Skip to main content
Download PDF

Identification of potential biomarkers for atrial fibrillation and stable coronary artery disease based on WGCNA and machine algorithms

BMC Cardiovascular Disorders volume 24, Article number: 401 (2024) Cite this article

Abstract

Background

Patients with atrial fibrillation (AF) often have coronary artery disease (CAD), but the biological link between them remains unclear. This study aims to explore the common pathogenesis of AF and CAD and identify common biomarkers.

Methods

Gene expression profiles for AF and stable CAD were downloaded from the Gene Expression Omnibus database. Overlapping genes related to both diseases were identified using weighted gene co-expression network analysis (WGCNA), followed by functional enrichment analysis. Hub genes were then identified using the machine learning algorithm. Immune cell infiltration and correlations with hub genes were explored, followed by drug predictions. Hub gene expression in AF and CAD patients was validated by real-time qPCR.

Results

We obtained 28 common overlapping genes in AF and stable CAD, mainly enriched in the PI3K-Akt, ECM-receptor interaction, and relaxin signaling pathway. Two hub genes, COL6A3 and FKBP10, were positively correlated with the abundance of MDSC, plasmacytoid dendritic cells, and regulatory T cells in AF and negatively correlated with the abundance of CD56dim natural killer cells in CAD. The AUCs of COL6A3 and FKBP10 were all above or close to 0.7. Drug prediction suggested that collagenase clostridium histolyticum and ocriplasmin, which target COL6A3, may be potential drugs for AF and stable CAD. Additionally, COL6A3 and FKBP10 were upregulated in patients with AF and CAD.

Conclusion

COL6A3 and FKBP10 may be key biomarkers for AF and CAD, providing new insights into the diagnosis and treatment of this disease.

Peer Review reports

Introduction

Cardiovascular disease, encompassing conditions like coronary artery disease (CAD) and atrial fibrillation (AF), remains the leading cause of death worldwide [1]. AF is the most prevalent serious cardiac arrhythmia, affecting 2% of the general population, with incidence increasing with age [2]. CAD, a multifactorial chronic disease, is associated with high rates of sudden death and severe complications. CAD can be categorized into stable CAD and acute coronary syndromes [3, 4].

The coexistence of AF and CAD is relatively common in clinical settings [5], with approximately 30% of patients with AF having CAD [6]. Recent research shows atherosclerotic CAD in about 70% of AF patients undergoing angiography [7]. The recurrence rate of AF was significantly reduced after percutaneous coronary intervention in patients with CAD, suggesting a direct link between AF and CAD [8]. The two diseases are likely rooted in shared risk factors and pathogenic mechanisms [9, 10]. These factors drive inflammation and endothelial dysfunction, two key elements in both diseases. Myocardial ischemia-induced local inflammation causes fibrosis and prolonged conduction time, triggering ectopic foci, re-entry, and neural alterations that may lead to AF [11]. Conversely, AF may promote the growth and instability of atherosclerotic plaques by inducing pro-inflammatory factors [12]. Additionally, the reduction of nitric oxide leads to endothelial dysfunction, increasing CAD risk [10, 13]. Therefore, it is clinically important to explore the shared underlying mechanisms of AF and CAD and identify promising biomarkers.

Machine learning has become a pivotal tool in basic and clinical research for uncovering disease mechanisms [14]. It has been employed to develop risk prediction models for AF and stable CAD [15, 16]. Genetic studies play a crucial role in understanding the mechanisms of both diseases [17, 18]. Our study aimed to identify common hub genes in AF and stable CAD that could serve as potential biomarkers, offering new insights into disease mechanisms and treatment. To achieve this, we utilized weighted gene co-expression network analysis (WGCNA) and machine learning algorithms on public databases to identify these common hub genes and explored their correlation with immune cells. Furthermore, real-time qPCR and receiver operating characteristic (ROC) curves were performed to validate the expression, diagnostic, and therapeutic values of hub genes.

Materials and methods

Data source and processing

Gene expression data were filtered by keywords “stable CAD” or “AF” and “Homo sapiens” using Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/). Finally, five datasets were included. GSE159657 (plasma exosome samples, n = 10 for controls and n = 8 for stable CAD) and GSE56885 (peripheral blood mononuclear cell samples, n = 2 for controls and n = 4 for stable CAD) were in the stable CAD group. GSE79768 (thirteen paired left and right atrial tissue samples, n = 6 for sinus rhythm and n = 7 for AF), GSE115574 (59 left and right atrial tissue samples from 16 patients with sinus rhythm and 15 patients with AF), and GSE41177 (Nineteen paired left atrial-pulmonary vein junction and left atrial appendage samples, n = 3 for sinus rhythm and n = 16 for AF) were in the AF group. Merged GSE79768 and GSE115574 contained a total of 85 left and right atrial tissue samples (from 22 patients with sinus rhythm and 22 patients with AF) after removing the batch effect using the “combat” function. GSE159657 and combined GSE79768 and GSE115574 were used as the training sets, and GSE56885 and GSE41177 were used as the validation sets, respectively.

WGCNA

GSE159657 and merged GSE79768 and GSE115574 were analyzed through the “WGCNA” package in R to obtain modules that positively related to stable CAD and AF, respectively. Genes with high expression variability (top 25% of expression standard deviation) were selected and outliers were excluded by hierarchical clustering. Scale-free networks were constructed, where connections between genes are based on their co-expression patterns. This network exhibits a power-law distribution, meaning a few highly connected genes (hub genes) influence many other genes. Appropriate thresholds (β) were selected using the “pickSoftThreshold” function to ensure the scale-free property of network (R2 > 0.9). Next, the topological overlap matrix (TOM) was calculated, which measures the similarity of gene connections within the network. Using dynamic tree-cutting methods, genes with high TOM values were clustered into modules, ensuring a minimum dissimilarity (phase dissimilarity < 0.25) between modules. Correlation analysis between modules’ eigenvalues (representative genes summarizing the module’s expression pattern) and clinical features of disease was performed to obtain modules related to disease. Detailed clinical information on the patients included in these datasets is described in Supplementary file 1 (Table S1-S4).

Identification of overlapping genes and protein-protein interaction (PPI) network analysis

Intersections were taken for genes in modules positively related to AF or stable CAD to obtain overlapping genes. These overlapping genes were then imported into the STRING database (https://cn.string-db.org/) for PPI analysis (species: “Homo sapiens”) [19].

Function enrichment analysis

To further understand the common biological mechanisms and signaling pathways between AF and stable CAD, we performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis using the DAVID database (https://david.ncifcrf.gov/) with a false discovery rate (FDR) < 0.05.

Identification of hub genes based on machine learning algorithms

Based on GSE159657 and merged GSE79768 and GSE115574, the least absolute shrinkage and selection operator (LASSO) regression analysis was performed to screen genes from the overlapping genes using the “glmnet” package in R, respectively. Screened genes were ranked by the value of the mean decrease accuracy using the “randomForest” package in R, followed by 10-fold cross-validation to obtain the optimal number of signature genes. For classification models, random forest (RF) was configured with 500 trees and enabled importance and proximity parameters in the “randomForest” package in R, while support vector machine (SVM) employed a radial basis function kernel with default settings and probability = TRUE for predicting class probabilities in the “e1071” packages in R. These parameters provided satisfactory results on the datasets. The receiver operating characteristic (ROC) curve of the classification models and signature genes was performed using the “pROC” package in R. The area under the curve (AUC) was used to evaluate their diagnostic capacity. Candidate hub genes were identified from signature genes that were differentially expressed in both the AF and stable CAD groups compared to controls. Moreover, these genes exhibited the same expression trend in both AF and stable CAD. Candidate hub genes were validated in the validation sets, and those with consistent results were confirmed as hub genes. Then, the ROC curves of the hub genes in the training and validation sets were plotted using the “pROC” package in R.

Comprehensive analysis of hub genes

Co-expression and functional enrichment analysis of hub genes were performed on GeneMANIA (http://genemania.org). The transcription factors (TFs) regulating the hub genes were predicted using the Cistrome DB database (http://genemania.org) and those with Regulatory Potential Score in the top 20 were selected and visualized using Cytoscape to construct the “TF-mRNA” regulatory network. In addition, the DGIdb database (https://dgidb.org/) was used to predict potential drugs that may target the hub genes.

Immune cell infiltration analysis

The relative abundance of immune cell infiltration in control and disease samples was quantified by enrichment score using the ssGSEA (Single Sample Gene Set Enrichment Analysis) algorithm, followed by comparing the differences using the t-test. Gene sets labeling each infiltrating immune cell type were obtained from Charoentong’s study [20]. Correlation analysis between pivotal genes and immune cell infiltration was conducted using Pearson correlation coefficients.

Patient samples and protocols

A total of 28 blood samples were collected from 8 healthy controls, 6 patients with stable CAD, 6 patients with AF, and 8 patients with AF-CAD samples. Detailed clinical information on these patients is provided in Supplementary file 1 (Table S5). Total RNA from blood samples was extracted using the Blood RNA Extraction Kit (R4163-02, Magen) and reversed to cDNA using the FastQuant cDNA First-Strand Synthesis Kit (KR106, TIANGEN). Real-time PCR was performed to detect the expression levels of hub genes using SuperReal PreMix Plus (SYBR Green) (FP205, TIANGEN) according to the manufacturer’s protocol. GADPH and β-actin were used as internal reference genes. Detected data were processed using the 2−∆∆Ct method. Data were shown as the means ± standard deviation (SD). T-test was used for comparison between two samples. The specific primer sequences are shown in Table 1.

Table 1 Primer sequences used for the real-time PCR
Full size table

Results

Identification of modules related to AF and stable CAD

A total of 21,665 common genes in merged GSE79768 and GSE115574 were obtained by removing batch effects using the “combat” function. The data distributions between the two datasets converge after batch effect removal, indicating effective mitigation of batch effects (Figure S1). In merged GSE79768 and GSE115574, β = 5 was chosen using the “pickSoftThreshold” function to construct scale-free networks (Fig. 1A). The minimum number of genes in a module was set to 100 and 18 modules were obtained after cutting the dynamic tree (Fig. 1B). Correction analysis revealed that 3 modules were significantly and positively related to AF, including purple (r = 0.25), magenta (r = 0.24), and tan (r = 0.3) modules (Fig. 1C). In GSE159657, β = 8 was chosen using the “pickSoftThreshold” function to construct scale-free networks (Fig. 1D). The minimum number of genes in a module was set to 40 and 31 modules were obtained after cutting the dynamic tree (Fig. 1E). Correction analysis revealed that 4 modules were significantly and positively related to stable CAD, including orange (r = 0.48), cyan (r = 0.66), white (r = 0.56), and tan (r = 0.57) modules (Fig. 1F).

Fig. 1
figure 1

Identification of modules associated with AF and stable CAD. A The scale-free fit index for soft-thresholding powers and mean connectivity (combined GSE79768 and GSE115574). B Gene dendrogram and module colors. C Correlation results between the 18 modules and AF trait. D The scale-free fit index for soft-thresholding powers and mean connectivity (GSE159657). E Gene dendrogram and module colors. F Correlation results between the 31 modules and stable CAD trait

Full size image

Identification of overlapping genes

The genes in modules related to AF and stable CAD were intersected to obtain 28 overlapping genes (Fig. 2A). The PPI network of overlapping genes identified 28 pairs of interacting genes. Among them, 4 pairs of genes with interaction scores were at 0.999, including COL1A1 and COL3A1, COL1A1 and COL1A2, COL1A2 and COL3A1, HLA-DQA1 and HLA-DQB1 (Fig. 2B). GO analysis showed that those overlapping genes were mainly distributed in the cellular component (CC) such as extracellular space and collagen trimer. These genes were mainly enriched in molecular functions (MF) such as extracellular matrix structural constituent, protease binding, and platelet-derived growth factor binding. These genes were mainly involved in biological processes (BP) such as collagen fibril and extracellular matrix organizations, cellular responses to amino acid stimuli, and wound healing (Fig. 2C). KEGG analysis showed that these genes were enriched in PI3K-Akt, relaxin Signaling Pathway, Extra Cellular Matrix (ECM)-receptor interaction, and focal adhesion (Fig. 2D).

Fig. 2
figure 2

Identification of overlapping genes and functional enrichment. A Venn diagram of CAD and AF module genes. B PPI network of overlapping genes. C GO enrichment analysis of overlapping genes. D KEGG enrichment analysis of overlapping genes

Full size image

Identification of hub genes based on the machine learning algorithm

Diagnostic models for AF and CAD were constructed through machine learning algorithms. In merged GSE79768 and GSE115574, 14 genes were screened from 28 overlapping genes using LASSO and ranked by mean decrease accuracy (Fig. 3A). The 10-fold cross-validation showed that the AUC of diagnosing AF was highest when the number of genes was 8 (Fig. 3B). Subsequently, SVM and RF models of AF were constructed using 8 signature genes. The AUCs of SVM and RF were 0.854 and 0.851, respectively (Fig. 3C). The SVM model achieved an accuracy of 0.812, a sensitivity of 0.786, and a specificity of 0.884. The RF model showed an accuracy of 0.800, a sensitivity of 0.690, and a specificity of 0.977. Similarly, 8 genes were screened in GSE159657 (Fig. 3D) and the AUC of diagnosing stable CAD was highest when the number of genes was 7 (Fig. 3E). SVM and RF models of stable CAD were also constructed using 7 signature genes. The AUCs of both the SVM and RF were 0.988 (Fig. 3F). The SVM model had an accuracy of 0.889, a sensitivity of 1.000, and a specificity of 0.900. The RF model exhibited comparable performance metrics to the SVM.

Fig. 3
figure 3

Identification of candidate hub genes based on machine learning algorithm. A Genes screened by LASSO and ranked by average decreasing precision in AF dataset. B The results of 10-fold cross validation. C ROC curves for model prediction accuracy in AF dataset. D Genes screened by LASSO and ranked by average decreasing precision in CAD dataset. E The results of 10-fold cross validation. F ROC curves for model prediction accuracy in CAD dataset

Full size image

Additionally, 8 AF-related signature genes and 7 stable CAD-related signature genes were overlapped to obtain 3 genes (COL6A3, FKBP10, and AVPI1) (Fig. 4A). The expression levels of COL6A3 and FKBP10 in training sets were significantly highly expressed in both AF and stable CAD, while AVPI1 expression was inconsistent (Fig. 4B-C). Therefore, COL6A3 and FKBP10 were used as candidate hub genes and further validated for expression in the validation sets. Validation showed that COL6A3 and FKBP10 were significantly up-regulated in AF in the GSE41177 dataset and trended towards high expression in stable CAD in the GSE56885 dataset (Fig. 4D-E) Finally, COL6A3 and FKBP10 were identified as hub genes potentially associated with stable CAD and AF.

Fig. 4
figure 4

The expressions of candidate genes were verified in training and validation sets. A Venn diagram of AF and CAD related genes. B The expression of candidate genes of AF in training sets. C The expression of candidate genes of CAD in training sets. D The expression of candidate genes of AF in validation sets. E The expression of candidate genes of CAD in validation sets

Full size image

Comprehensive analysis of hub genes

A complex network was constructed using GeneMANIA, including 20 genes related to COL6A3 and FKBP10, and 778 interactions (Fig. 5A). The functional enrichment results suggested that these 22 genes may be linked to various aspects of the extracellular matrix, such as collagen trimer (Fig. 5A). The “TF-mRNA” regulatory network revealed 5 common TFs (BRD4, EP300, MYC, POLR2A, and RELA) that regulate COL6A3 and FKBP10 (Fig. 5B). The expression levels of BRD4 and POLR2A were significantly higher in the stable CAD group compared to the controls, while there was no significant difference between the AF group and its controls (Fig. 5C). Therefore, it is hypothesized that hub genes in CAD could be regulated by BRD4 and POLR2A, while in AF they may be regulated by other TFs. Furthermore, the drug prediction results revealed that two drugs targeted COL6A3, including collagenase clostridium histolyticum and ocriplasmin, while there were no corresponding drugs searched for FKBP10 (Fig. 5D).

Fig. 5
figure 5

Comprehensive analysis of hub genes. A Co-expression network of COL6A3 and FKBP10, and functional enrichment. B The “TF-mRNA” regulatory network of COL6A3 and FKBP10. C The expression of common transcription factors in AF and CAD. D Drug prediction for COL6A3 and FKBP10 based on DIGDB database

Full size image

Assessment of the predictive accuracy of hub genes

To evaluate the diagnostic ability of hub genes for two diseases, we performed ROC analysis. The validation set for CAD was excluded from this analysis due to its small sample size. The results showed that the AUC values were consistently near or exceeded 0.7 in all three datasets, suggesting the potential ability of COL6A3 and FKBP10 to distinguish patients with AF and CAD from controls (Fig. 6).

Fig. 6
figure 6

ROC curve of hub genes in training and validation sets

Full size image

Immune cell infiltration analysis

Given the pathogenic role of inflammation in both stable CAD and AF, we performed immune cell infiltration analyses in training sets for the two diseases. The results showed that the levels of immature B cells, myeloid-derived suppressor cells (MDSC), plasmacytoid dendritic cells, and regulatory T cells were significantly higher in the AF group compared to the controls (Fig. 7A). Correlation analysis showed that COL6A3 was significantly positively correlated with immature B cells (r = 0.36), MDSC (r = 0.41), plasmacytoid dendritic cells (r = 0.45), and regulatory T cells (r = 0.6). FKBP10 showed significant positive correlations with MDSC (r = 0.26), plasmacytoid dendritic cells (r = 0.49), and regulatory T cells (r = 0.35), but not with immature B cells (Fig. 7B). Similarly, the levels of CD56dim natural killer cells were significantly decreased in the stable CAD group compared to the controls, while natural killer T cells significantly increased (Fig. 7C). Meanwhile, CD56dim natural killer cells exhibited significant negative correlations with COL6A3 (r = -0.6) and FKBP10 (r = -0.48). However, no significant correlation was found between natural killer T cells and the two hub genes (Fig. 7D).

Fig. 7
figure 7

Analysis of immune cell infiltration. A The comparison of immune infiltration in the AF and control group. B Correlation analysis between differential immune cell in AF with COL6A3 and FKBP10. C The comparison of immune infiltration in the CAD and control group. D Correlation analysis between differential immune cell in CAD with COL6A3 and FKBP10

Full size image

Validation of hub genes in patients with stable CAD and AF

The expression levels of FKBP10 were significantly higher in all groups (CAD, AF, and AF + CAD) compared to controls (Fig. 8A). The expression levels of COL6A3 were significantly higher in the CAD and AF groups compared to controls (Fig. 8B). Although the AF + CAD group did not show a significant difference from the control group, there was a trend toward increased expression.

Fig. 8
figure 8

Validation of hub genes in patients with stable CAD and AF

Full size image

Discussion

A two-sample Mendelian randomization study showed that CAD is an independent risk factor for AF [21], suggesting a strong relationship between the two diseases. Another study showed a greater correlation between D-dimer and CAD in patients with AF compared to non-AF patients [22]. However, the shared mechanism of stable CAD and AF remains unclear. In this study, we first analyzed genetic datasets from both diseases to uncover common mechanisms and key biomarkers.

Twenty-eight overlapping genes were identified through WGCNA and positively associated with AF and stable CAD. These genes may be associated with collagen fibers, extracellular matrix structure, and wound healing. Atrial tissue fibrosis is an important feature of AF [23]. Histological studies show that AF-associated atrial remodeling manifests as fibrosis and enlarged myogenic fibers [24]. Wound healing is significant in the prognosis of myocardial infarction and affects the outcome of catheter ablation for AF, potentially causing serious complications [25, 26]. KEGG analysis revealed that these overlapping genes were significantly enriched in PI3K-Akt, ECM-receptor interaction, and relaxin signaling pathways. ECM deposition is a major pathological feature of AF [27]. PI3K-Akt could regulate cardiac fibroblast proliferation and ECM deposition [27], while ECM alterations are linked to inflammation and plaque formation in atherosclerosis [28]. Relaxin can cause vasorelaxation by modulating endothelium-dependent responses and is clinically evaluated for CAD treatment [29], also having anti-inflammatory and antifibrotic effects beneficial for AF patients [30]. Therefore, functional annotation suggests these overlapping genes may contribute to the pathology of AF and CAD.

Two hub genes, COL6A3 and FKBP10, were further identified by a machine learning algorithm as significantly up-regulated in both the AF and CAD groups. COL6A3 encodes collagen VI, involved in connective tissues development and aorta tensile strength [31]. It may regulate cardiovascular dysfunction in CAD patients through aging-related genes [32]. FKBP10, a chaperone protein, interacts with collagen I [33] and may be targeted by SHOX2 to promote conduction traits in AF [34]. FKBP10 also interacts with collagen VI and co-regulates lung fibroblast migration [30]. These previous studies demonstrate that COL6A3 and FKBP10 are key regulators of ECM remodeling, playing important roles in AF and CAD development. Nonetheless, their specific roles in AF and stable CAD remain to be elucidated. The DGIdb database predicted potential therapeutic drugs for AF and CAD, such as collagenase clostridium histolyticum for fibrotic plaque-like disease [35] and Ocriplasmin for ophthalmic diseases [36]. The ROC curve results implied that COL6A3 and FKBP10 have good diagnostic ability for AF and stable CAD, and real-time PCR results confirmed the bioinformatics results.

We constructed a “TF-mRNA” regulatory network to understand the possible regulatory relationship between COL6A3 and FKBP10 in AF and CAD. BRD4 and POLR2A may regulate these genes in CAD. Increased BRD4 expression in coronary arteries of pulmonary hypertension patients contributes to vascular remodeling [37]. Although BRD4 expression did not differ between AF and controls, it is important in cardiac fibrosis [38]. POLR2A is upregulated in Kawasaki disease patients and may be associated with coronary artery abnormalities [39]. Despite no difference in POLR2A between AF and controls, it is a potentially stable internal reference gene in different cardiac cavities and disease conditions [40].

Inflammation is a common feature of AF and CAD, and the incidence of these conditions appears to increase with inflammation [41]. Patients with AF often exhibit upregulation of platelet-binding factor and plasma stromal cell-derived factor 1, which are risk factors for CAD and play roles in immune cell recruitment [42]. Targeting inflammation through treatments may help prevent the development of AF or reduce its recurrence in patients with CAD [11]. It has shown that gut microbes may control inflammation in elderly AF patients by regulating regulatory T cells [43], and CAD patients have lower levels of CD56dim natural killer cells [44]. In some diseases, COL6A3 is associated with immune responses and may be regulated by the PI3K, Jun, and NF-κB pathways [45, 46]. FKBP proteins modulate immune responses with immunosuppressive drugs [47]. Combined with previous studies, it is hypothesized that COL6A3 and FKBP10 may influence the development of AF and stable CAD through immune cell infiltration.

This study has some limitations. The limited size of the dataset could potentially bias the results, especially the validation set GSE56885 for CAD, which likely represents the principal restriction of the study. Furthermore, these findings are based on public databases and require validation in larger clinical cohorts and experimental models.

Conclusion

Our study delved into the genetic basis and common mechanisms between AF and CAD. We identified COL6A3 and FKBP10 as significantly upregulated in both AF and stable CAD, with good diagnostic abilities and potential as therapeutic targets. These findings enhance our understanding of the shared mechanisms and offer promising insights into the treatment of these two diseases.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

AF:

atrial fibrillation

CAD:

coronary artery disease

WGCNA:

weighted gene co-expression network analysis

GEO:

Gene Expression Omnibus

TOM:

topological overlap matrix

GO:

gene ontology

KEGG:

Kyoto Encyclopedia of Genes and Genomes

FDR:

false discovery rate

LASSO:

least absolute shrinkage and selection operator

RF:

random forest

SVM:

support vector machine

ROC:

receiver operating characteristic

AUC:

area under the curve

TFs:

transcription factors

ssGSEA:

Single Sample Gene Set Enrichment Analysis

SD:

standard deviation

CC:

cellular component

MF:

molecular functions

BP:

biological processes

ECM:

Extra Cellular Matrix

MDSC:

myeloid-derived suppressor cells

References

  1. Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, et al. Global Burden of Cardiovascular diseases and Risk factors, 1990–2019: Update from the GBD 2019 study. J Am Coll Cardiol. 2020;76:2982–3021. https://doi.org/10.1016/j.jacc.2020.11.010.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Escudero-Martínez I, Morales-Caba L, Segura T. Atrial fibrillation and stroke: a review and new insights. Trends Cardiovasc Med. 2023;33:23–9. https://doi.org/10.1016/j.tcm.2021.12.001.

    Article  PubMed  Google Scholar 

  3. Akkaif MA, Sha’aban A, Daud NAA, Yunusa I, Ng ML, Sk Abdul Kader MA, et al. Coronary heart disease (CHD) in Elderly patients: which drug to choose, Ticagrelor and Clopidogrel? A systematic review and Meta-analysis of Randomized controlled trials. J Cardiovasc Dev Dis. 2021;8. https://doi.org/10.3390/jcdd8100123.

  4. Mallika V, Goswami B, Rajappa M. Atherosclerosis pathophysiology and the role of novel risk factors: a clinicobiochemical perspective. Angiology. 2007;58:513–22. https://doi.org/10.1177/0003319707303443.

    Article  CAS  PubMed  Google Scholar 

  5. Rencuzogullari I, Çağdaş M, Karakoyun S, Yesin M, Gürsoy MO, Artaç İ, et al. Propensity score matching analysis of the impact of Syntax score and syntax score II on new onset atrial fibrillation development in patients with ST segment elevation myocardial infarction. Ann Noninvasive Electrocardiol. 2018;23:e12504. https://doi.org/10.1111/anec.12504.

    Article  PubMed  Google Scholar 

  6. Verheugt FWA, ten Berg JM, Storey RF, Cuisset T, Granger CB, Antithrombotics. From aspirin to DOACs in coronary artery Disease and Atrial Fibrillation (Part 3/5). J Am Coll Cardiol. 2019;74:699–711. https://doi.org/10.1016/j.jacc.2019.02.080.

    Article  CAS  PubMed  Google Scholar 

  7. Sharma YP, Batta A, Makkar K, Hatwal J, A AG, Kaur N, et al. Angiographic profile and outcomes in persistent non-valvular atrial fibrillation: a study from tertiary care center in North India. Indian Heart J. 2022;74:7–12. https://doi.org/10.1016/j.ihj.2021.12.010.

    Article  PubMed  Google Scholar 

  8. Hiraya D, Sato A, Hoshi T, Watabe H, Yoshida K, Komatsu Y, et al. Impact of coronary artery disease and revascularization on recurrence of atrial fibrillation after catheter ablation: importance of ischemia in managing atrial fibrillation. J Cardiovasc Electrophysiol. 2019;30:1491–8. https://doi.org/10.1111/jce.14029.

    Article  PubMed  Google Scholar 

  9. Michniewicz E, Mlodawska E, Lopatowska P, Tomaszuk-Kazberuk A, Malyszko J. Patients with atrial fibrillation and coronary artery disease - double trouble. Adv Med Sci. 2018;63:30–5. https://doi.org/10.1016/j.advms.2017.06.005.

    Article  PubMed  Google Scholar 

  10. Liang F, Wang Y. Coronary heart disease and atrial fibrillation: a vicious cycle. Am J Physiol Heart Circ Physiol. 2021;320:H1–12. https://doi.org/10.1152/ajpheart.00702.2020.

    Article  CAS  PubMed  Google Scholar 

  11. Batta A, Hatwal J, Batta A, Verma S, Sharma YP. Atrial fibrillation and coronary artery disease: an integrative review focusing on therapeutic implications of this relationship. World J Cardiol. 2023;15:229–43. https://doi.org/10.4330/wjc.v15.i5.229.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Mekhael M, Marrouche N, Hajjar AHE, Donnellan E. The relationship between atrial fibrillation and coronary artery disease: understanding common denominators. Trends Cardiovasc Med. 2024;34:91–8. https://doi.org/10.1016/j.tcm.2022.09.006.

    Article  CAS  PubMed  Google Scholar 

  13. Artaç İ, Öğün M, Omar T, Karakayalı M, İliş D, Arslan A, et al. The importance of nitric oxide and oxidative stress in Atrial High-Rate episodes in patients with Cardiac devices. Turk Kardiyol Dern Ars. 2024;52:81–7. https://doi.org/10.5543/tkda.2023.07433.

    Article  PubMed  Google Scholar 

  14. Auslander N, Gussow AB, Koonin EV. Incorporating machine learning into established Bioinformatics frameworks. Int J Mol Sci. 2021;22. https://doi.org/10.3390/ijms22062903.

  15. Ishii M, Kaikita K, Yasuda S, Akao M, Ako J, Matoba T, et al. Risk prediction score for clinical outcome in atrial fibrillation and stable coronary artery disease. Open Heart. 2023;10. https://doi.org/10.1136/openhrt-2023-002292.

  16. Silva CAO, Morillo CA, Leite-Castro C, González-Otero R, Bessani M, González R, et al. Machine learning for atrial fibrillation risk prediction in patients with sleep apnea and coronary artery disease. Front Cardiovasc Med. 2022;9:1050409. https://doi.org/10.3389/fcvm.2022.1050409.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Trenkwalder T, Kessler T, Schunkert H. [Genetic testing in polygenic diseases: atrial fibrillation, arterial hypertension and coronary artery disease]. Herz. 2017;42:440–8. https://doi.org/10.1007/s00059-017-4576-z.

    Article  CAS  PubMed  Google Scholar 

  18. Cao Y, Cai X, Zhu Q, Li N. Screening and identification of potential biomarkers for obstructive sleep apnea via microarray analysis. Med (Baltim). 2021;100:e24435. https://doi.org/10.1097/md.0000000000024435.

    Article  CAS  Google Scholar 

  19. Cao Y, Zhu Q, Cai X, Wu T, Aierken X, Ahmat A, et al. Glial cell-derived neurotrophic factor functions as a potential candidate gene in Obstructive Sleep Apnea based on a combination of Bioinformatics and targeted capture sequencing analyses. Biomed Res Int. 2021;2021:6656943. https://doi.org/10.1155/2021/6656943.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Charoentong P, Finotello F, Angelova M, Mayer C, Efremova M, Rieder D, et al. Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade. Cell Rep. 2017;18:248–62. https://doi.org/10.1016/j.celrep.2016.12.019.

    Article  CAS  PubMed  Google Scholar 

  21. Yan T, Zhu S, Xie C, Zhu M, Weng F, Wang C, et al. Coronary artery Disease and Atrial Fibrillation: a bidirectional mendelian randomization study. J Cardiovasc Dev Dis. 2022;9. https://doi.org/10.3390/jcdd9030069.

  22. O’Neal WT, Soliman EZ, Howard G, Howard VJ, Safford MM, Cushman M, et al. Inflammation and hemostasis in atrial fibrillation and coronary heart disease: the REasons for Geographic and racial differences in Stroke study. Atherosclerosis. 2015;243:192–7. https://doi.org/10.1016/j.atherosclerosis.2015.09.009.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Moreira LM, Takawale A, Hulsurkar M, Menassa DA, Antanaviciute A, Lahiri SK, et al. Paracrine signalling by cardiac calcitonin controls atrial fibrogenesis and arrhythmia. Nature. 2020;587:460–5. https://doi.org/10.1038/s41586-020-2890-8.

    Article  CAS  PubMed  Google Scholar 

  24. Takahashi Y, Yamaguchi T, Otsubo T, Nakashima K, Shinzato K, Osako R, et al. Histological validation of atrial structural remodelling in patients with atrial fibrillation. Eur Heart J. 2023;44:3339–53. https://doi.org/10.1093/eurheartj/ehad396.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Frantz S, Bauersachs J, Ertl G. Post-infarct remodelling: contribution of wound healing and inflammation. Cardiovasc Res. 2009;81:474–81. https://doi.org/10.1093/cvr/cvn292.

    Article  CAS  PubMed  Google Scholar 

  26. Xinyu Z, Sheng X. Wound incidence and outcomes in atrial fibrillation: Comparing catheter ablation and anti-arrhythmic drug therapy. Int Wound J. 2023;10.1111/iwj.14612.

  27. Wang J, Guo R, Ma X, Wang Y, Zhang Q, Zheng N, et al. Liraglutide inhibits AngII-induced cardiac fibroblast proliferation and ECM deposition through regulating miR-21/PTEN/PI3K pathway. Cell Tissue Bank. 2023;24:125–37. https://doi.org/10.1007/s10561-022-10021-9.

    Article  CAS  PubMed  Google Scholar 

  28. Sharifi MA, Wierer M, Dang TA, Milic J, Moggio A, Sachs N, et al. ADAMTS-7 modulates atherosclerotic plaque formation by degradation of TIMP-1. Circ Res. 2023;133:674–86. https://doi.org/10.1161/circresaha.123.322737.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Acar-Sahan S, Guner O, Ates M, Kaya-Temiz T, Durmus N. In vitro effect of relaxin in the rat corpus cavernosum under hyperglycemic and normoglycemic conditions. Int J Impot Res. 2024;36:72–7. https://doi.org/10.1038/s41443-022-00653-6.

    Article  CAS  PubMed  Google Scholar 

  30. Martin B, Romero G, Salama G. Cardioprotective actions of relaxin. Mol Cell Endocrinol. 2019;487:45–53. https://doi.org/10.1016/j.mce.2018.12.016.

    Article  CAS  PubMed  Google Scholar 

  31. Benjamins JW, Yeung MW, van de Vegte YJ, Said MA, van der Linden T, Ties D, et al. Genomic insights in ascending aortic size and distensibility. EBioMedicine. 2022;75:103783. https://doi.org/10.1016/j.ebiom.2021.103783.

    Article  PubMed  Google Scholar 

  32. Savić R, Yang J, Koplev S, An MC, Patel PL, O’Brien RN, et al. Integration of transcriptomes of senescent cell models with multi-tissue patient samples reveals reduced COL6A3 as an inducer of senescence. Cell Rep. 2023;42:113371. https://doi.org/10.1016/j.celrep.2023.113371.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Ishikawa Y, Vranka J, Wirz J, Nagata K, Bächinger HP. The rough endoplasmic reticulum-resident FK506-binding protein FKBP65 is a molecular chaperone that interacts with collagens. J Biol Chem. 2008;283:31584–90. https://doi.org/10.1074/jbc.M802535200.

    Article  CAS  PubMed  Google Scholar 

  34. Hoffmann S, Schmitteckert S, Raedecke K, Rheinert D, Diebold S, Roeth R, et al. Network-driven discovery yields new insight into Shox2-dependent cardiac rhythm control. Biochim Biophys Acta Gene Regul Mech. 2021;1864:194702. https://doi.org/10.1016/j.bbagrm.2021.194702.

    Article  CAS  PubMed  Google Scholar 

  35. Mills SA, Gelbard MK. Sixty years in the making: collagenase Clostridium histolyticum, from benchtop to FDA approval and beyond. World J Urol. 2020;38:269–77. https://doi.org/10.1007/s00345-019-02818-3.

    Article  PubMed  Google Scholar 

  36. de Smet MD, Castilla M. Ocriplasmin for diabetic retinopathy. Expert Opin Biol Ther. 2013;13:1741–7. https://doi.org/10.1517/14712598.2013.853737.

    Article  CAS  PubMed  Google Scholar 

  37. Meloche J, Lampron MC, Nadeau V, Maltais M, Potus F, Lambert C, et al. Implication of inflammation and epigenetic readers in coronary artery remodeling in patients with Pulmonary arterial hypertension. Arterioscler Thromb Vasc Biol. 2017;37:1513–23. https://doi.org/10.1161/atvbaha.117.309156.

    Article  CAS  PubMed  Google Scholar 

  38. Song S, Liu L, Yu Y, Zhang R, Li Y, Cao W, et al. Inhibition of BRD4 attenuates transverse aortic constriction- and TGF-β-induced endothelial-mesenchymal transition and cardiac fibrosis. J Mol Cell Cardiol. 2019;127:83–96. https://doi.org/10.1016/j.yjmcc.2018.12.002.

    Article  CAS  PubMed  Google Scholar 

  39. Li SM, Liu WT, Yang F, Yi QJ, Zhang S, Jia HL. Phosphorylated proteomics analysis of human coronary artery endothelial cells stimulated by Kawasaki disease patients serum. BMC Cardiovasc Disord. 2019;19:21. https://doi.org/10.1186/s12872-018-0982-2.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Molina CE, Jacquet E, Ponien P, Muñoz-Guijosa C, Baczkó I, Maier LS, et al. Identification of optimal reference genes for transcriptomic analyses in normal and diseased human heart. Cardiovasc Res. 2018;114:247–58. https://doi.org/10.1093/cvr/cvx182.

    Article  CAS  PubMed  Google Scholar 

  41. Mekhael M, Marrouche N, Hajjar AHE, Donnellan E. The relationship between atrial fibrillation and coronary artery disease: Understanding common denominators. Trends Cardiovasc Med. 2022;10.1016/j.tcm.2022.09.006.

  42. Hu YF, Chen YJ, Lin YJ, Chen SA. Inflammation and the pathogenesis of atrial fibrillation. Nat Rev Cardiol. 2015;12:230–43. https://doi.org/10.1038/nrcardio.2015.2.

    Article  CAS  PubMed  Google Scholar 

  43. Zhang Y, Sun D, Zhao X, Luo Y, Yu H, Zhou Y, et al. Bacteroides fragilis prevents aging-related atrial fibrillation in rats via regulatory T cells-mediated regulation of inflammation. Pharmacol Res. 2022;177:106141. https://doi.org/10.1016/j.phrs.2022.106141.

    Article  CAS  PubMed  Google Scholar 

  44. Jonasson L, Backteman K, Ernerudh J. Loss of natural killer cell activity in patients with coronary artery disease. Atherosclerosis. 2005;183:316–21. https://doi.org/10.1016/j.atherosclerosis.2005.03.011.

    Article  CAS  PubMed  Google Scholar 

  45. Ezzati-Mobaser S, Malekpour-Dehkordi Z, Nourbakhsh M, Tavakoli-Yaraki M, Ahmadpour F, Golpour P, et al. The up-regulation of markers of adipose tissue fibrosis by visfatin in pre-adipocytes as well as obese children and adolescents. Cytokine. 2020;134:155193. https://doi.org/10.1016/j.cyto.2020.155193.

    Article  CAS  PubMed  Google Scholar 

  46. Wang H, Lu L, Liang X, Chen Y. Identification of prognostic genes in the pancreatic adenocarcinoma immune microenvironment by integrated bioinformatics analysis. Cancer Immunol Immunother. 2022;71:1757–69. https://doi.org/10.1007/s00262-021-03110-3.

    Article  CAS  PubMed  Google Scholar 

  47. Alqudah A, AbuDalo R, Qnais E, Wedyan M, Oqal M, McClements L. The emerging importance of immunophilins in fibrosis development. Mol Cell Biochem. 2023;478:1281–91. https://doi.org/10.1007/s11010-022-04591-1.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

This work was supported by the Shandong Medical Association Clinical Research Fund - Qilu Special Fund (YXH2022ZX02140).

Author information

Authors and Affiliations

  1. Department of Cardiology, The Affiliated Taian City Central Hospital of Qingdao University, Taian, 271000, China

    Ke Wu & Fan Li

  2. Shandong University, Jinan, 250012, China

    Ke Wu & Nannan Li

  3. Intensive Care Department, The Affiliated Taian City Central Hospital of Qingdao University, Taian, 271000, China

    Hao Chen

  4. Department of Interventional Radiology, The Affiliated Taian City Central Hospital of Qingdao University, Taian, 271000, China

    Xiangjuan Meng

  5. Medical Imaging Department, The Affiliated Taian City Central Hospital of Qingdao University, Taian, 271000, China

    Lin Chen

  6. Department of Respiratory and Critical Care, The Affiliated Taian City Central Hospital of Qingdao University, No. 29 Longtan Road, Taian, Shandong, 27100, China

    Nannan Li

Authors
  1. Ke Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hao Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiangjuan Meng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lin Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Nannan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

K.W.: conceptualization, investigation, data curation, validation, writing – original draft; H.C.: investigation, methodology, validation, visualization; F.L.: software, validation, visualization; X.M.: software, methodology, investigation; L.C.: validation, visualization; N.L.: project administration, resources, methodology, data curation; All authors read and approved the final manuscript.

Corresponding author

Correspondence to Nannan Li.

Ethics declarations

Ethics approval and consent to participate

This study was approved by the Ethics Committee of the Affiliated Taian City Central Hospital of Qingdao University (2024-05-11). All enrolled patients received written informed consent before this study. And consent to participate and publish was acquired from each patient/participant. This study strictly adhered to the Declaration of Helsinki.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Clinical trial

Not applicable.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

12872_2024_4062_MOESM2_ESM.png

Supplementary Material 2: Figure S1 Batch effect removal in merged GSE79768 and GSE115574 datasets for AF. (A) Box plot showing sample distribution before and (D) after batch effect removal. (B) Density plot illustrating sample distribution before (top) and after (bottom) batch effect removal. (C) UMAP plot demonstrates that samples from the two datasets cluster separately before batch effect removal (top) and intermingle after batch effect removal (bottom).

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, K., Chen, H., Li, F. et al. Identification of potential biomarkers for atrial fibrillation and stable coronary artery disease based on WGCNA and machine algorithms. BMC Cardiovasc Disord 24, 401 (2024). https://doi.org/10.1186/s12872-024-04062-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12872-024-04062-z

Keywords

BMC Cardiovascular Disorders

ISSN: 1471-2261

Contact us