗10.8074.86E-040.8132.60E-02COL1A2∗60.8213.18E-040.8013.04E-02ACTB∗40.7701.28E-030.8421.74E-02VCAM1∗60.7681.33E-030.7574.87E-02MMP2∗30.9222.73E-060.9173.68E-03VWF100.7701.29E-030.6251.34E-01BMP450.7462.18E-030.5272.24E-01CD4420.8569.24E-050.6191.38E-01∗P<0.05in both dataset. cor: Pearson correlation value.


### 3.4. Pathways and the Verified Hub Genes

It is descripted in Figure5, the verified hub genes were enriched in different and/or same KEGG pathways. CXCL12, ACTB, and MMP2 were enriched in leukocyte transendothelial migration (hsa04670). FN1 and COL1A2 were both enriched in PI3K-Akt signaling pathway (hsa04151), amoebiasis (hsa05146), and focal adhesion (hsa04510). In addition, CXCL12 was enriched in rheumatoid arthritis (hsa05323), and C3 was enriched in leishmaniasis (hsa05140).Figure 5
Sankey diagram of the hub genes and the KEGG pathways they enriched in. The band on the left represents the core gene; and the band on the right represents the pathway the hub gene enriched in. The width of the cloth connecting the two strips represents theP value.


### 3.5. Potential Drugs Targeted by the Verified Hub Genes

To detect the potential drugs of the hub genes, we used the online database DrugBank (http://www.drugbank.ca) to identify the drug that targeted by the verified hub gene. DrugBank is an online and free-access database, integrating the mechanisms, targets, and interactions of the drugs [25]. We selected the genes, which have a complete record of actions and have gotten the approval for presentation in Table 3.Table 3
Target drug of the hub genes.
GeneDrug IDNameStatusActionsC3DB00028Human immunoglobulin GApprovedBinderDB14533Zinc chlorideApprovedInhibitor, ligandDB14548Zinc sulfateApprovedInhibitor, ligandDB16694PegcetacoplanApprovedBinder, regulatorFN1DB08888OcriplasminApprovedCleavageDB14533Zinc chlorideApprovedModulator, ligandDB14548Zinc sulfateApprovedModulator, ligandVCAM1DB01136CarvedilolApprovedInhibitorDB11338Clove oilApprovedAntagonist


## 3.1. Identification of TGFBI Coexpression Genes and Pathway Enrichment Analyses

A total of 1049 negatively coexpressed genes and 769 positively coexpressed genes were identified after cor analysis. These genes were enriched in a series of BPs, such as collagen fibril organization (GO:0030199), collagen catabolic process (GO:0030574), cell adhesion (GO:0007155), and angiogenesis (GO:0001525); 38 CCs, such as collagen trimer (GO:0005581), proteinaceous extracellular matrix (GO:0005578), and basement membrane (GO:0005604); 36 MFs, such as collagen binding (GO:0005518), receptor binding (GO:0005102), and peptide antigen binding (GO:0042605). As shown in Figure2(a), the top 30 GOs were selected for visualization, and the whole information of the GO results were shown in Table S1.Figure 2
Go annotation and KEGG pathway ofTGFBI and its coexpression genes in atrial fibrillation. (a) Go annotation: the strip represents the value of −log10P of GO term. (b) KEGG pathway: the size of ball represents the count of enriched genes, and the color of ball represents the value of −log10P of pathway.
(a)(b)In addition, a total of 39 KEGG pathways were identified, such as antigen processing and presentation (hsa04612), cell adhesion molecules (CAMs, hsa04514), viral myocarditis (hsa05416), and PI3K-Akt signaling pathway (hsa04151). The visualization of these KEGG pathways is shown in Figure2(b), and the whole information of these pathways is shown in Table 1.Table 1
Pathways enriched byTGFBI and its coexpression genes in AF patients.
TermCountP valueEnriched geneshsa05332: Graft versus host disease139.14E-08HLA-DQB1/HLA-A/HLA-C/HLA-B/HLA-DMB/HLA-E/HLA-DMA/HLA-G/HLA-F/HLA-DPA1/HLA-DPB1/CD28/HLA-DRAhsa04612: Antigen processing and presentation191.13E-07HLA-DQB1/PDIA3/KIR2DS5/HLA-A/HLA-C/HLA-B/KIR2DS3/HLA-DMB/HLA-E/HLA-DMA/RFXANK/CD74/HLA-G/HLA-F/CD4/HLA-DPA1/HLA-DPB1/KIR2DL3/HLA-DRAhsa05150:Staphylococcus aureus infection161.37E-07HLA-DQB1/C3AR1/C3/C1R/ITGB2/C1S/HLA-DMB/HLA-DMA/C1QA/C1QB/FCGR2B/HLA-DPA1/FCGR2A/CFI/HLA-DPB1/HLA-DRAhsa04514: Cell adhesion molecules (CAMs)262.13E-07CLDN8/CLDN16/HLA-DQB1/ITGB2/HLA-DMB/HLA-DMA/CDH5/CLDN14/SDC3/VCAM1/CD4/HLA-DPB1/CD28/ICAM2/HLA-A/CD99/HLA-C/CLDN20/HLA-B/HLA-E/HLA-G/HLA-F/NCAM2/VCAN/HLA-DPA1/HLA-DRAhsa04940: Type I diabetes mellitus142.35E-07HLA-DQB1/HLA-A/HLA-C/HLA-B/HLA-DMB/HLA-E/HLA-DMA/HLA-G/HLA-F/CPE/HLA-DPA1/HLA-DPB1/CD28/HLA-DRAhsa05330: Allograft rejection133.89E-07HLA-DQB1/HLA-A/HLA-C/HLA-B/HLA-DMB/HLA-E/HLA-DMA/HLA-G/HLA-F/HLA-DPA1/HLA-DPB1/CD28/HLA-DRAhsa05320: Autoimmune thyroid disease155.51E-07HLA-DQB1/HLA-A/HLA-C/HLA-B/HLA-DMB/HLA-E/HLA-DMA/HLA-G/HLA-F/IFNA4/HLA-DPA1/IFNA14/HLA-DPB1/HLA-DRA/CD28hsa04145: Phagosome266.30E-07HLA-DQB1/C3/C1R/ITGB2/HLA-DMB/HLA-DMA/STX12/HLA-DPB1/THBS2/TUBA1B/ACTB/MRC1/NCF2/HLA-A/COLEC12/HLA-C/HLA-B/HLA-E/HLA-G/HLA-F/FCGR2B/CD209/HLA-DPA1/FCGR2A/CD14/HLA-DRAhsa05416: Viral myocarditis151.85E-06ACTB/HLA-DQB1/HLA-A/ITGB2/HLA-C/HLA-B/HLA-DMB/HLA-E/HLA-DMA/HLA-G/HLA-F/HLA-DPA1/HLA-DPB1/HLA-DRA/CD28hsa04512: ECM-receptor interaction171.93E-05COL4A2/COL4A1/COL3A1/ITGA1/COL5A2/VWF/LAMA4/LAMA3/GP6/CD44/COL6A5/COL6A3/COL1A2/COL6A1/LAMB1/THBS2/FN1hsa04610: Complement and coagulation cascades148.99E-05F13B/C1QA/C7/VWF/C3AR1/C1QB/THBD/SERPINF2/C3/TFPI/SERPING1/C1R/CFI/C1Shsa05152: Tuberculosis233.06E-04MRC1/HLA-DQB1/SPHK2/C3/CEBPG/TIRAP/ITGB2/HLA-DMB/HLA-DMA/RFXANK/CD74/TNFRSF1A/FCGR2B/CD209/IFNA4/FCER1G/HLA-DPA1/IFNA14/FCGR2A/PPP3CA/HLA-DPB1/CD14/HLA-DRAhsa05133: Pertussis138.02E-04C1QA/C1QB/GNAI3/C3/IRF1/TIRAP/PYCARD/ITGB2/SERPING1/C1R/C1S/CASP1/CD14hsa05166: HTLV-I infection288.39E-04HLA-DQB1/WNT16/NRP1/ADCY8/ITGB2/HLA-DMB/HLA-DMA/VCAM1/TNFRSF1A/CDKN2A/PPP3CA/HLA-DPB1/EGR1/FZD9/IL2RB/LTBR/HLA-A/HLA-C/HLA-B/HLA-E/FZD4/HLA-G/HLA-F/ETS2/PDGFRA/PDGFRB/HLA-DPA1/HLA-DRAhsa04672: Intestinal immune network for IgA production109.65E-04HLA-DQB1/LTBR/HLA-DPA1/TNFSF13/HLA-DPB1/HLA-DMB/HLA-DMA/CXCL12/HLA-DRA/CD28hsa04974: Protein digestion and absorption141.07E-03COL4A2/COL14A1/COL4A1/COL7A1/COL6A5/COL3A1/COL6A3/PRCP/COL1A2/COL15A1/COL6A1/CPB1/COL5A2/SLC7A7hsa04670: Leukocyte transendothelial migration161.68E-03ACTB/CLDN16/CLDN8/MYL7/GNAI3/NCF2/CD99/ITGB2/CLDN20/MMP2/CXCL12/CLDN14/CDH5/VCAM1/RAP1B/MSNhsa04151: PI3K-Akt signaling pathway332.83E-03FGFR1/FGFR4/COL3A1/GNG11/LPAR1/G6PC2/COL6A5/IL4R/IFNA4/COL6A3/CREB3L2/COL6A1/LAMB1/THBS2/CSF1R/FN1/IL2RB/COL4A2/COL4A1/ITGA1/FGF21/FGF20/COL5A2/VWF/GNGT1/LAMA4/LAMA3/GNB1/LPAR6/COL1A2/PDGFRA/PDGFRB/IFNA14hsa04810: Regulation of actin cytoskeleton233.00E-03ACTB/FGFR1/MYL7/FGFR4/ARHGEF7/ITGA1/IQGAP2/ITGB2/ARPC5/FGF21/FGF20/IQGAP1/ARPC1B/PAK3/ARPC2/TIAM1/SCIN/PDGFRA/PDGFRB/CYFIP1/TMSB4X/MSN/FN1hsa05322: Systemic lupus erythematosus173.01E-03HLA-DQB1/C7/HIST1H4L/C3/GRIN2A/C1R/C1S/H2AFJ/HLA-DMB/HLA-DMA/C1QA/C1QB/HLA-DPA1/FCGR2A/HLA-DPB1/HLA-DRA/CD28hsa05200: Pathways in cancer363.61E-03DCC/FGFR1/WNT16/GNAI3/ADCY8/GNG11/LPAR1/MMP2/CXCL12/CDKN2A/LAMB1/TRAF5/TRAF4/CSF1R/FN1/FZD9/BMP4/PTGER2/COL4A2/COL4A1/PTGER4/EPAS1/LEF1/FGF21/FGF20/MECOM/FZD4/GNGT1/LAMA4/LAMA3/GNAQ/GNB1/LPAR6/RASSF1/PDGFRA/PDGFRBhsa04510: Focal adhesion225.09E-03ACTB/MYL7/COL4A2/COL4A1/COL3A1/ITGA1/FLNB/COL5A2/VWF/LAMA4/LAMA3/PAK3/COL6A5/COL6A3/COL1A2/PDGFRA/COL6A1/PDGFRB/RAP1B/LAMB1/THBS2/FN1hsa05310: Asthma75.44E-03HLA-DQB1/FCER1G/HLA-DPA1/HLA-DPB1/HLA-DMB/HLA-DMA/HLA-DRAhsa00590: Arachidonic acid metabolism106.18E-03AKR1C3/GGT5/PLA2G4A/PTGIS/ALOX12B/GGT1/ALOX5/CBR3/LTC4S/PLA2G2Fhsa04611: Platelet activation151.30E-02ACTB/GNAI3/ADCY8/COL3A1/COL5A2/VWF/PLA2G4A/GP6/GNAQ/VAMP8/COL1A2/FCER1G/RAP1B/FCGR2A/LCP2hsa05140: Leishmaniasis101.63E-02HLA-DQB1/NCF2/C3/HLA-DPA1/ITGB2/FCGR2A/HLA-DPB1/HLA-DMB/HLA-DMA/HLA-DRAhsa04666: Fc gamma R-mediated phagocytosis111.76E-02ARPC1B/FCGR2B/SPHK2/ARPC2/SCIN/ASAP2/MARCKS/FCGR2A/INPP5D/ARPC5/PLPP3hsa05169: Epstein-Barr virus infection141.77E-02HLA-DQB1/CD44/FCER2/VIM/HLA-A/HLA-C/HLA-DPA1/HLA-B/HLA-DPB1/HLA-E/TRAF5/HLA-G/HLA-DRA/HLA-Fhsa05323: Rheumatoid arthritis112.37E-02HLA-DQB1/CTSK/HLA-DPA1/ITGB2/TNFSF13/HLA-DPB1/HLA-DMB/HLA-DMA/CXCL12/HLA-DRA/CD28hsa05321: Inflammatory bowel disease (IBD)92.47E-02MAF/HLA-DQB1/IL4R/HLA-DPA1/HLA-DPB1/HLA-DMB/IL21/HLA-DMA/HLA-DRAhsa05168: Herpes simplex infection182.57E-02HLA-DQB1/C3/HLA-A/HLA-C/HLA-B/HLA-DMB/HLA-E/HLA-DMA/HLA-G/CD74/HLA-F/TNFRSF1A/IFNA4/HLA-DPA1/IFNA14/HLA-DPB1/TRAF5/HLA-DRAhsa04380: Osteoclast differentiation143.00E-02NOX3/NCF2/NOX1/TNFRSF1A/CYBB/CTSK/LILRA1/FCGR2B/OSCAR/FCGR2A/PPP3CA/LCP2/CSF1R/TYROBPhsa05146: Amoebiasis123.31E-02COL4A2/LAMA4/LAMA3/COL4A1/GNAQ/COL3A1/COL1A2/ITGB2/LAMB1/COL5A2/CD14/FN1hsa04650: Natural killer cell mediated cytotoxicity133.84E-02CD244/TNFSF10/KIR2DS5/ICAM2/IFNA4/FCER1G/IFNA14/ITGB2/PPP3CA/KIR2DS3/KIR2DL3/LCP2/TYROBPhsa04390: Hippo signaling pathway154.12E-02ACTB/BMP4/FZD9/WNT16/LEF1/ITGB2/WWTR1/FZD4/FRMD1/AFP/CRB1/ID2/ID1/RASSF1/TEAD4hsa05145: Toxoplasmosis124.18E-02HLA-DQB1/TNFRSF1A/LAMA4/LAMA3/GNAI3/HLA-DPA1/ALOX5/HLA-DPB1/HLA-DMB/LAMB1/HLA-DMA/HLA-DRAhsa04015: Rap1 signaling pathway194.43E-02ACTB/FGFR1/FGFR4/GNAI3/ADCY8/GRIN2A/ITGB2/LPAR1/FGF21/FGF20/GNAQ/ID1/TIAM1/PDGFRA/RAPGEF5/PDGFRB/RAP1B/LCP2/CSF1Rhsa04014: Ras signaling pathway204.66E-02FGFR1/FGFR4/GRIN2A/GNG11/FGF21/FGF20/RGL1/GNGT1/PLA2G4A/PAK3/GNB1/TIAM1/RASSF1/ETS2/PDGFRA/RAPGEF5/PDGFRB/RAP1B/PLA2G2F/CSF1Rhsa04360: Axon guidance134.97E-02DCC/NRP1/GNAI3/PLXNA1/EFNB2/CXCL12/EPHB1/SLIT2/SEMA5A/ROBO1/PAK3/UNC5D/PPP3CA


## 3.2. PPI-Network Construction and Hub Gene Identification

As shown in Figure3(a), the interactions between TGFBI and its coexpression genes were presented by a PPI-network with 1465 nodes. The average node degree was 12.2, and the PPI enrichment P value was <1.0E-16. This finding was saved in TSV format and then imported into Cytoscape for visualization. With a cutoff criterion of a degree that is >5 and a K-core >5, three clusters were constructed as shown in Figure S1; and it is shown in Figure 3(c) that the top 10 hub genes of this PPI-network were also identified (CXCL12, C3, FN1, COL1A2, ACTB, VCAM1, MMP2, VWF, BMP4, and CD44), with the degree ratio ranking method.Figure 3
PPI-network ofTGFBI and its coexpression genes in atrial fibrillation and hub genes. (a) PPI-network constructed based on TGFBI and its coexpression genes. (b) Cluster 1 of the PPI-network: the red ball represents the coexpression, and the orange line represents the correlation. (c) Hub genes: the color depth represents the ranking of hub genes. The sequence of colors is red-orange-yellow from high to low ranking.
(a)(b)(c)We selected the first cluster, which is descripted in Figure3(b) for GO and KEGG pathway analyses and found that the coexpression genes in this cluster enriched in 20 BPs, such as collagen catabolic process (GO:0030574), collagen fibril organization (GO:0030199), and endodermal cell differentiation (GO:0035987); 18CCs, such as collagen trimer (GO:0005581), plasma membrane (GO:0005886), and platelet alpha granule lumen (GO:0031093); 5MFs, such as collagen binding (GO:0005518), and platelet-derived growth factor binding (GO:0048407), and 47 KEGG pathways like viral myocarditis (hsa05416), PI3K-Akt signaling pathway (hsa04151), rheumatoid arthritis (hsa05323), and Inflammatory bowel disease (IBD, hsa05321). The visualization of the mentioned GOs and KEGG pathways is shown in Figure 4, and the whole information of them is shown in Table S2.Figure 4
Go annotation and KEGG pathway of TGFBI and its coexpression genes in cluster 1. (a) Go annotation: the strip represents the value of−log10P of GO term. (b) KEGG pathway: the size of ball represents the count of enriched genes, and the color of ball represents the value of −log10P of pathway.
(a)(b)


## 3.3. Verification of the Hub Gene

The correlations between the hub genes andTGFBI were verified in GSE79768 dataset. We used cor analysis to detect the correlation value between the coexpression gene and TGFBI in GSE79768. As shown in Table 2, except for VWF, BMP4, and CD44, the correlations between TGFBI with CXCL12, C3, FN1, COL1A2, ACTB, VCAM1, and MMP2 were consistent with the results in GSE115574 dataset as they were all positively correlative and the P value <0.05.Table 2
Validation of the correlations between the hub genes andTGFBI.
GeneDegree rankGSE115574GSE79768corPcorPCXCL12∗60.9154.61E-060.8391.82E-02C3∗60.7332.87E-030.8571.38E-02FN1∗10.8074.86E-040.8132.60E-02COL1A2∗60.8213.18E-040.8013.04E-02ACTB∗40.7701.28E-030.8421.74E-02VCAM1∗60.7681.33E-030.7574.87E-02MMP2∗30.9222.73E-060.9173.68E-03VWF100.7701.29E-030.6251.34E-01BMP450.7462.18E-030.5272.24E-01CD4420.8569.24E-050.6191.38E-01∗P<0.05in both dataset. cor: Pearson correlation value.


## 3.4. Pathways and the Verified Hub Genes

It is descripted in Figure5, the verified hub genes were enriched in different and/or same KEGG pathways. CXCL12, ACTB, and MMP2 were enriched in leukocyte transendothelial migration (hsa04670). FN1 and COL1A2 were both enriched in PI3K-Akt signaling pathway (hsa04151), amoebiasis (hsa05146), and focal adhesion (hsa04510). In addition, CXCL12 was enriched in rheumatoid arthritis (hsa05323), and C3 was enriched in leishmaniasis (hsa05140).Figure 5
Sankey diagram of the hub genes and the KEGG pathways they enriched in. The band on the left represents the core gene; and the band on the right represents the pathway the hub gene enriched in. The width of the cloth connecting the two strips represents theP value.


## 3.5. Potential Drugs Targeted by the Verified Hub Genes

To detect the potential drugs of the hub genes, we used the online database DrugBank (http://www.drugbank.ca) to identify the drug that targeted by the verified hub gene. DrugBank is an online and free-access database, integrating the mechanisms, targets, and interactions of the drugs [25]. We selected the genes, which have a complete record of actions and have gotten the approval for presentation in Table 3.Table 3
Target drug of the hub genes.
GeneDrug IDNameStatusActionsC3DB00028Human immunoglobulin GApprovedBinderDB14533Zinc chlorideApprovedInhibitor, ligandDB14548Zinc sulfateApprovedInhibitor, ligandDB16694PegcetacoplanApprovedBinder, regulatorFN1DB08888OcriplasminApprovedCleavageDB14533Zinc chlorideApprovedModulator, ligandDB14548Zinc sulfateApprovedModulator, ligandVCAM1DB01136CarvedilolApprovedInhibitorDB11338Clove oilApprovedAntagonist


## 4. Discussion

The rapid development of sequencing technology has helped researchers to gain a deeper understanding of several comprehensive diseases, such as cardiovascular diseases, tumors, and autoimmune diseases. The mechanism of AF remains not well clear. In the current study, we identified the potential mechanism ofTGFBI and its coexpression genes in AF patients, and verified the hub genes, hoping to provide reference for the further study of AF.It is descripted in Figure6 that TGFBI is located in 5q31.1, and it expresses in several organs like heart, liver, colon, urinary bladder, and so on (https://www.ncbi.nlm.nih.gov/gene/7045). In early studies, the research of TGFBI mainly focused on corneal dystrophy as it is a primary disease-causing gene of corneal dystrophy, leading to protein deposits on the cornea then cause blindness [26, 27]. In addition, it is also a star gene owing to its important effect on the outcome of cancer and the sensitivity to chemotherapeutic drugs. It affects the progress of tumor main because it would promote cell proliferation, migration, and change the microenvironment [28, 29]. However, the function of TGFBI in heart remains unclear. An early study suggested that in diseased heart, the expression of TGFBI mRNA induced [30]. Similarly, Schwanekamp et al. [31] reported that the expression of TGFBI in the heart increased after injury. What is more, in a plasma proteome profiling, TGFBI has a potential role in extracellular matrix remodeling in fibrosis [32]. A recent study further confirmed that TGFBI and ADAM19 were associated with the TGF-β1 pathway and cardiac fibrosis [33]. Thus, TGFBI probably plays a role in myocardial fibrosis.Figure 6
Location and expression aboutTGFBI. (a) Location of TGFBI in chromosome. (b) Expression of TGFBI in different samples.
(a)(b)Myocardial fibrosis is a key pathophysiological mechanism of heart failure and arrhythmia. Cardiac fibrosis gets the pathological feature with disorder of cardiac muscle cells and notable increasing of collagen fibers. Disordered myocardium and interwoven collagen fibers lead to the uncoordinated conduction of power induced by systole and diastole, and anisotropy of action potentials, which lead to the decrease of heart function and the risk of arrhythmias [34, 35]. As is known, genes with similar functions often show coexpression relationships and then coregulate biological functions. We found that TGFBI and its coexpression genes enriched in several GOs and pathways like collagen fibril organization, PI3K-Akt pathway, and viral myocarditis. PI3K-Akt pathway has been reported regulating myocardial fibrosis. FN1 and COL1A2 genes are both enriched in this pathway. Huang et al. [36] reported that miR-144-3p/FN1 and miR-9-3p/FN1 pathways may play an important role in myocardial fibrosis. In addition, FN1 takes part in the cardiac endothelial cell dysfunction induced by myofibroblast-derived exosomes. Actually, FN1 has been used as a myocardial fibrosis marker in research [37, 38]. The gene ACTB, encoding one of six different actin proteins, which are involved in structure, integrity, cell motility, and intercellular signaling, was enriched in the viral myocarditis pathway. Viral myocarditis is the result of direct damage to the myocardium by virus and indirect damage to the myocardium by immune response. These damage leads to edema and necrosis of the myocardial cell and proliferation and fibrosis of interstitial cell [39]. Thus, these genes may play a role in the regulation of myocardial fibrosis and then increase the risk of arrhythmia.Recent studies gave increasing suggestion of the association of immune-related diseases (IRD) and AF. A nationwide population-based study with 37,696 patients with IBD showed that patients with IBD got a 36% (95% confidenceinterval=20%-54%) higher risk of AF than controls [40]. Similarly, patients with systemic lupus erythematosus (SLE) have also been reported to get a higher risk of AF compared to controls (Hazard ratio=2.84, 95% CI=2.50-3.23) [41]. When it comes to the systemic sclerosis (SSc), they may get a higher risk of AF at 1.75 times of the controls (95% CI=1.51-2.04), what is more, SSc could affect the heart and then lead to myocardial fibrosis, which would promote the formation of AF [42, 43]. Rheumatoid arthritis (RA), another autoimmune disease, has been suggested to increase the risk of AF [44]. In an early study, researchers reported that systemic inflammatory can lead to epicardial adipose tissue expansion and inflammation, and then cause the enlarger, fibrotic, and noncompliant of the left atrium, finally results in AF [45]. IL-6 is a crucial prerequisite for fibrosis of cardiac myocytes, when it causes the decrease expression of Cx40 and Cx43, it is strongly correlated with the high expression of collagen fibrin I and collagen fibrin III via the pSTAT3 pathway [46]. The expression of IL-6 increases in several IRDs, and it may be the reason that it could be suggested as a biomarker of AF [47–49]. In this study, we found that the hub gene C3 was enriched in SLE, and CXCL12 was enriched in rheumatoid arthritis. The expression of these genes may affect the activation of the autoimmune system, and then promotes the remodeling of the atrium, which leads to AF.Targeted drugs of the hub genes were also identified in this study. The complement system is an important part of the human immune system and is involved in the inflammatory response. Pegcetacopla, a PEGylated peptide targeting C3, is an inhibitor of hemolysis used in clinics. It can improve hemoglobin, clinical outcome, and hematologic outcome via effect control of intravascular hemolysis as well as extravascular hemolysis [50]. C3 has also been reported associated with age and hypertension, which is known as the risk of AF. In addition, dipeptidyl peptidase III can prevent heart from inflammatory cell infiltration and fibrosis via cleavage of a peptide that is a part of C3 [51, 52]. However, little is known of the use of pegcetacopla in the treatment of heart disease. Carvedilol is one of the calcium channel blockers, and it could alter circulating miR-1 and miR-214, which are suggested in the processes of myocyte hypertrophy and apoptosis and release myocardial fibrosis [53, 54]. In addition, it can provide prevention of chemotherapy-related cardiotoxicity [55]. Thus, carvedilol may be an ideal antifibrosis target drug. Vitreomacular adhesion (VMA) is an eye disease, and it always leads to visual impairment, even loss of vision when it gets worse with vitreomacular traction (VMT). Pharmacological vitreolysis was an alternative treatment for VMA. Recently, a new drug named ocriplasmin, a recombinant DNA molecule based on autologous plasmin, was developed to catalyze the breakdown of the bond of laminin and fibronectin, maintaining vitreous adhesion [56]. The blood clots caused by AF are usually venous thrombosis, and fibrinolysis enzymes and clotting factor inhibitors are common treatments. However, whether ocriplasmin would be an effect component of anticoagulant or thrombolytic therapy still needs further researches to detect.With the progress of technology and the continuous evolution of algorithms, our understanding of complex diseases is further deepened. Several related works provide powerful boost to medical research. Su et al. [57] reported a framework using horizontal and vertical multiverse optimization, providing an effective segmentation method for diagnosing Coronavirus Disease 2019 (COVID-19). Similarly, Qi et al. [58] reported a directional mutation and crossover boosted ant colony optimization for diagnosing COVID-19. In addition, saliency detection network with neutrosophic enhancement have been reported to be an effective approach to colorectal polyp region extraction [59]. Bioinformatics has developed rapidly in the last decade, and it was not only owing to the development of sequencing technology but also to the update of algorithms. No matter what the specific mechanism is, at present, the cause of AF is basically considered as cardiac fibrosis induced by atrial remodel. In the current study, we detected the potential mechanism of TGFBI and its coexpression genes of AF and found that they may induce cardiac fibrosis via several pathways. The identified hub genes may be potential targets for the interference of AF.Based on our finding, our future work was designed as follows. Firstly, we aim to cooperate with surgeons, collecting several LAAs of AF patients, and verify the correlation of the expression levels ofTGFBI and the identified hub genes via reverse transcription-polymerase chain reaction (RT-qPCR). Secondly, measuring the expression of proteins in the downstream of the pathway they enriched in via knocking down or overexpressing these genes to verify their function. Thirdly, after the intervention of the extracted cardiomyocytes from the LAAs with the predicted targeted drugs, the effect on the expression of these genes and proteins was evaluated by RT-qPCR and western blot. In addition, we aimed to construct an AF animal model with pacemaker via persistent rapid atrial stimulation to repeat the experiment. Finally, several representative computational intelligence algorithms, like monarch butterfly optimization (MBO) [60], earthworm optimization algorithm (EWA) [61], elephant herding optimization (EHO) [62], slime mould algorithm (SMA) [63], hunger games search (HGS) [64], RUNge Kutta optimizer (RUN) [65], colony predation algorithm (CPA) [66], and Harris hawks optimization (HHO) [67, 68] could be used to optimized our analysis.


## 5. Conclusions

In the current study, we usedTGFBI and its coexpression genes to identify the potential molecular mechanisms of AF. These findings may help elucidate the functions of these genes in AF and provide a target of AF management. However, there were several limitations of the current study. First, the sample-size is not large as other randomized controlled study. Second, the results of this study were mainly based on bioinformatic analysis, and further experiments are needed to confirm both in vivo and in vitro. Finally, several potential factors that participant in the formation of AF may not be included. Fortunately, with the development of algorithms, several representative computational intelligence algorithms like MBO, EWA, EHO, SMA, HGS, RUN, CPA, and HHO may be used to solve the problems. In the future, the results of our study will be further verified by more optimized algorithms, expanded samples, and experiments both in vivo and in vitro.



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