Identification and validation of the common pathogenesis and hub biomarkers in chronic obstructive pulmonary disease complicated with atrial fibrillation

Identification and validation of the common pathogenesis and hub biomarkers in chronic obstructive pulmonary disease complicated with atrial fibrillation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Identification and validation of the common pathogenesis and hub biomarkers in chronic obstructive pulmonary disease complicated with atrial fibrillation Xiaojie Deng, Yi Chen, Huaxing Huang, Xiahui Yang, Zhihua Gan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8439150/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 13 You are reading this latest preprint version Abstract Although increasing evidence has shown that there is a correlation between the progression of chronic obstructive pulmonary disease (COPD) and atrial fibrillation (AF), the molecular mechanism underlying their co-occurrence remains unknown. The purpose of this study was to further explore the underlying mechanism and biomarkers for the co-occurrence of COPD and AF. Thus, we analyzed the GEO data of COPD and AF patients via the following methods: GO functional enrichment, protein–protein interaction network construction and module analysis. RT‒qPCR was performed to validate the expression of the hub genes in the COPD and AF patient samples. Bioinformatics analysis confirmed that five hub genes, CSF2RB, RNase6, MS4A6A, TRIQK, and LRRN3, are significant novel biomarkers of COPD and AF and may be beneficial for the early diagnosis and treatment of COPD. Furthermore, the hub genes are related to immune cell infiltration and play important roles in the COPD-AF immune microenvironment. T cells may contribute to the pathogenesis and molecular mechanism of COPD-AF. RT‒qPCR revealed that the expression of RNASE6 and CSF2RB was associated with the co-occurrence of the two diseases. This provides an in-depth study of the underlying mechanism of COPD-AF. Health sciences/Biomarkers Health sciences/Cardiology Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Biological sciences/Genetics Health sciences/Medical research COPD Atrial fibrillation Hub genes T cells Immune cell infiltration Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Chronic obstructive pulmonary disease (COPD) is a respiratory condition that causes persistent airflow limitation, increasing the risk of adverse events such as heart failure, thromboembolism, hospitalization, and mortality. Epidemiological data show that the global incidence of COPD was estimated at 391 million individuals in 2019, with a projected significant increase in the future [ 1 ] . Cigarette smoking is the primary risk factor for COPD and is also linked to environmental pollution, genetic factors, and susceptibility to infections. This disease causes chronic inflammation in the lungs, leading to damage to lung tissue and disruption of the body's normal repair and defense mechanisms [ 2 – 3 ] . Clinical symptoms of COPD include cough, sputum production, difficulty breathing, expiratory wheezing, and profound fatigue. These symptoms significantly impact the patient's quality of life and have a considerable effect on prognosis. Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia in clinical practice. It is caused by irregular atrial impulses that initiate multiple microreentrant circuits, leading to a disruption in atrial rhythm. This results in disorganized excitations and ineffective contractions of the atria, increasing the risk of complications such as heart failure, stroke, mortality, and the formation of intracardiac thrombi with consequent embolic events. AF is a common complication among individuals afflicted with chronic obstructive pulmonary disease (COPD) [ 4 – 5 ] . Globally, the incidence of AF ranges from 9–10%, increasing concomitantly with age. The primary predisposing factors for AF include hypertension, coronary artery disease, valvular heart disease, heart failure, hyperthyroidism, obesity, diabetes, and sleep apnea [ 6 ] . Clinical manifestations of AF include symptoms such as palpitations, chest tightness, dyspnea, fatigue, and episodes of syncope, which have a substantial impact on both the quality of life and prognosis of affected individuals. COPD and AF are two diseases characterized by high heterogeneity and complexity, with intricate interconnections and mutual influences. Recent research suggests that there are numerous shared aspects of pathogenesis and risk factors between these two conditions. Both COPD and AF are associated with factors such as inflammation, oxidative stress, protease-antiprotease imbalance, and autonomic nervous system dysregulation [ 7 – 8 ] . COPD may induce or exacerbate AF through various pathways. Conversely, AF may impact the progression and prognosis of COPD. The incidence of AF in COPD patients is 2–4 times greater than that in the general population, while the occurrence of COPD in AF patients is 1.5-2 times greater than that in the general population [ 9 – 10 ] . The coexistence of COPD and AF increases the risk of mortality, cardiovascular events, and hospitalization for individuals. However, the shared pathogenic mechanisms between COPD and AF remain elusive, and effective prevention and treatment strategies are currently lacking. The onset and progression of COPD and AF involve multiple changes at the molecular and cellular levels, including inflammation, oxidative stress, autophagy, fibrinolysis, vascular remodeling, myocardial remodeling, electrophysiological abnormalities, and the regulation of gene expression [ 11 – 12 ] . In recent years, several studies have identified several molecular pathways and biomarkers that are shared between COPD and AF, such as inflammatory factors, oxidative stress indicators, platelet-activating factors, and cardiac calmodulin[9]. However, these studies have small sample sizes, inconsistent results, and lack validation and mechanistic explanations. Therefore, studying the common pathogenesis between COPD and AF, exploring the causal relationship between the two, and identifying common risk factors and preventive measures for both are necessary and meaningful. This will not only help to improve the understanding and diagnosis of COPD and AF but also help to provide new targets and strategies for the treatment of COPD and AF. The aim of this study was to investigate potential biomarkers and pathways linked to COPD and AF. To achieve this goal, we integrated transcriptomic data related to COPD and AF from the Gene Expression Omnibus (GEO) and used the 'LIMMA' package in conjunction with weighted gene coexpression network analysis (WGCNA) to identify differentially expressed genes and key modules for each disease. Analyses were conducted using intersection and machine learning methods to identify shared diagnostic genes between the two diseases. The performance of these genes was validated using external datasets. Materials and methods Data download The GEO database (http://www.ncbi.nlm. nih.gov/geo), known as GENE EXPRESSION OMNIBUS, is a gene expression profile constructed by the National Center for Biotechnology Information (NCBI). Three microarray datasets of AF (GSE31821, GSE79768 and GSE115574) were downloaded from the GEO database. The GSE42057 dataset contains 93 COPD patients and 43 normal controls. Differential expression analysis The molecular mechanism of COPD and AF was analyzed by using the R-Pack "limma". The DEGs between the normal group and disease group were identified. The screening conditions for differentially expressed genes were a P value0.585. A volcano map and heatmap of the differentially expressed genes were generated. Selection and analysis of hub genes The hub genes were identified using Cytoscape’s cytoHubba plug-in. To analyze the hub genes, seven standard algorithms (MCC, MNC, degree, closeness, radiality, stress, and EPC) were used. The key hub genes shared across these seven approaches were filtered using an UpSet diagram. Subsequently, a coexpression network of these hub genes was constructed via GeneMANIA (http://www.genemania.org/), which is a widely used tool for revealing internal associations in gene sets. Validation of hub gene expression in datasets and disease samples The mRNA expression of the identified hub genes was verified in the GSE42057, GSE31821, GSE11574 and GSE79768 datasets. The GSE31821 dataset contains 2 controls and 4 AF patients. GSE11574 consists of 31 control samples and 28 AF samples. Comparisons between two sets of data were performed with a t test. A p value <0.05 was considered significant. RT‒qPCR was performed to analyze the mRNA expression of the hub genes in AF and COPD samples and normal controls. This study was approved by the Ethics Committee of the Second Affiliated Hospital of Guangzhou Medical University. Total RNA was isolated from colon tissues with Simgen. qPCR and data analysis were performed using a LightCycler 96 instrument. The relative expression levels were estimated using GAPDH as an internal reference. The experiments were conducted three times with biological duplicates. GO semantic similarity Based on the GO semantic similarity, we rank proteins by the functional similarity between proteins and interacting proteins. GO semantic similarity has been verified by correlation between gene expression profiles [13] , which provides a basis for functional comparisons of gene products and has been widely used in bioinformatics, such as protein‒protein interaction analysis [14] , pathway analysis [15] and gene function prediction [16] . Here, we measured the functional similarity between proteins in terms of cellular components (CCs) and biological pathways (BPs) to explore the relationships between each protein and its interacting proteins. The semantic similarity between interacting histones in CCs and BPs was determined by the GoSemsim software package [17] , which is implemented in a more accurate way by considering the GO topology [18] . Immune cell infiltration analysis The ssGSEA method distinguishes 29 human immune cell phenotypes, including T cells, B cells, and NK cells. In this study, the ssGSEA algorithm was used to quantify immune cells in the expression profile. CMap drug prediction The CMap is a gene expression profiling database based on interventional gene expression data developed by the Broad Institute. It is primarily used to reveal associations between small molecules, gene expression and disease. 8. Statistical analysis All the statistical analyses were performed using R (version 4.2.2). All the statistical tests were two-sided, and p<0.05 indicated statistical significance. Results Identification of differentially expressed genes in COPD and AF patients As shown in the microarray results, DEGs associated with AF (GSE31821, GSE79768 and GSE115574) and COPD (GSE42057) were identified (Figure 1A, B). A total of 47 DEGs with the same expression trends were obtained, including 34 upregulated genes and 13 downregulated genes (Figure 1C and D). Analysis of the functional characteristics of common differentially expressed genes GO and KEGG pathway enrichment analyses were used to investigate the biological functions and pathways associated with the 47 common DEGs. GO analysis revealed that these genes were mainly enriched in cell death, biotic-stimulus, vacuolar-lumen, and lipopolysaccharide receptor (Figure 2A and B). According to the KEGG pathway analysis, the three significantly enriched pathways were related to hypertrophic cardiomyopathy, cell adhesion molecules, coreceptor activity, chemokine binding and polysome binding (Figure 2C and D). Protein–protein interaction network construction With the use of Cytoscape, a PPI network of common DEGs with total scores greater than 0.4 was created (Figure 3A). KEGG pathway analysis revealed that these genes were mostly involved in cytokine–cytokine receptor interactions and the NF-κB signaling pathway (Figure 3B). GO analysis revealed that these genes are related to the immune system response and necroptotic progression (Figure 3C). Selection and analysis of hub genes After calculation with the cytoHubba plug-in, Venn diagrams showing 5 common hub genes, CSF2RB, MS4A6A, RNASE6, TRIQK and LRRN3, were validated (Figure 4A). The coexpression network of these hub genes was explored using the GeneMANIA database (Figure 4B). GO analysis revealed that these genes are involved mainly in endonuclease activity, nuclease activity, and the IL-3 signaling pathway (Figure 4C). In addition, KEGG pathway analysis revealed that the hub genes were mostly involved in the immune receptor signaling pathway and cytokine receptor signaling pathway (Figure 4D). Validation of hub gene expression We selected the COPD datasets and AF dataset to confirm the expression levels of these hub genes. Compared with those in the normal group, all the hub genes except LRRN3 were significantly upregulated in the COPD dataset (GSE42507) (Figure 5A-D). The expression of LRRN3 in COPD tissues was lower than that in normal colon tissues (Figure 5E). Similarly, the expression level of CSF2RB in the disease group was significantly greater than that in the normal group in the AF dataset (GSE115574) (Figure 5F). However, the expression of TRIQK and LRRN3 was lower in the AF group than in the normal group. Therefore, we performed RT‒qPCR to confirm the expression of the hub genes in these two disease tissues. We collected the serum of the patients and then measured the mRNA expression levels of the hub genes. As shown in Figure 6, we found that the mRNA expression of CSF2RB and RNASE6 in the normal group was lower than that in the disease group. However, the mRNA expression of these genes in the COPD-AF group was significantly greater than that in the COPD, AF and normal groups. The mRNA expression of TRIQK, LRRN4 and MS4A6A in the normal group was lower than that in the disease group. However, there was no significant difference in the mRNA expression of these three genes between the COPD-AF group and the normal group. Expression profile of hub genes and immune infiltration analysis We analyzed the relationship between the hub genes and immune infiltration in the two disease datasets and further explored the underlying molecular mechanism of the hub genes affecting the onset of disease. The proportions of immune cells and their correlations with each other are shown in Figure 7A-D. In addition, immune infiltration showed that four immune cell types, including gamma delta T cells, resting NK cells, neutrophils and dendritic cells, were activated in the COPD samples and in the AF samples. We further explored the relationships between the hub genes and immune cells and found that several hub genes were strongly correlated with immune cells (Figure 7E-F). In addition, the prevalence of T cells in the two diseases significantly correlated with MS4A6A. These findings suggest that the hub genes are related to immune cell infiltration and play important roles in the COPD-AF immune microenvironment. CMap drug prediction We divided the top 50 genes into two groups and performed drug prediction with the Connectivity Map database. The results showed that the expression profiles of drug perturbations, such as cefalexin, cephaeline, glipizide, lidoflazine and propranolol, were more significantly negatively correlated with the expression profiles of disease perturbations, suggesting that these drugs could alleviate or even reverse the disease state (Figure S1). Discussion Several studies have reported a relationship between COPD and AF. Patients with COPD who had a reduced forced expiratory volume in one second (FEV1) were more likely to have AF [ 19 – 22 ] . Moreover, patients with COPD have greater postoperative AF (23.3% vs. 11.0%, respectively, p < 0.0001) than patients without COPD [ 23 – 25 ] . On the other hand, impaired pulmonary function, hypercapnia, and high pulmonary artery systolic pressure in COPD patients may indicate the occurrence of AF [ 26 ] . Regarding the pathophysiological mechanism, some reports have shown that hypoxia may be an unfavorable factor in the atrial conduction of COPD patients, which leads to the occurrence of AF [ 27 ] . On the one hand, hypercapnia is also a risk factor for AF occurrence in COPD patients. Capnia may result in an increased possibility of atrial refractoriness and an obvious decline in atrial conduction. On the other hand, left ventricular diastolic dysfunction may serve as another possible pathophysiological mechanism for persistent and permanent AF initiation [ 28 ] . COPD contributes to ventricular diastolic dysfunction, which is the basis for the incidence of AF in COPD [ 29 – 31 ] patients. However, the pathological mechanism underlying these two diseases is still unclear. Although there are many biomarkers for predicting the progression of COPD and AF, a predictive model for the co-onset of COPD and AF has not been reported. Thus, an exploration of the pathophysiological mechanisms implicated in COPD in AF is needed to identify better therapeutic options and reduce mortality in these patients. In our study, five hub genes were identified to reveal the underlying mechanisms associated with the co-occurrence of COPD and AF. In the present study, a series of bioinformatics analyses were performed on COPD and AF patients, and 47 DEGs common to both COPD and AF patients were obtained from the GEO database. The results of GO enrichment analysis showed that the DEGs were mainly enriched in the immune response. These results indicated that the DEGs between COPD patients and AF patients are related to inflammation and the immune response, which is consistent with previous studies [ 32 – 33 ] . In addition, KEGG analysis revealed that the DEGs were mainly enriched in arrythmogenic right ventricular cardiomyopathy, hypertrophic cardiomyopathy, dilated cardiomyopathy and cytokine‒cytokine receptor interactions. We further classified these pathways according to the KEGG pathway database. We found that these genes are mainly related to immunity and inflammation. Many studies have shown that immune inflammation plays a critical role in AF and COPD [ 34 ] . Subsequently, according to the MCODE plug-in and cytoHubba plug-in of Cytoscape, we screened nine overlapping DEGs, namely, LRRN3, CSF2RB, MS4A6A, RNASE6 and TRIQK, as hub genes in the PPI network. These five genes were all upregulated in both AP patients and COPD patients, suggesting that these genes may play important roles in the mechanism of AF and COPD. LRRN3 was reported to be associated with cigarettes because it has three DNA methylation loci (cg09837977, cg05221370, and cg11556164) in its 5’UTR, which are related to smoking [ 35 ] . LRRN3 may mediate the development of HF following AMI via the MAPK signaling pathway and its downstream effector. identified two genes, IL1R2 and LRRN3, as possible target genes for the development of HF following AMI [ 36 ] . CSF2RB, a CSF2 receptor, can enhance the cardiac homing of intravenously delivered MSCs for repairing ischemic heart injury [ 37 – 39 ] . CSF2RB is also known as a granulocyte–macrophage colony–stimulating factor that responds to distinct types of AMI [ 40 ] . Furthermore, CSF2RB may also be an M2 macrophage-related biomarker in the progression of acute myocardial infarction [ 41 ] . CSF2RB could be an effective predictor of the progression of patients with lung adenocarcinoma and a potential target for cancer treatment [ 42 ] . MS4A6A is a promising biomarker for immunotherapy of lung adenocarcinoma and is associated with HRD in lung adenocarcinoma [ 43 – 44 ] . RNASE6, as a prognostic target, may play a role in the development of atherosclerosis based on functional differences in TAMs [ 45 ] . As shown above, the identified functions of these five hub genes highlighted the correlation between respiratory and cardiac disease. We utilized RT‒qPCR to measure the expression of the hub genes in the normal group, COPD group, AF group and co-occurrence group. The mRNA expression of CSF2RB, RNASE6, TRIQK, LRRN4 and MS4A6A in the normal group was significantly lower than that in the disease group, which indicated that these genes may be risk factors for these two diseases. Moreover, the mRNA expression of these genes in the COPD-AF group was greater than that in the COPD, AF and normal groups. However, the mRNA expression of TRIQK, LRRN4 and MS4A6A in the normal group was lower than that in the disease group. There was no significant difference between the COPD-AF group and the normal group. These results suggested that CSF2RB and RNASE6 may play a role in the mechanism underlying the on-occurrence of these two diseases. It is generally recognized that the differential expression of T-cell subsets plays a role in the pathogenesis of COPD, especially in the airways and lung parenchyma [ 46 ] . Furthermore, CD4 + T cells mainly produce activating cytokines, which may assist in the physical training and prognosis of COPD patients [ 47 – 48 ] . Numerous studies have shown a relationship between inflammation and AF [ 49 – 50 ] . A previous study showed that the expression of PD-1 and PD-L1/2 is substantially downregulated on CD4 + T cells in patients with paroxysmal and persistent AF. Moreover, T-cell excitation may regulate cytokines via the PD-1 and PD-L1 pathways, which participate in AF pathogenesis [ 51 – 52 ] . Consistent with previous studies, our study analyzed the immune levels of immune cells, such as T cells, NK cells, macrophages, and neutrophils, in AF and COPD patients via the ImmuCellAI and CIBERSORT databases. These results confirmed that high immune cell levels are abnormal in COPD and AF patients. The immune infiltration data revealed that four immune cell types, namely, gamma delta T cells, resting NK cells, neutrophils and dendritic cells, were activated in the COPD samples and in the AF samples. In addition, the prevalence of T cells in the two diseases significantly correlated with MS4A6A. This suggests that the immune function of T cells may be the key point in the occurrence of COPD and AF. Overall, we utilized these hub genes to construct a diagnostic prediction model that will be beneficial for the prevention and diagnosis of postoperative COPD-AF to provide a theoretical basis for the molecular mechanism of COPD and AF co-occurrence. Furthermore, our study also predicted potential therapeutic drugs for 5 hub genes, such as cefalexin, cephaeline, glipizide, lidoflazine and propranolol, which will support future studies of the treatment of this disease. However, there are several limitations in our study. First, our research is based on bioinformatics analysis. Therefore, some prospective clinical studies should be performed to verify the prognostic characteristics of these patients. Second, the molecular mechanisms underlying how the five hub genes are involved in the pathogenesis and progression of COPD-AF are still unknown and require further biological and experimental verification. In addition, animal experiments are needed for potential therapeutic drugs. Finally, further investigation of the relationship between AF and COPD progression may still be needed. In conclusion, a total of five hub genes involved in the development and progression of COPD-AF were identified and integrated into a diagnostic model for two diseases. This study provides novel insights into the pathogenesis and mechanisms of the occurrence of COPD and AF. Conclusion In our study, we analyzed the GEO data of COPD and AF patients via the following methods: Gene Ontology (GO) functional enrichment, protein–protein interaction network construction and module analysis. Bioinformatics analysis confirmed five hub genes as significant novel biomarkers of COPD and AF, which may be beneficial for the early diagnosis and treatment of COPD. The above analysis revealed that immunity plays an important role in the co-occurrence of COPD-AF and that T cells may contribute to the pathogenesis and molecular mechanism of COPD-AF. This provides an in-depth study of the underlying mechanism of COPD-AF. Declarations Ethics approval and consent to participate This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of The Second Affiliated Hospital of Guangzhou Medical University (Approval No: 2021-23-02). Informed consent was obtained from all individual participants included in the study. Consent for publication Written informed consent was obtained from all individual participants included in the study. The participants consented to the publication of the results derived from their data. Availability of data and materials The datasets generated and/or analysed during the current study are available in the Gene Expression Omnibus (GEO) repository, under accession numbers GSE31821, GSE79768, GSE115574, and GSE42057. Competing Interests The authors declare that there are no conflicts of interest. Funding This work was supported by the Natural Science Foundation of Guangdong Province (Grant No. 2022A1515110107) and the China Postdoctoral Science Foundation (Grant No. 2023M740831). Authors' contributions D-XJ made major contributions to the conception and design of the study and the writing and revision of the manuscript; Y-C and G-ZH contributed to the data acquisition, analysis, and interpretation of the data; H-HX and Y-XH made important contributions to the revision of the manuscript. YJ put forward many important suggestions for the revision of this article, which greatly improved the text. All the authors have read and approved the final manuscript. 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The PD-1 with PD-L1 Axis Is Pertinent with the Immune Modulation of Atrial Fibrillation by Regulating T-Cell Excitation and Promoting the Secretion of Inflammatory Factors. J IMMUNOL RES 2022, 2022: 3647817. Additional Declarations No competing interests reported. Supplementary Files abstract.jpg FigureS1.png Figure S1. CMap showing the drug predictions for patients with COPD and AF. 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11:29:25","extension":"html","order_by":31,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":118358,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8439150/v1/fab85666ca4925c9eb0d5759.html"},{"id":100583018,"identity":"d4c0dd10-dd15-4971-9517-7882f9aee830","added_by":"auto","created_at":"2026-01-19 11:29:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":8300228,"visible":true,"origin":"","legend":"\u003cp\u003e(A) The volcano map of AF-GEO. (B) Volcano map of GSE42057. Upregulated genes are marked in red; downregulated genes are marked in green. (C) The downregulated genedatabase showed an overlap of 13 DEGs. (D) Thedatabase of upregulated genes showed an overlap of 34 DEGs.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8439150/v1/6d1241f24f21bda37338aade.png"},{"id":100582937,"identity":"544b88f2-c673-4abb-b9c3-252f65e5f2bd","added_by":"auto","created_at":"2026-01-19 11:28:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":11161549,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eGOBP\u003cstrong\u003e \u003c/strong\u003eof\u003cstrong\u003e \u003c/strong\u003e34 upregulated genes; (B) GOCC of 34 upregulated genes; (C) KEGG pathway of 34 upregulated genes; (D) GOMF of 34 upregulated genes; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8439150/v1/f01eb360ef26722b84d67c81.png"},{"id":100582980,"identity":"a706804d-ff19-4d3b-8385-8650529d2d91","added_by":"auto","created_at":"2026-01-19 11:29:03","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1230561,"visible":true,"origin":"","legend":"\u003cp\u003e(A) PPI network diagram. Green indicates upregulated genes, and red‒violet indicates downregulated genes. (B-C) GO and KEGG enrichment analyses of the modular genes. PPI, protein–protein interaction.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8439150/v1/b4a6e03f0f8f854b48f5b6a5.jpg"},{"id":100582946,"identity":"b99c99ed-bf88-479c-9df0-e81c6d0d9426","added_by":"auto","created_at":"2026-01-19 11:28:53","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":14183170,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Venn diagram showing that the five algorithms identified nine overlapping hub genes. (B) Hub genes and their coexpressed genes were analyzed via GeneMANIA. (C, D) GO enrichment analyses of the hub genes. The outermost circle is the term on the right, and the inner circle on the left represents the significant p value of the corresponding pathway of the gene. GO, Gene Ontology.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8439150/v1/76388ea582f087410d9ffd49.png"},{"id":100582897,"identity":"4cbea9b0-3234-427c-96ab-9d373aa95785","added_by":"auto","created_at":"2026-01-19 11:28:34","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":4954894,"visible":true,"origin":"","legend":"\u003cp\u003e(A-E) Validation of the expression of the nine hub genes in the external COPD gene expression profile (GSE42057). (F-J) Validation of the expression of the nine hub genes in the external AF gene expression profile (GSE115574). P \u0026lt; 0.05 was considered\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-8439150/v1/f3fcfcfb18fd3b03cdfb291c.png"},{"id":100583023,"identity":"c0a13bd9-c17d-406f-acf5-2645524621bd","added_by":"auto","created_at":"2026-01-19 11:29:26","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1336210,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of the hub genes via RT‒qPCR. P \u0026lt; 0.05 is indicated.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-8439150/v1/68b630fafb707b051263b9e3.png"},{"id":100582959,"identity":"b00851b0-7e82-411e-a528-78862b1243d5","added_by":"auto","created_at":"2026-01-19 11:28:58","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":27185085,"visible":true,"origin":"","legend":"\u003cp\u003eCOPD and AF immune cell composition. (A). Infiltrating immune cells were plotted in a stacked bar chart for the COPD group. (B). A violindiagram indicated that the COPD group exhibited significantly different types of immune cells. (C). A stacked bar chart shows the characteristics of infiltrating immune cells in the AF group. (D). Evident differences in immune cell types are shown in the violin diagram of the AF group. (E). Correlations between Hub gene expression and immune cells\u003c/p\u003e\n\u003cp\u003ein the COPD group. (F). Relationships between the expression of Hub genes and immunity in AF patients, P \u0026lt; 0.05\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-8439150/v1/890b7aa2d02c53916cc0fe23.png"},{"id":100597954,"identity":"4ecdf795-214c-4114-9b87-699e34a2368f","added_by":"auto","created_at":"2026-01-19 14:21:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":49153605,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8439150/v1/b9231465-306f-43f4-b4c9-140d43c50b35.pdf"},{"id":100582990,"identity":"f7193d20-58bc-4000-b4c1-d09be30e0a40","added_by":"auto","created_at":"2026-01-19 11:29:12","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":392503,"visible":true,"origin":"","legend":"","description":"","filename":"abstract.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8439150/v1/3acd2e5c7bb85bb2cfa2e8b5.jpg"},{"id":100583008,"identity":"c29701a3-3a9f-4396-a37d-74f831a78ad8","added_by":"auto","created_at":"2026-01-19 11:29:22","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2045298,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFigure S1.\u003c/strong\u003e CMap showing the drug predictions for patients with COPD and AF.\u003c/p\u003e","description":"","filename":"FigureS1.png","url":"https://assets-eu.researchsquare.com/files/rs-8439150/v1/c94d30cde48a04e846b7b008.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification and validation of the common pathogenesis and hub biomarkers in chronic obstructive pulmonary disease complicated with atrial fibrillation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChronic obstructive pulmonary disease (COPD) is a respiratory condition that causes persistent airflow limitation, increasing the risk of adverse events such as heart failure, thromboembolism, hospitalization, and mortality. Epidemiological data show that the global incidence of COPD was estimated at 391\u0026nbsp;million individuals in 2019, with a projected significant increase in the future\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Cigarette smoking is the primary risk factor for COPD and is also linked to environmental pollution, genetic factors, and susceptibility to infections. This disease causes chronic inflammation in the lungs, leading to damage to lung tissue and disruption of the body's normal repair and defense mechanisms\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Clinical symptoms of COPD include cough, sputum production, difficulty breathing, expiratory wheezing, and profound fatigue. These symptoms significantly impact the patient's quality of life and have a considerable effect on prognosis.\u003c/p\u003e \u003cp\u003eAtrial fibrillation (AF) is the most common sustained cardiac arrhythmia in clinical practice. It is caused by irregular atrial impulses that initiate multiple microreentrant circuits, leading to a disruption in atrial rhythm. This results in disorganized excitations and ineffective contractions of the atria, increasing the risk of complications such as heart failure, stroke, mortality, and the formation of intracardiac thrombi with consequent embolic events. AF is a common complication among individuals afflicted with chronic obstructive pulmonary disease (COPD)\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Globally, the incidence of AF ranges from 9\u0026ndash;10%, increasing concomitantly with age. The primary predisposing factors for AF include hypertension, coronary artery disease, valvular heart disease, heart failure, hyperthyroidism, obesity, diabetes, and sleep apnea\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Clinical manifestations of AF include symptoms such as palpitations, chest tightness, dyspnea, fatigue, and episodes of syncope, which have a substantial impact on both the quality of life and prognosis of affected individuals.\u003c/p\u003e \u003cp\u003eCOPD and AF are two diseases characterized by high heterogeneity and complexity, with intricate interconnections and mutual influences. Recent research suggests that there are numerous shared aspects of pathogenesis and risk factors between these two conditions. Both COPD and AF are associated with factors such as inflammation, oxidative stress, protease-antiprotease imbalance, and autonomic nervous system dysregulation\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. COPD may induce or exacerbate AF through various pathways. Conversely, AF may impact the progression and prognosis of COPD. The incidence of AF in COPD patients is 2\u0026ndash;4 times greater than that in the general population, while the occurrence of COPD in AF patients is 1.5-2 times greater than that in the general population\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. The coexistence of COPD and AF increases the risk of mortality, cardiovascular events, and hospitalization for individuals. However, the shared pathogenic mechanisms between COPD and AF remain elusive, and effective prevention and treatment strategies are currently lacking.\u003c/p\u003e \u003cp\u003eThe onset and progression of COPD and AF involve multiple changes at the molecular and cellular levels, including inflammation, oxidative stress, autophagy, fibrinolysis, vascular remodeling, myocardial remodeling, electrophysiological abnormalities, and the regulation of gene expression\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. In recent years, several studies have identified several molecular pathways and biomarkers that are shared between COPD and AF, such as inflammatory factors, oxidative stress indicators, platelet-activating factors, and cardiac calmodulin[9]. However, these studies have small sample sizes, inconsistent results, and lack validation and mechanistic explanations. Therefore, studying the common pathogenesis between COPD and AF, exploring the causal relationship between the two, and identifying common risk factors and preventive measures for both are necessary and meaningful. This will not only help to improve the understanding and diagnosis of COPD and AF but also help to provide new targets and strategies for the treatment of COPD and AF.\u003c/p\u003e \u003cp\u003eThe aim of this study was to investigate potential biomarkers and pathways linked to COPD and AF. To achieve this goal, we integrated transcriptomic data related to COPD and AF from the Gene Expression Omnibus (GEO) and used the 'LIMMA' package in conjunction with weighted gene coexpression network analysis (WGCNA) to identify differentially expressed genes and key modules for each disease. Analyses were conducted using intersection and machine learning methods to identify shared diagnostic genes between the two diseases. The performance of these genes was validated using external datasets.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003col\u003e\n \u003cli\u003e\u003cstrong\u003eData download\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe GEO database (http://www.ncbi.nlm. nih.gov/geo), known as GENE EXPRESSION OMNIBUS, is a gene expression profile constructed by the National Center for Biotechnology Information (NCBI). Three microarray datasets of AF (GSE31821, GSE79768 and GSE115574) were downloaded from the GEO database. The GSE42057 dataset contains 93 COPD patients and 43 normal controls.\u003c/p\u003e\n\u003col start=\"2\"\u003e\n \u003cli\u003e\u003cstrong\u003eDifferential expression analysis\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe molecular mechanism of COPD and AF was analyzed by using the R-Pack \u0026quot;limma\u0026quot;. The DEGs between the normal group and disease group were identified. The screening conditions for differentially expressed genes were a P value\u0026lt;0.05 and a | logFC|\u0026gt;0.585. A volcano map and heatmap of the differentially expressed genes were generated.\u003c/p\u003e\n\u003col start=\"3\"\u003e\n \u003cli\u003e\u003cstrong\u003eSelection and analysis of hub genes\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe hub genes were identified using Cytoscape\u0026rsquo;s cytoHubba plug-in. To analyze the hub genes, seven standard algorithms (MCC, MNC, degree, closeness, radiality, stress, and EPC) were used. The key hub genes shared across these seven approaches were\u0026nbsp;filtered using an UpSet diagram. Subsequently, a coexpression network of these hub genes was constructed\u0026nbsp;via\u0026nbsp;GeneMANIA (http://www.genemania.org/),\u003c/p\u003e\n\u003cp\u003ewhich is a widely used tool for revealing internal associations in gene sets.\u003c/p\u003e\n\u003col start=\"4\"\u003e\n \u003cli\u003e\u003cstrong\u003eValidation of hub gene expression in datasets and disease samples\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe mRNA expression of the identified hub genes was verified in the GSE42057, GSE31821, GSE11574 and GSE79768 datasets. The GSE31821 dataset contains 2 controls and 4 AF patients. GSE11574 consists of 31 control samples and 28 AF samples. Comparisons between two sets of data were performed with a t test. A p value \u0026lt;0.05 was considered significant. RT‒qPCR was performed to analyze the mRNA expression of the hub genes in AF and COPD samples and normal controls. This study was approved by the Ethics Committee of the Second Affiliated Hospital of Guangzhou Medical University. Total RNA was isolated from colon tissues with Simgen. qPCR and data analysis were performed using a LightCycler 96 instrument. The relative expression levels were estimated using GAPDH as an internal reference. The experiments were conducted three times with biological duplicates.\u003c/p\u003e\n\u003col start=\"5\"\u003e\n \u003cli\u003e\u003cstrong\u003eGO semantic similarity\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eBased on the GO semantic similarity, we rank proteins by the functional similarity between proteins and interacting proteins. GO semantic similarity has been verified by correlation between gene expression profiles\u003csup\u003e[13]\u003c/sup\u003e, which provides a basis for functional comparisons of gene products and has been widely used in bioinformatics, such as protein‒protein interaction analysis\u003csup\u003e[14]\u003c/sup\u003e, pathway analysis\u003csup\u003e[15]\u003c/sup\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003eand gene function prediction\u003csup\u003e[16]\u003c/sup\u003e. Here, we measured the functional similarity between proteins in terms of cellular components (CCs) and biological pathways (BPs) to explore the relationships between each protein and its interacting proteins. The semantic similarity between interacting histones in CCs and BPs was determined by the GoSemsim software package\u003csup\u003e[17]\u003c/sup\u003e, which is implemented in a more accurate way by considering the GO topology\u003csup\u003e[18]\u003c/sup\u003e.\u003c/p\u003e\n\u003col start=\"6\"\u003e\n \u003cli\u003e\u003cstrong\u003eImmune cell infiltration analysis\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe ssGSEA method distinguishes 29 human immune cell phenotypes, including T cells, B cells, and NK cells. In this study, the ssGSEA algorithm was used to quantify immune cells in the expression profile.\u003c/p\u003e\n\u003col start=\"7\"\u003e\n \u003cli\u003e\u003cstrong\u003eCMap drug prediction\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eThe CMap is a gene expression profiling database based on interventional gene expression data developed by the Broad Institute. It is primarily used to reveal associations between small molecules, gene expression and disease.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e8. Statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the statistical analyses were performed using R (version 4.2.2). All the statistical tests were two-sided, and p\u0026lt;0.05 indicated statistical significance.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eIdentification of differentially expressed genes in COPD and AF patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in the microarray results, DEGs associated with AF (GSE31821, GSE79768 and GSE115574) and COPD (GSE42057) were identified (Figure 1A, B). A total of 47 DEGs with the same expression trends were obtained, including 34 upregulated genes and 13 downregulated genes (Figure 1C and D).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of the functional characteristics of common differentially expressed genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGO and KEGG pathway enrichment analyses were used to investigate the biological functions and pathways associated with the 47 common DEGs. GO analysis revealed that these genes were mainly enriched in cell death, biotic-stimulus, vacuolar-lumen, and lipopolysaccharide receptor (Figure 2A and B). According to the KEGG pathway analysis, the three significantly enriched pathways were related to hypertrophic cardiomyopathy, cell adhesion molecules, coreceptor activity, chemokine binding and polysome binding (Figure 2C and D).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProtein\u0026ndash;protein interaction network construction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWith the use of Cytoscape, a PPI network of common DEGs with total scores greater than 0.4 was created (Figure 3A). KEGG pathway analysis revealed that these genes were mostly involved in cytokine\u0026ndash;cytokine receptor interactions and the NF-\u0026kappa;B signaling pathway (Figure 3B). GO analysis revealed that these genes are related to the immune system response and necroptotic progression (Figure 3C).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSelection and analysis of hub genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter calculation with the cytoHubba plug-in, Venn diagrams showing 5 common hub genes, CSF2RB, MS4A6A, RNASE6, TRIQK and LRRN3, were validated (Figure 4A). The coexpression network of these hub genes was explored using the GeneMANIA database (Figure 4B). GO analysis revealed that these genes are involved mainly in endonuclease activity, nuclease activity, and the IL-3 signaling pathway (Figure 4C). In addition, KEGG pathway analysis revealed that the hub genes were mostly involved in the immune receptor signaling pathway and cytokine receptor signaling pathway (Figure 4D).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation of hub gene expression\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe selected the COPD datasets and AF dataset to confirm the expression levels of these hub genes. Compared with those in the normal group, all the hub genes except LRRN3 were significantly upregulated in the COPD dataset (GSE42507) (Figure 5A-D). The expression of LRRN3 in COPD tissues was lower than that in normal colon tissues (Figure 5E). Similarly, the expression level of CSF2RB in the disease group was significantly greater than that in the normal group in the AF dataset (GSE115574) (Figure 5F). However, the expression of TRIQK and LRRN3 was lower in the AF group than in the normal group. Therefore, we performed RT‒qPCR to confirm the expression of the hub genes in these two disease tissues. We collected the serum of the patients and then measured the mRNA expression levels of the hub genes.\u003c/p\u003e\n\u003cp\u003eAs shown in Figure 6, we found that the mRNA expression of CSF2RB and RNASE6 in the normal group was lower than that in the disease group. However, the mRNA expression of these genes in the COPD-AF group was significantly greater than that in the COPD, AF and normal groups. The mRNA expression of TRIQK, LRRN4 and MS4A6A in the normal group was lower than that in the disease group. However, there was no significant difference in the mRNA expression of these three genes between the COPD-AF group and the normal group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExpression profile of hub genes and immune infiltration analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe analyzed the relationship between the hub genes and immune infiltration in the two disease datasets and further explored the underlying molecular mechanism of the hub genes affecting the onset of disease. The proportions of immune cells and their correlations with each other are shown in Figure 7A-D. In addition, immune infiltration showed that four immune cell types, including gamma delta T cells, resting NK cells, neutrophils and dendritic cells, were activated in the COPD samples and in the AF samples. We further explored the relationships between the hub genes and immune cells and found that several hub genes were strongly correlated with immune cells (Figure 7E-F). In addition, the prevalence of T cells in the two diseases significantly correlated with MS4A6A. These findings suggest that the hub genes are related to immune cell infiltration and play important roles in the COPD-AF immune microenvironment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCMap drug prediction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe divided the top 50 genes into two groups and performed drug prediction with the Connectivity Map database. The results showed that the expression profiles of drug perturbations, such as cefalexin, cephaeline, glipizide, lidoflazine and propranolol, were more significantly negatively correlated with the expression profiles of disease perturbations, suggesting that these drugs could alleviate or even reverse the disease state (Figure S1).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eSeveral studies have reported a relationship between COPD and AF. Patients with COPD who had a reduced forced expiratory volume in one second (FEV1) were more likely to have AF \u003csup\u003e[\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Moreover, patients with COPD have greater postoperative AF (23.3% vs. 11.0%, respectively, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) than patients without COPD \u003csup\u003e[\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. On the other hand, impaired pulmonary function, hypercapnia, and high pulmonary artery systolic pressure in COPD patients may indicate the occurrence of AF\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Regarding the pathophysiological mechanism, some reports have shown that hypoxia may be an unfavorable factor in the atrial conduction of COPD patients, which leads to the occurrence of AF \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. On the one hand, hypercapnia is also a risk factor for AF occurrence in COPD patients. Capnia may result in an increased possibility of atrial refractoriness and an obvious decline in atrial conduction. On the other hand, left ventricular diastolic dysfunction may serve as another possible pathophysiological mechanism for persistent and permanent AF initiation\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. COPD contributes to ventricular diastolic dysfunction, which is the basis for the incidence of AF in COPD\u003csup\u003e[\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e patients. However, the pathological mechanism underlying these two diseases is still unclear. Although there are many biomarkers for predicting the progression of COPD and AF, a predictive model for the co-onset of COPD and AF has not been reported. Thus, an exploration of the pathophysiological mechanisms implicated in COPD in AF is needed to identify better therapeutic options and reduce mortality in these patients. In our study, five hub genes were identified to reveal the underlying mechanisms associated with the co-occurrence of COPD and AF.\u003c/p\u003e \u003cp\u003eIn the present study, a series of bioinformatics analyses were performed on COPD and AF patients, and 47 DEGs common to both COPD and AF patients were obtained from the GEO database. The results of GO enrichment analysis showed that the DEGs were mainly enriched in the immune response. These results indicated that the DEGs between COPD patients and AF patients are related to inflammation and the immune response, which is consistent with previous studies \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. In addition, KEGG analysis revealed that the DEGs were mainly enriched in arrythmogenic right ventricular cardiomyopathy, hypertrophic cardiomyopathy, dilated cardiomyopathy and cytokine‒cytokine receptor interactions. We further classified these pathways according to the KEGG pathway database. We found that these genes are mainly related to immunity and inflammation. Many studies have shown that immune inflammation plays a critical role in AF and COPD\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSubsequently, according to the MCODE plug-in and cytoHubba plug-in of Cytoscape, we screened nine overlapping DEGs, namely, LRRN3, CSF2RB, MS4A6A, RNASE6 and TRIQK, as hub genes in the PPI network. These five genes were all upregulated in both AP patients and COPD patients, suggesting that these genes may play important roles in the mechanism of AF and COPD. LRRN3 was reported to be associated with cigarettes because it has three DNA methylation loci (cg09837977, cg05221370, and cg11556164) in its 5\u0026rsquo;UTR, which are related to smoking\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. LRRN3 may mediate the development of HF following AMI via the MAPK signaling pathway and its downstream effector. identified two genes, IL1R2 and LRRN3, as possible target genes for the development of HF following AMI\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. CSF2RB, a CSF2 receptor, can enhance the cardiac homing of intravenously delivered MSCs for repairing ischemic heart injury\u003csup\u003e[\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. CSF2RB is also known as a granulocyte\u0026ndash;macrophage colony\u0026ndash;stimulating factor that responds to distinct types of AMI\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. Furthermore, CSF2RB may also be an M2 macrophage-related biomarker in the progression of acute myocardial infarction\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. CSF2RB could be an effective predictor of the progression of patients with lung adenocarcinoma and a potential target for cancer treatment\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. MS4A6A is a promising biomarker for immunotherapy of lung adenocarcinoma and is associated with HRD in lung adenocarcinoma\u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e. RNASE6, as a prognostic target, may play a role in the development of atherosclerosis based on functional differences in TAMs\u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e. As shown above, the identified functions of these five hub genes highlighted the correlation between respiratory and cardiac disease.\u003c/p\u003e \u003cp\u003eWe utilized RT‒qPCR to measure the expression of the hub genes in the normal group, COPD group, AF group and co-occurrence group. The mRNA expression of CSF2RB, RNASE6, TRIQK, LRRN4 and MS4A6A in the normal group was significantly lower than that in the disease group, which indicated that these genes may be risk factors for these two diseases. Moreover, the mRNA expression of these genes in the COPD-AF group was greater than that in the COPD, AF and normal groups. However, the mRNA expression of TRIQK, LRRN4 and MS4A6A in the normal group was lower than that in the disease group. There was no significant difference between the COPD-AF group and the normal group. These results suggested that CSF2RB and RNASE6 may play a role in the mechanism underlying the on-occurrence of these two diseases.\u003c/p\u003e \u003cp\u003eIt is generally recognized that the differential expression of T-cell subsets plays a role in the pathogenesis of COPD, especially in the airways and lung parenchyma\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. Furthermore, CD4\u0026thinsp;+\u0026thinsp;T cells mainly produce activating cytokines, which may assist in the physical training and prognosis of COPD patients\u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e. Numerous studies have shown a relationship between inflammation and AF\u003csup\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e. A previous study showed that the expression of PD-1 and PD-L1/2 is substantially downregulated on CD4\u0026thinsp;+\u0026thinsp;T cells in patients with paroxysmal and persistent AF. Moreover, T-cell excitation may regulate cytokines via the PD-1 and PD-L1 pathways, which participate in AF pathogenesis\u003csup\u003e[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eConsistent with previous studies, our study analyzed the immune levels of immune cells, such as T cells, NK cells, macrophages, and neutrophils, in AF and COPD patients via the ImmuCellAI and CIBERSORT databases. These results confirmed that high immune cell levels are abnormal in COPD and AF patients. The immune infiltration data revealed that four immune cell types, namely, gamma delta T cells, resting NK cells, neutrophils and dendritic cells, were activated in the COPD samples and in the AF samples. In addition, the prevalence of T cells in the two diseases significantly correlated with MS4A6A. This suggests that the immune function of T cells may be the key point in the occurrence of COPD and AF.\u003c/p\u003e \u003cp\u003eOverall, we utilized these hub genes to construct a diagnostic prediction model that will be beneficial for the prevention and diagnosis of postoperative COPD-AF to provide a theoretical basis for the molecular mechanism of COPD and AF co-occurrence. Furthermore, our study also predicted potential therapeutic drugs for 5 hub genes, such as cefalexin, cephaeline, glipizide, lidoflazine and propranolol, which will support future studies of the treatment of this disease.\u003c/p\u003e \u003cp\u003eHowever, there are several limitations in our study. First, our research is based on bioinformatics analysis. Therefore, some prospective clinical studies should be performed to verify the prognostic characteristics of these patients. Second, the molecular mechanisms underlying how the five hub genes are involved in the pathogenesis and progression of COPD-AF are still unknown and require further biological and experimental verification. In addition, animal experiments are needed for potential therapeutic drugs. Finally, further investigation of the relationship between AF and COPD progression may still be needed.\u003c/p\u003e \u003cp\u003eIn conclusion, a total of five hub genes involved in the development and progression of COPD-AF were identified and integrated into a diagnostic model for two diseases. This study provides novel insights into the pathogenesis and mechanisms of the occurrence of COPD and AF.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn our study, we analyzed the GEO data of COPD and AF patients via the following methods: Gene Ontology (GO) functional enrichment, protein\u0026ndash;protein interaction network construction and module analysis. Bioinformatics analysis confirmed five hub genes as significant novel biomarkers of COPD and AF, which may be beneficial for the early diagnosis and treatment of COPD. The above analysis revealed that immunity plays an important role in the co-occurrence of COPD-AF and that T cells may contribute to the pathogenesis and molecular mechanism of COPD-AF. This provides an in-depth study of the underlying mechanism of COPD-AF.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of The Second Affiliated Hospital of Guangzhou Medical University (Approval No: 2021-23-02). Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all individual participants included in the study. The participants consented to the publication of the results derived from their data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are available in the Gene Expression Omnibus (GEO) repository, under accession numbers GSE31821, GSE79768, GSE115574, and GSE42057.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Natural Science Foundation of Guangdong Province (Grant No. 2022A1515110107) and the China Postdoctoral Science Foundation (Grant No. 2023M740831).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eD-XJ made major contributions to the conception and design of the study and the writing and revision of the manuscript; Y-C and G-ZH contributed to the data acquisition, analysis, and interpretation of the data; H-HX and Y-XH made important contributions to the revision of the manuscript. YJ put forward many important suggestions for the revision of this article, which greatly improved the text. All the authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdeloye D, Song P, Zhu Y, Campbell H, Sheikh A, Rudan I. Global, regional, and national prevalence of, and risk factors for, chronic obstructive pulmonary disease (COPD) in 2019: a systematic review and modeling analysis. Lancet Respir Med 2022, \u003cstrong\u003e10\u003c/strong\u003e(5)\u003cstrong\u003e:\u003c/strong\u003e 447-458.\u003c/li\u003e\n\u003cli\u003eSafiri S, Carson-Chahhoud K, Noori M, Nejadghaderi SA, Sullman M, Ahmadian HJ, Ansarin K, Mansournia MA, Collins GS, Kolahi AA, Kaufman JS. 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PD-1/PD-L1 expression on CD (4+) T cells and myeloid DCs correlates with the immune pathogenesis of atrial fibrillation. J CELL MOL MED 2015, \u003cstrong\u003e19\u003c/strong\u003e(6)\u003cstrong\u003e:\u003c/strong\u003e 1223-1233.\u003c/li\u003e\n\u003cli\u003eChang G, Chen Y, Liu Z, Wang Y, Ge W, Kang Y, Guo S. The PD-1 with PD-L1 Axis Is Pertinent with the Immune Modulation of Atrial Fibrillation by Regulating T-Cell Excitation and Promoting the Secretion of Inflammatory Factors. J IMMUNOL RES 2022, \u003cstrong\u003e2022:\u003c/strong\u003e 3647817.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"COPD, Atrial fibrillation, Hub genes, T cells, Immune cell infiltration","lastPublishedDoi":"10.21203/rs.3.rs-8439150/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8439150/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAlthough increasing evidence has shown that there is a correlation between the progression of chronic obstructive pulmonary disease (COPD) and atrial fibrillation (AF), the molecular mechanism underlying their co-occurrence remains unknown. The purpose of this study was to further explore the underlying mechanism and biomarkers for the co-occurrence of COPD and AF. Thus, we analyzed the GEO data of COPD and AF patients via the following methods: GO functional enrichment, protein–protein interaction network construction and module analysis. RT‒qPCR was performed to validate the expression of the hub genes in the COPD and AF patient samples. Bioinformatics analysis confirmed that five hub genes, CSF2RB, RNase6, MS4A6A, TRIQK, and LRRN3, are significant novel biomarkers of COPD and AF and may be beneficial for the early diagnosis and treatment of COPD. Furthermore, the hub genes are related to immune cell infiltration and play important roles in the COPD-AF immune microenvironment. T cells may contribute to the pathogenesis and molecular mechanism of COPD-AF. RT‒qPCR revealed that the expression of RNASE6 and CSF2RB was associated with the co-occurrence of the two diseases. This provides an in-depth study of the underlying mechanism of COPD-AF.\u003c/p\u003e","manuscriptTitle":"Identification and validation of the common pathogenesis and hub biomarkers in chronic obstructive pulmonary disease complicated with atrial fibrillation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-19 11:22:40","doi":"10.21203/rs.3.rs-8439150/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-04T12:12:48+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-28T07:30:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-23T10:02:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"338796085893607653191460264140447842999","date":"2026-04-14T11:16:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"53967920953419536370668947485363381323","date":"2026-04-14T11:10:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"168329988927782694241904955972029509260","date":"2026-04-13T06:33:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-08T06:54:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"151862321398235491350884897675887130410","date":"2026-04-08T06:43:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-14T01:53:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-14T01:49:24+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-07T14:25:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-03T12:19:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-01-03T12:12:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"48c5dcc2-e9ad-406f-ba80-73d0f9793832","owner":[],"postedDate":"January 19th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-04T12:12:48+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":61100965,"name":"Health sciences/Biomarkers"},{"id":61100966,"name":"Health sciences/Cardiology"},{"id":61100967,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":61100968,"name":"Health sciences/Diseases"},{"id":61100969,"name":"Biological sciences/Genetics"},{"id":61100970,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2026-05-04T12:24:50+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-19 11:22:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8439150","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8439150","identity":"rs-8439150","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}