LAASO algorithm-based bioinformatics analysis for exploring the pathogenesis and immune infiltration of atrial fibrillation and dilated cardiomyopathy

LAASO algorithm-based bioinformatics analysis for exploring the pathogenesis and immune infiltration of atrial fibrillation and dilated cardiomyopathy | 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 LAASO algorithm-based bioinformatics analysis for exploring the pathogenesis and immune infiltration of atrial fibrillation and dilated cardiomyopathy Hongdao Li, Yuyang Chen, Shumin Liang, Bifang Mai, Wenqing Tu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4494391/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: It is well known that atrial fibrillation (AF) is a serious complication of dilated cardiomyopathy (DCM) and they could affect each other. This study endeavors to uncover the shared diagnostic markers and immune pathways contributing to both disorders. Methods: We downloaded AF and DCM data from the Gene Expression Omnibus (GEO) database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were used to clarify shared signaling pathways of DEGs. Further, the least absolute shrinkage and selection operator (LASSO) regression was applied to pinpoint key hub genes and construct a diagnostic model. Results: A total of 20 common DEGs in AF and DCM were identified for subsequent analyses. The LASSO analysis results showed that RGCC, KCNN2 and TGFBR3 are 3 most important cross-talk genes between these 2 diseases. The immune infiltration results revealed that Th17/Treg cells may play an important role in the pathogeneses of AF and DCM. Conclusion: This pioneering study merges bioinformatics and machine learning to map the shared molecular landscape of AF and DCM, highlighting RGCC, KCNN2, and TGFBR3 as key diagnostic biomarkers. The imbalance of Th17/Treg may be vital in the progression of these 2 diseases. atrial fibrillation dilated cardiomyopathy bioinformatics differentially expressed genes hub immune-related genes immune infiltration Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Dilated cardiomyopathy is conventionally defined as ventricular remodelling and dysfunction with left ventricular or biventricular dilatation but without coronary artery disease or cardiac overload 1 . However, DCM accompanied by atrial fibrillation is extremely common in the population 2 . In fact, AF is a common arrhythmia in DCM and affects common prognostic indicators such as all-cause mortality. In addition, it can also increase the risk of stroke and hospitalization for heart failure 3 . Therefore, AF and DCM accompany and influence each other. From a pathophysiological perspective, AF and DCM share many common pathogeneses. For example, cardiac remodeling and fibrosis are common mechanism of both diseases 4 . Increased atrial pressure and dilatation in DCM patients cause cardiac structure remodeling and scar formation, intertwining the pathogenesis of these two diseases further 5 . Many changes in gene expression and immune mechanisms may be related to the common mechanism of their pathogeneses 6 . In addition, treatment of AF with DCM presents a therapeutic challenge due to the heightened risks associated with traditional drug and nondrug therapies 2, 7 . Therefore, it is quite necessary to explore the co-pathogenesis, molecular diagnostic strategy and treatment of AF and DCM. In light of these complexities, our study employed a comprehensive analysis using six Gene Expression Omnibus (GEO) databases to delve deeper into the molecular mechanisms underlying the co-pathogenesis of AF and DCM. Through rigorous bioinformatics methods and machine learning tools, we aimed to identify key differentially expressed genes (DEGs), explore their shared signaling pathways, and assess their potential as diagnostic markers. This integrative approach hopes to pave the way for more precise diagnosis, mitigating the combined impact of AF and DCM on patient health. Methods 2.1. Data download We sourced 6 high-quality raw expression profile datasets from the GEO database, 2 for AF (GSE2240 and GSE41177) and 4 for DCM (GSE120895, GSE29819, GSE3585 and GSE42955). Datasets are available at https://www.ncbi.nlm.nih.gov/geo/ for more information 8 . 4 of these databases (GSE2240, GSE120895, GSE29819, GSE3585) were used as training sets to screen key genes and build models, while the remaining two datasets (GSE41177 and GSE42955) were served as validation sets for external validation. The details of the above datasets are presented in Table S1 . A total of 110 patients (80 for DCM and 30 for control) in DCM were included in the study, while 68 patients (42 for AF and 26 for control) were included in AF. We mainly used R software to process the chip data.“Gcrma”package was used to background correction and standardize the microarray data. Due to the different date, personnel, place and equipment of the experiment, there must be batch effects in the chip data. We can't get rid of batch effects by standardizing the data. Therefore, we remove the batch effect by using “SVA” packages 9 . 2.2. Differentially expressed genes (DEGs) analysis After background correction, normalization and removal of batch effect, differentially expressed genes analysis was performed on the chip data. DEGs in AF and DCM datasets were identifed using the “Limma” package in R software 10 . The criteria for DEGs screening are adjusted P value < 0.05 and |log FC| ≥ 0.5. In addition, because there are 3 DCM datasets and they are not from the same platform, the “RobustRankAggreg” package was used to conduct RRA analysis to find the common DEGs of the 3 DCM datasets. 2.3.WGCNA network construction and module identification WGCNA is a bioinformatics analysis method used to describe gene association patterns among different samples. It can divide DEGs into different modules and look for the correlations between them and phenotypes. There are a total of 4 steps in this analysis and the key R package is “WGCNA” 11 . In step 1,we cleaned the data and removed outlier samples. In step 2, we screened out the most appropriate soft thresholding power(β) to keep a balance between scale independence and mean connectivity. In step 3, the gene expression similarity matrix was calculated and put into an adjacency matrix according to the soft thresholding power (β) set in the previous step by using the “adjacency” function. Then, a topological overlap matrix (TOM) was calculated to minimize the effects of noise and spurious associations according to the adjacency matrix we obtained in the previous step. In step 4, we used the R package “flashClust” to build network modules in different colors. Due to the large number of modules, we need to further merge these modules constructed by dynamic shear tree clustering. Finally,associations between different modules of genes and diseases will be presented. 2.4. Identification of shared genes and pathway enrichment Venn diagram was used to present the results of the WGCNA and DEGs consults. The result of RRA is shown in supplement S1. The overlapping of these 2 results were used as further functional enrichment analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted by using the “enrichplot” and “ggplot2”packages in R. Statistical significance was set at P < 0.05. 2.5. Machine learning The least absolute shrinkage and selection operator (LASSO), a new and advanced machine learning algorithm, was used to refine the list of potential diagnostic genes. The “glmnet” package in R was used to conduct this algorithm to screen the best predictors of AF and DCM from their 20 DEGs 12 . This package was aslo used to plot clinical decision curve and clinical impact curve. Moreover, the internal verification was conducted by plotting consistency curve and calculating coefficient of consistency by using the “rms” package. Finally, nomogram was used to visualize the lasso model. 2.6. Statistical analysis Statistical analyses were performed using IBM SPSS Statistics 26 and R 4.2.2 software. Results were displayed as mean ± SD. Diferences between the two groups were compared by unpaired Student′s t-test. P < 0.05 was regarded as statistical signifcance. 2.7. ssGSEA ssGSEA was performed by the “GSVA” R package to analyse the infiltration of 29 immune cells in diseased and normal samples. Spearman’s rank correlation tests was used to analyse the relationship between hub genes and immune cells. Results 3.1. Data processing Te strategy of bioinformatics analysis is performed as shown in Figure. S1. First, the raw materials of 6 GEO databases (2 for AF and 4 for DCM) were downloaded from GEO official website and the targets information was organized. Second, the gcrma method was used to normalize the data of 6 GEO databases. Third, we used the “sva” package to remove batch effects of the GEO datasets based on the production date of the chips. PCA plots were used to represent the clustering of data according to their groups. 3.2. Identification of DEGs For GSE2240 (AF), 352 DEGs were identified based on the default standard we set (adjusted P value < 0.05 and |log FC| ≥ 0.5), including 119 upregulated DEGs and 233 downregulated DEGs ( Fig. 1). For the 3 datasets of DCM as training set, the numbers of DEGs are 389, 1126 and 244 respectively. The RRA method was used to identify the common DEGs of these 3 DCM datasets, including top 150 common upregulated DEGs and top 150 common downregulated DEGs (Fig. 2). 3.3. The construction of weighted gene co-expression network and the identification of key modules in AF The weighted gene co-expression network analysis (WGCNA) was conducted to further explore the most relevant gene modules with AF. First, sample clustering tree was used to remove 4 outliers and 26 remaining samples were used in WGCNA analysis (Fig. 3a). In order to ensure as large scale independence and average connectivity as possible, we choose 10 as the most appropriate soft-thresholding power (Fig. 3b). We initially divided all of the genes into 28 modules and merged modules with similarities greater than 0.75. Finally, 12 gene modules were generated and the correlation degree between each module and AF is shown in Fig. 4d. The result showed that the skyblue2 module has the strongest positive correlation with AF (r = 0.81, p<0.001), while the darkorange module has the strongest negative correlation with AF(r=-0.63, p<0.001). The intersection of the DEGs and AF hub modules was shown by a Venn diagram, and 60 intersection upregulated genes and 134 intersection downregulated genes were obtained(Fig. 4g). 3.4. Identification of shared genes and pathway enrichment A total of 194 AF DEGs were selected by WGCNA analysis, while 300 DCM DEGs were identified by RRA method. The 20 intersection of AF and DCM DEGs was shown by a Venn diagram (Fig. 4a). They are ACE2, ASPN, COMP, FRZB, LOXL1, LTBP2, NAV2, NES, PLCE1, RGCC, TNNT1, AMD1, CD14, GOS2, KCNN2, MID1IP1, PPP1R1A, RARRES1, SELENBP1, TGFBR3. GO and KEGG enrichment analyses were conducted on the above 20 genes to explore their shared pathways. The GO analysis indicated that these genes may have relationship with muscle contraction, collagen-containing extracellular matrix, collagen binding, SMAD binding and glycosaminoglycan binding(Fig. 4b-d). The KEGG analysis showed that these common genes were strongly associated with phagosome and renin-angiotensin system(Fig. 4e). 3.5. Potential shared diagnostic genes selection by least absolute shrinkage and selection operator The 20 genes we screened out in the previous step were put into LASSO regression algorithm to further identify the most meaningful shared diagnostic genes of AF and DCM datasets. A λ corresponding to the lowest binomial deviance is the most appropriate λ value (Fig. 5a-b). By using this algorithm, 8 AF diagnostic genes and 9 DCM diagnostic genes were selected. Finally, the Venn diagram showed 3 common shared genes (RGCC, KCNN2, and TGFBR3) have the greatest diagnostic value. 3.6. Immune cell infiltration analysis The infiltration of 28 types of immune cells in AF and DCM was analysed in the study. We found that Th2, Th17 and B cells are obviously upregulated in both groups, while Tregs are obviously downregulated in both groups (Fig. 6a-b). In addition, the biomarker RGCC has strong relationship with these immune cell changes (Fig. 6c-d). 3.7. Model performance and internal validation The results showed that the AUC values of the diagnostic LASSO model based on 3 common shared genes are 1.000 and 0.907 for AF and DCM respectively (Fig. 7a-b). Clinical decision curves and clinical impact curves were drawn to evaluate the actual clinical application of the model. The results showed that the lasso model has a definite large net benefit for patients under all threshold probabilities and good risk stratification accuracy (Fig. 7c-f). In addition, we used the consistency curve to verify the model internally (Fig. 7g-h). The consistency curve shows that the model is in great agreement with the actual situation. In addition, the bias corrected C-index of the lasso model are 0.995 and 0.878. 3.8. Model visualization and external validation Nomograms were used to visualize the diagnostic model constructed by 3 common shared genes (RGCC, KCNN2, TGFBR3) in AF and DCM. 2 GEO datasets (GSE41177 and GSE42955), one for AF and the other for DCM, were used as validation set to test the extrapolation of the model. Compared to the training set, the AUC for the validation set remain high, which are 0.891 and 0.976 respectively. This result showed that the findings of this study may also be applied to other populations (Fig. 8a-d). Discussion For the past few years, the applications of microarray and sequencing methods have been very popular in exploring the hidden mechanisms behind various diseases, which marks the rapid development of bioinformatics technology 13, 14 . In the meantime, machine learning tools are increasingly performed to variables screening and diagnosis model construction based on big data, which could help to find novel genes and potential diagnostic/prognostic biomarkers 15 . In this article, we combine traditional bioinformatics with machine learning to explore the common mechanism behind AF and DCM. The results indicated that 20 genes ( ACE2, ASPN, COMP, FRZB, LOXL1, LTBP2, NAV2, NES, PLCE1, RGCC, TNNT1, AMD1, CD14, GOS2, KCNN2, MID1IP1, PPP1R1A ,RARRES1, SELENBP1, TGFBR3) were the most vital cross-talk genes between AF and DCM, suggesting a strong association with muscle contraction, collagen interactions, and the renin-angiotensin system. Among them, RGCC, KCNN2, and TGFBR3 emerged as the most significant diagnostic markers. The diagnostic model constructed by these 3 genes is highly accurate and has also been validated in external datasets. In addition, the immune infiltration results showed that Th2 cells and the imbalance of Th17/Treg cells may have strong relationship with both of the diseases. The result of GO and KEGG analysis indicates that the shared genes are associated with muscle contraction, collagen-containing extracellular matrix and renin-angiotensin system. Myocardial contraction is the most basic manifestation of cardiac circulatory function and is regulated by Ca 2+ , which is a fundamental second messenger in all cell types. In the heart, abnormal contraction caused by abnormal regulation of Ca2 + have strong relationship with the occurrence of AF and DCM 16 . Moreover, extracellular matrix production and fibrosis are the common mechanism of these two diseases. Some studies indicate that the degree of atrial fibrosis is highly correlated with the progression and prognosis of atrial fibrillation 17, 18 . Cardiac fibrosis plays an important role in dilated cardiomyopathy, increasing its morbidity and mortality 19 . In addition, renin-angiotensin system alter the extracellular matrix by impacting the synthesis of type 1 and type 3 collagen, fibronectin, and α-smooth muscle actin. Therefore, the pathways enriched by these 20 genes are highly associated with both diseases. In this article, the possible common immune mechanism of AF and DCM was elucidated based on the results of immune infiltration analysis. We observed a notable upregulation of Th2 and Th17 cells in both conditions, coupled with a significant downregulation of Treg cells. Th2 cells is a subset of CD4 + T cells which could lead to inflammatory responses and fibrosis by secreting Th2 cytokines 20 . Some studies indicate that Th2 immune response may be related to ventricular remodeling which promotes the progression of DCM 21 . Th17 cells and Treg cells share a common precursor cell and have the same signal for initial differentiation 22 . Studies have shown that the imbalance of Th17/Treg cells induces many inflammatory diseases 23 . Th17 cells increase tissue inflammation and promote autoimmune activation while Treg cells antagonize this biological process, which could play a protective role 21 . What's more, the elevated plasma levels of Th17-related cytokines may increase the risk of atrial fibrillation 24 . These results show that Th2 cells and the imbalance of Th17/Treg cells may play an important role in the progression of AF and DCM. RGCC, also known as RGC-32, is a kind of cell cycle regulator which is positively regulated by TGF-β 25 . The results of our immunoinfiltration analysis showed a strong association between RGCC and the above-mentioned immune cells, especially for Th17 and Treg cells, and Treg/Th17 plays an important role in DCM 26 . Li et al. have shown that RGCC could accelerate the progression of DCM by inducing the imbalance of Treg/Th17 ratio, which is consistent with our findings 27 . However, RGCC has been less reported in AF and little is known about how these subpopulations of immune cells influence the progression of AF 28 . In our study,we suggest that the biological process of RGCC leads to the imbalance of Treg/Th17 ratio may be a common mechanism of AF and DCM. TGFBR3, also known as betaglycan, is the most highly expressed TGF-βreceptors in the cell. When TGF-βbinds to TGFBR3 it will not bind to TGFBR1 and TGFBR2. While TGFBR1 and TGFBR2 work through canonical pathway, TGFBR3 works via a pathway that is noncanonical 29 . TGF-βcould activate smad2/3 expression by binding to TGFBR1/2, which could cause fibrosis and extracellular-matrix remodeling in AF and DCM 18 . TGFBR3 is acted as an antagonist of TGF-βwhich could inhibit this biological process to prevent the occurrence of these 2 diseases 30 . KCNN2 encodes a kind of small-conductance Ca2+-activated K + channel named SK2. SK channels, particularly SK2 channels, are expressed in different kinds of cardiomyocytes 31 . In our study, this gene is significantly downregulated in both diseases. Abnormality of intracellular Ca2 + handling has a strong relationship with the occurrence and progression of AF and KCNN2 could influence intracellular Ca2 + handling and membrane electrical events. In mouse model, the knockout of KCNN2 could lead to atrial action potential prolongation and atrial arrhythmias 32, 33 . Moreover, mitochondrial SK channels may decrease the generation of ROS and Ca2 + overload, which could lead to the damage of myocardial cells 31 . Our study has several strengths. In this study, a LASSO model was constructed for the first time to explore the common mechanism of AF and DCM. The results showed that the diagnostic model of these 3 hub genes based on LASSO has high accuracy and reliability, of which the auc is higher than 0.9. In the meantime, both internal and external verification prove that the model has high consistency and extrapolation. Moreover, the genes we screened have also been shown in other literature to be highly associated with AF and DCM. The results of immunoinfiltration analysis were also highly consistent with the conclusions of the model. However, the sample size of the article was relatively small. For this point, we have performed external validation to prove the reliability of the results. Conclusion This study is the first to combine bioinformatics tools and machine learning to explore the commonpathogeneses between AF and DCM. RGCC, KCNN2 and TGFBR3 are the most important cross-talk genes between AF and DCM and have high diagnostic value. The imbalance of Th17/Treg and Th2 cells may be vital in the progression of these 2 diseases. Declarations Author contribution Hongdao li and Shuanglun Xie wrote the main manuscript text and Yuyang Chen prepared figures 1-3. Shumin Liang, Bifang Mai, Wenqing Tu1 and Yuhan Lin prepared figures 4-9.Qi Chen checked the details.All authors reviewed the manuscript. Data availability statement The datasets generated and/or analysed during the current study are not publicly available due to its big amount but are available from the corresponding author on reasonable request. Conflict of interest On behalf of all authors, the corresponding author states that there is no conflict of interest. Funding Statement This work was supported by the National Natural Science Foundation of China [grant no. 82070412]. References Heymans, S.; Lakdawala, N. K.; Tschöpe, C.; Klingel, K. Dilated cardiomyopathy: causes, mechanisms, and current and future treatment approaches. Lancet 2023 , 402 (10406), 998-1011. DOI: 10.1016/s0140-6736(23)01241-2 From NLM. Saxon, L. A. Atrial fibrillation and dilated cardiomyopathy: therapeutic strategies when sinus rhythm cannot be maintained. Pacing Clin Electrophysiol 1997 , 20 (3 Pt 1), 720-725. DOI: 10.1111/j.1540-8159.1997.tb03891.x From NLM. Verma, A.; Kalman, J. M.; Callans, D. J. Treatment of Patients With Atrial Fibrillation and Heart Failure With Reduced Ejection Fraction. Circulation 2017 , 135 (16), 1547-1563. DOI: 10.1161/circulationaha.116.026054 From NLM. Carlisle, M. A.; Fudim, M.; DeVore, A. D.; Piccini, J. P. Heart Failure and Atrial Fibrillation, Like Fire and Fury. JACC Heart Fail 2019 , 7 (6), 447-456. DOI: 10.1016/j.jchf.2019.03.005 From NLM. Gan, T.; Hu, J.; Aledan, A. K. O.; Liu, W.; Li, C.; Lu, S.; Wang, Y.; Xu, Q.; Wang, Y.; Wang, Z. Exploring the pathogenesis and immune infiltration in dilated cardiomyopathy complicated with atrial fibrillation by bioinformatics analysis. Front Immunol 2023 , 14 , 1049351. DOI: 10.3389/fimmu.2023.1049351 From NLM Medline. Lardizabal, J. A.; Deedwania, P. C. Atrial fibrillation in heart failure. Med Clin North Am 2012 , 96 (5), 987-1000. DOI: 10.1016/j.mcna.2012.07.007 From NLM. Yeung, C.; Enriquez, A.; Suarez-Fuster, L.; Baranchuk, A. Atrial fibrillation in patients with inherited cardiomyopathies. Europace 2019 , 21 (1), 22-32. DOI: 10.1093/europace/euy064 From NLM. Barrett, T.; Wilhite, S. E.; Ledoux, P.; Evangelista, C.; Kim, I. F.; Tomashevsky, M.; Marshall, K. A.; Phillippy, K. H.; Sherman, P. M.; Holko, M.; et al. NCBI GEO: archive for functional genomics data sets--update. Nucleic Acids Res 2013 , 41 (Database issue), D991-995. DOI: 10.1093/nar/gks1193 From NLM Medline. Leek, J. T.; Johnson, W. E.; Parker, H. S.; Jaffe, A. E.; Storey, J. D. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 2012 , 28 (6), 882-883. DOI: 10.1093/bioinformatics/bts034 From NLM Medline. Ritchie, M. E.; Phipson, B.; Wu, D.; Hu, Y.; Law, C. W.; Shi, W.; Smyth, G. K. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 2015 , 43 (7), e47. DOI: 10.1093/nar/gkv007 From NLM Medline. Langfelder, P.; Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 2008 , 9 , 559. DOI: 10.1186/1471-2105-9-559 From NLM Medline. Jerome Friedman, T. H. Regularization Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software 2010 . Petrik, J. Diagnostic applications of microarrays. Transfus Med 2006 , 16 (4), 233-247. DOI: 10.1111/j.1365-3148.2006.00673.x From NLM. Su, Z.; Ning, B.; Fang, H.; Hong, H.; Perkins, R.; Tong, W.; Shi, L. Next-generation sequencing and its applications in molecular diagnostics. Expert Rev Mol Diagn 2011 , 11 (3), 333-343. DOI: 10.1586/erm.11.3 From NLM. Sajda, P. Machine learning for detection and diagnosis of disease. Annu Rev Biomed Eng 2006 , 8 , 537-565. DOI: 10.1146/annurev.bioeng.8.061505.095802 From NLM. Dridi, H.; Kushnir, A.; Zalk, R.; Yuan, Q.; Melville, Z.; Marks, A. R. Intracellular calcium leak in heart failure and atrial fibrillation: a unifying mechanism and therapeutic target. Nat Rev Cardiol 2020 , 17 (11), 732-747. DOI: 10.1038/s41569-020-0394-8 From NLM. Malcolme-Lawes, L. C.; Juli, C.; Karim, R.; Bai, W.; Quest, R.; Lim, P. B.; Jamil-Copley, S.; Kojodjojo, P.; Ariff, B.; Davies, D. W.; et al. Automated analysis of atrial late gadolinium enhancement imaging that correlates with endocardial voltage and clinical outcomes: a 2-center study. Heart Rhythm 2013 , 10 (8), 1184-1191. DOI: 10.1016/j.hrthm.2013.04.030 From NLM. Nattel, S.; Heijman, J.; Zhou, L.; Dobrev, D. Molecular Basis of Atrial Fibrillation Pathophysiology and Therapy: A Translational Perspective. Circ Res 2020 , 127 (1), 51-72. DOI: 10.1161/circresaha.120.316363 From NLM. Cojan-Minzat, B. O.; Zlibut, A.; Agoston-Coldea, L. Non-ischemic dilated cardiomyopathy and cardiac fibrosis. Heart Fail Rev 2021 , 26 (5), 1081-1101. DOI: 10.1007/s10741-020-09940-0 From NLM. Kokubo, K.; Onodera, A.; Kiuchi, M.; Tsuji, K.; Hirahara, K.; Nakayama, T. Conventional and pathogenic Th2 cells in inflammation, tissue repair, and fibrosis. Front Immunol 2022 , 13 , 945063. DOI: 10.3389/fimmu.2022.945063 From NLM. Zheng, S. Y.; Dong, J. Z. Role of Toll-Like Receptors and Th Responses in Viral Myocarditis. Front Immunol 2022 , 13 , 843891. DOI: 10.3389/fimmu.2022.843891 From NLM. Lee, G. R. The Balance of Th17 versus Treg Cells in Autoimmunity. Int J Mol Sci 2018 , 19 (3). DOI: 10.3390/ijms19030730 From NLM. Thomas, R.; Qiao, S.; Yang, X. Th17/Treg Imbalance: Implications in Lung Inflammatory Diseases. Int J Mol Sci 2023 , 24 (5). DOI: 10.3390/ijms24054865 From NLM. Wu, N.; Xu, B.; Liu, Y.; Chen, X.; Tang, H.; Wu, L.; Xiang, Y.; Zhang, M.; Shu, M.; Song, Z.; et al. Elevated plasma levels of Th17-related cytokines are associated with increased risk of atrial fibrillation. Sci Rep 2016 , 6 , 26543. DOI: 10.1038/srep26543 From NLM. Vlaicu, S. I.; Tatomir, A.; Anselmo, F.; Boodhoo, D.; Chira, R.; Rus, V.; Rus, H. RGC-32 and diseases: the first 20 years. Immunol Res 2019 , 67 (2-3), 267-279. DOI: 10.1007/s12026-019-09080-0 From NLM. Li, J.; Wang, L.; Wang, S.; Zhu, H.; Ye, P.; Xie, A.; Shen, B.; Liu, C.; Guo, C.; Fu, Q.; et al. The Treg/Th17 imbalance in patients with idiopathic dilated cardiomyopathy. Scand J Immunol 2010 , 71 (4), 298-303. DOI: 10.1111/j.1365-3083.2010.02374.x From NLM. Li, B.; Zhou, W.; Tang, X.; Wang, W.; Pan, J.; Tan, M. Response Gene to Complement-32 Promotes the Imbalance of Treg/Th17 in Patients with Dilated Cardiomyopathy. Cell Physiol Biochem 2017 , 43 (4), 1515-1525. DOI: 10.1159/000481975 From NLM. Hu, Y. F.; Chen, Y. J.; Lin, Y. J.; Chen, S. A. Inflammation and the pathogenesis of atrial fibrillation. Nat Rev Cardiol 2015 , 12 (4), 230-243. DOI: 10.1038/nrcardio.2015.2 From NLM. Vander Ark, A.; Cao, J.; Li, X. TGF-β receptors: In and beyond TGF-β signaling. Cell Signal 2018 , 52 , 112-120. DOI: 10.1016/j.cellsig.2018.09.002 From NLM. Yang, Z.; Xiao, Z.; Guo, H.; Fang, X.; Liang, J.; Zhu, J.; Yang, J.; Li, H.; Pan, R.; Yuan, S.; et al. Novel role of the clustered miR-23b-3p and miR-27b-3p in enhanced expression of fibrosis-associated genes by targeting TGFBR3 in atrial fibroblasts. J Cell Mol Med 2019 , 23 (5), 3246-3256. DOI: 10.1111/jcmm.14211 From NLM. Liu, T.; Li, T.; Xu, D.; Wang, Y.; Zhou, Y.; Wan, J.; Huang, C. L.; Tan, X. Small-conductance calcium-activated potassium channels in the heart: expression, regulation and pathological implications. Philos Trans R Soc Lond B Biol Sci 2023 , 378 (1879), 20220171. DOI: 10.1098/rstb.2022.0171 From NLM. Li, N.; Timofeyev, V.; Tuteja, D.; Xu, D.; Lu, L.; Zhang, Q.; Zhang, Z.; Singapuri, A.; Albert, T. R.; Rajagopal, A. V.; et al. Ablation of a Ca2+-activated K+ channel (SK2 channel) results in action potential prolongation in atrial myocytes and atrial fibrillation. J Physiol 2009 , 587 (Pt 5), 1087-1100. DOI: 10.1113/jphysiol.2008.167718 From NLM. Yu, T.; Deng, C.; Wu, R.; Guo, H.; Zheng, S.; Yu, X.; Shan, Z.; Kuang, S.; Lin, Q. Decreased expression of small-conductance Ca2+-activated K+ channels SK1 and SK2 in human chronic atrial fibrillation. Life Sci 2012 , 90 (5-6), 219-227. DOI: 10.1016/j.lfs.2011.11.008 From NLM. Additional Declarations No competing interests reported. Supplementary Files supplementmaterialJournal.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4494391","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":312685780,"identity":"863fdbf9-cc24-4055-8da5-f201f8d9be22","order_by":0,"name":"Hongdao Li","email":"","orcid":"","institution":"Sun Yat-sen Memorial Hospital of Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Hongdao","middleName":"","lastName":"Li","suffix":""},{"id":312685782,"identity":"7eecbe0d-83ee-4a7c-9561-63fb278f90a8","order_by":1,"name":"Yuyang Chen","email":"","orcid":"","institution":"Sun Yat-sen Memorial Hospital of Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Yuyang","middleName":"","lastName":"Chen","suffix":""},{"id":312685783,"identity":"a8ad1499-b2eb-4a82-89d7-92ebef54f0de","order_by":2,"name":"Shumin Liang","email":"","orcid":"","institution":"Sun Yat-sen Memorial Hospital of Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Shumin","middleName":"","lastName":"Liang","suffix":""},{"id":312685786,"identity":"1983bdfb-d627-4ec0-a1cd-c50f55fdd7d1","order_by":3,"name":"Bifang Mai","email":"","orcid":"","institution":"Sun Yat-sen Memorial Hospital of Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Bifang","middleName":"","lastName":"Mai","suffix":""},{"id":312685787,"identity":"fa02c76c-2d8b-440a-96d9-df2dd9e21126","order_by":4,"name":"Wenqing Tu","email":"","orcid":"","institution":"Sun Yat-sen Memorial Hospital of Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Wenqing","middleName":"","lastName":"Tu","suffix":""},{"id":312685789,"identity":"8f2d5d05-562f-40e3-a497-8f70e22b5a92","order_by":5,"name":"Yuhan Lin","email":"","orcid":"","institution":"Sun Yat-sen Memorial Hospital of Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Yuhan","middleName":"","lastName":"Lin","suffix":""},{"id":312685790,"identity":"3b90f7a3-c4f7-4248-9d70-2fb450540fc0","order_by":6,"name":"Qi Chen","email":"","orcid":"","institution":"The Second Affiliated Hospital of Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Chen","suffix":""},{"id":312685791,"identity":"3f5f6b21-9db3-4588-abf8-344d56bc2ea1","order_by":7,"name":"Shuanglun Xie","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYDACZjACAnYg/sAgQYoWIMk4gygtDEhamHmIUW5wnPnw58I2uzx5Zx6zxzZ/LPL4G5gfPrqBR4tkM1ua9My25GLDwzzmxrltEsUSB9iMjXPwaOFn5jFj5m1jTtzYzGMmndsgkdhwgIdNGp8WNmb+z5952+ohWiz+SCTOJ6QFaAuDNG/b4cT5QOukGdgkEjcQ0gL0i5k0z7njiRuY2coke9skEjceJuAXg/OHH3/mKatOnN/evE3ix5+6xHnHmx8+xqcFofcAjMVMjHIQkG8gVuUoGAWjYBSMOAAAytVAtdVBpzYAAAAASUVORK5CYII=","orcid":"","institution":"Sun Yat-sen Memorial Hospital of Sun Yat-sen University","correspondingAuthor":true,"prefix":"","firstName":"Shuanglun","middleName":"","lastName":"Xie","suffix":""}],"badges":[],"createdAt":"2024-05-29 05:25:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4494391/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4494391/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58233705,"identity":"4a3b46e4-f44c-4d52-a2bd-70f0461e6d06","added_by":"auto","created_at":"2024-06-12 20:22:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":873390,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of differentially expressed genes of AF. (A) Data standardization result of GSE2240. (B)PCA figure of GSE2240. (C) A volcano plot of the top 50 DEGs in GSE2240. (D) A heatmap of the top 50 DEGs in GSE2240.\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-4494391/v1/ecff82d520ae77b879049fa9.png"},{"id":58233378,"identity":"333ff0ad-5c56-4d51-b73e-786abc297534","added_by":"auto","created_at":"2024-06-12 20:06:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":566509,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of differentially expressed genes of DCM. (A) A volcano plot of the top 50 DEGs in GSE120895. (B) A volcano plot of the top 50 DEGs in GSE29819. (C) A volcano plot of the top 50 DEGs in GSE3585. (D) A heatmap of the top 50 DEGs in GSE120895. (E) A heatmap of the top 50 DEGs in GSE29819 (F) A heatmap of the top 50 DEGs in GSE3585.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-4494391/v1/a4123cc8f3b35d56b5196a81.png"},{"id":58233381,"identity":"a92e8bbc-83d9-4c07-b38c-10b67c51d15d","added_by":"auto","created_at":"2024-06-12 20:06:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":658917,"visible":true,"origin":"","legend":"\u003cp\u003eScreening of key module genes in the AF dataset by WGCNA analysis. (A) Sample clustering tree\u003c/p\u003e\n\u003cp\u003eof AF dataset. (B) The scale-free topology model was used to find thatβ=10 was the best soft\u003c/p\u003e\n\u003cp\u003ethreshold based on the average connectivity and scale independence. (C) Gene dendrogram and\u003c/p\u003e\n\u003cp\u003emodule colors. (D) Heatmap of the module-trait relationships in AF dataset. (E) module membership in skyblue2 module. (F) Module membership in darkorange module. (G)Venn diagram shows that 194 genes overlap in the DEGs and key modules of AF dataset.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-4494391/v1/f7e74f29d9c8353443a1ac99.png"},{"id":58233611,"identity":"c05941d3-dcc1-4eec-b81a-b52fc3991b56","added_by":"auto","created_at":"2024-06-12 20:14:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":306272,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment analyses of the shared genes. (A)Venn diagram shows that 56 genes overlap in the IgAN and periodontitis modules. (B) Biological process. (C) Cellular component.\u003c/p\u003e\n\u003cp\u003e(D) Molecular function. (E) KEGG pathway enrichment analysis of the shared genes.\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-4494391/v1/c8f532c4669eb4636cb99c42.png"},{"id":58233613,"identity":"f381b86c-97e2-4186-aaba-4b6f49148f23","added_by":"auto","created_at":"2024-06-12 20:14:15","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":251828,"visible":true,"origin":"","legend":"\u003cp\u003eIdentifcation of potential diagnostic biomarkers for AF and DCM by the machine learning methods. (A) Tenfold cross-validation to select the optimal tuning parameter log (lambda) in AF database. (B) Tenfold cross-validation to select the optimal tuning parameter log (lambda) in the DCM database. (C) LASSO coefficient profiles of diagnostic genes in the AF database. (D) LASSO coefficient profiles of diagnostic genes in the DCM database. (E) The ROC curve for\u003c/p\u003e\n\u003cp\u003ethe diagnostic model in AF database. (F) The ROC curve for the diagnostic model in DCM database. (G) Venn diagram showing the optimal diagnostic biomarkers.\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-4494391/v1/4d27ec2fcc33277f7ca37390.png"},{"id":58233387,"identity":"5f5748e9-3977-440f-847c-1c444cf87fea","added_by":"auto","created_at":"2024-06-12 20:06:15","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":562103,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of immune infiltration. (A) The box plot showing the distribution of immune cells of AF dataset. (B) The box plot showing the distribution of immune cells of DCM dataset. (C)The relationship between diagnostic genes and immune cell infiltration in AF dataset. (D)The relationship between diagnostic genes and immune cell infiltration in DCM dataset.\u003c/p\u003e","description":"","filename":"Fig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-4494391/v1/71e629568aee4f2098e4ac5c.png"},{"id":58233614,"identity":"d5b953ba-0b59-4983-82f5-3ff2e2fa1c49","added_by":"auto","created_at":"2024-06-12 20:14:15","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":666534,"visible":true,"origin":"","legend":"\u003cp\u003eModel performance and internal validation. (A) The ROC curve for the hub genes diagnostic model in AF database. (B) The ROC curve for the hub genes diagnostic model in DCM database.\u003c/p\u003e\n\u003cp\u003e(C-D) Clinical decision curves. (E-F) Clinical impact curves. (G-H) Consistency curves.\u003c/p\u003e","description":"","filename":"Fig.7.png","url":"https://assets-eu.researchsquare.com/files/rs-4494391/v1/7503629185775c84861d6bbd.png"},{"id":58233386,"identity":"bff58144-834e-41ae-9f5a-c2d63891ca82","added_by":"auto","created_at":"2024-06-12 20:06:15","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":763258,"visible":true,"origin":"","legend":"\u003cp\u003eModel visualization and external validation. (A-B) Nomograms for the model visualization of AF and DCM. (C) The ROC curve for external validation in GSE 41177. (D) The ROC curve for external validation in GSE 42955.\u003c/p\u003e","description":"","filename":"Fig.8.png","url":"https://assets-eu.researchsquare.com/files/rs-4494391/v1/148a6aba985f4e6d01051f2a.png"},{"id":70224405,"identity":"48a9bf12-bfcd-4d7f-893d-213e505e5167","added_by":"auto","created_at":"2024-11-29 18:01:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5591295,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4494391/v1/c171d8e1-5b9c-4cf9-a409-83e6048d7728.pdf"},{"id":58233384,"identity":"373d1738-b24a-4518-a05a-88e2ba52641c","added_by":"auto","created_at":"2024-06-12 20:06:15","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":179188,"visible":true,"origin":"","legend":"","description":"","filename":"supplementmaterialJournal.docx","url":"https://assets-eu.researchsquare.com/files/rs-4494391/v1/01c61e2cf4216d55657afa3f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"LAASO algorithm-based bioinformatics analysis for exploring the pathogenesis and immune infiltration of atrial fibrillation and dilated cardiomyopathy","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDilated cardiomyopathy is conventionally defined as ventricular remodelling and dysfunction with left ventricular or biventricular dilatation but without coronary artery disease or cardiac overload\u003csup\u003e1\u003c/sup\u003e. However, DCM accompanied by atrial fibrillation is extremely common in the population\u003csup\u003e2\u003c/sup\u003e. In fact, AF is a common arrhythmia in DCM and affects common prognostic indicators such as all-cause mortality. In addition, it can also increase the risk of stroke and hospitalization for heart failure\u003csup\u003e3\u003c/sup\u003e. Therefore, AF and DCM accompany and influence each other.\u003c/p\u003e \u003cp\u003eFrom a pathophysiological perspective, AF and DCM share many common pathogeneses. For example, cardiac remodeling and fibrosis are common mechanism of both diseases\u003csup\u003e4\u003c/sup\u003e. Increased atrial pressure and dilatation in DCM patients cause cardiac structure remodeling and scar formation, intertwining the pathogenesis of these two diseases further\u003csup\u003e5\u003c/sup\u003e. Many changes in gene expression and immune mechanisms may be related to the common mechanism of their pathogeneses\u003csup\u003e6\u003c/sup\u003e. In addition, treatment of AF with DCM presents a therapeutic challenge due to the heightened risks associated with traditional drug and nondrug therapies\u003csup\u003e2, 7\u003c/sup\u003e. Therefore, it is quite necessary to explore the co-pathogenesis, molecular diagnostic strategy and treatment of AF and DCM.\u003c/p\u003e \u003cp\u003eIn light of these complexities, our study employed a comprehensive analysis using six Gene Expression Omnibus (GEO) databases to delve deeper into the molecular mechanisms underlying the co-pathogenesis of AF and DCM. Through rigorous bioinformatics methods and machine learning tools, we aimed to identify key differentially expressed genes (DEGs), explore their shared signaling pathways, and assess their potential as diagnostic markers. This integrative approach hopes to pave the way for more precise diagnosis, mitigating the combined impact of AF and DCM on patient health.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Data download\u003c/h2\u003e \u003cp\u003eWe sourced 6 high-quality raw expression profile datasets from the GEO database, 2 for AF (GSE2240 and GSE41177) and 4 for DCM (GSE120895, GSE29819, GSE3585 and GSE42955). Datasets are available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e for more information\u003csup\u003e8\u003c/sup\u003e. 4 of these databases (GSE2240, GSE120895, GSE29819, GSE3585) were used as training sets to screen key genes and build models, while the remaining two datasets (GSE41177 and GSE42955) were served as validation sets for external validation. The details of the above datasets are presented in Table\u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. A total of 110 patients (80 for DCM and 30 for control) in DCM were included in the study, while 68 patients (42 for AF and 26 for control) were included in AF.\u003c/p\u003e \u003cp\u003eWe mainly used R software to process the chip data.\u0026ldquo;Gcrma\u0026rdquo;package was used to background correction and standardize the microarray data. Due to the different date, personnel, place and equipment of the experiment, there must be batch effects in the chip data. We can't get rid of batch effects by standardizing the data. Therefore, we remove the batch effect by using \u0026ldquo;SVA\u0026rdquo; packages\u003csup\u003e9\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Differentially expressed genes (DEGs) analysis\u003c/h2\u003e \u003cp\u003eAfter background correction, normalization and removal of batch effect, differentially expressed genes analysis was performed on the chip data. DEGs in AF and DCM datasets were identifed using the \u0026ldquo;Limma\u0026rdquo; package in R software\u003csup\u003e10\u003c/sup\u003e. The criteria for DEGs screening are adjusted P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log FC| \u0026ge; 0.5. In addition, because there are 3 DCM datasets and they are not from the same platform, the \u0026ldquo;RobustRankAggreg\u0026rdquo; package was used to conduct RRA analysis to find the common DEGs of the 3 DCM datasets.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3.WGCNA network construction and module identification\u003c/h2\u003e \u003cp\u003eWGCNA is a bioinformatics analysis method used to describe gene association patterns among different samples. It can divide DEGs into different modules and look for the correlations between them and phenotypes. There are a total of 4 steps in this analysis and the key R package is \u0026ldquo;WGCNA\u0026rdquo;\u003csup\u003e11\u003c/sup\u003e. In step 1,we cleaned the data and removed outlier samples. In step 2, we screened out the most appropriate soft thresholding power(β) to keep a balance between scale independence and mean connectivity. In step 3, the gene expression similarity matrix was calculated and put into an adjacency matrix according to the soft thresholding power (β) set in the previous step by using the \u0026ldquo;adjacency\u0026rdquo; function. Then, a topological overlap matrix (TOM) was calculated to minimize the effects of noise and spurious associations according to the adjacency matrix we obtained in the previous step. In step 4, we used the R package \u0026ldquo;flashClust\u0026rdquo; to build network modules in different colors. Due to the large number of modules, we need to further merge these modules constructed by dynamic shear tree clustering. Finally,associations between different modules of genes and diseases will be presented.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Identification of shared genes and pathway enrichment\u003c/h2\u003e \u003cp\u003eVenn diagram was used to present the results of the WGCNA and DEGs consults. The result of RRA is shown in supplement S1. The overlapping of these 2 results were used as further functional enrichment analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted by using the \u0026ldquo;enrichplot\u0026rdquo; and \u0026ldquo;ggplot2\u0026rdquo;packages in R. Statistical significance was set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Machine learning\u003c/h2\u003e \u003cp\u003eThe least absolute shrinkage and selection operator (LASSO), a new and advanced machine learning algorithm, was used to refine the list of potential diagnostic genes. The \u0026ldquo;glmnet\u0026rdquo; package in R was used to conduct this algorithm to screen the best predictors of AF and DCM from their 20 DEGs\u003csup\u003e12\u003c/sup\u003e. This package was aslo used to plot clinical decision curve and clinical impact curve. Moreover, the internal verification was conducted by plotting consistency curve and calculating coefficient of consistency by using the \u0026ldquo;rms\u0026rdquo; package. Finally, nomogram was used to visualize the lasso model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Statistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using IBM SPSS Statistics 26 and R 4.2.2 software. Results were displayed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. Diferences between the two groups were compared by unpaired\u003c/p\u003e \u003cp\u003eStudent\u0026prime;s t-test. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was regarded as statistical signifcance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. ssGSEA\u003c/h2\u003e \u003cp\u003essGSEA was performed by the \u0026ldquo;GSVA\u0026rdquo; R package to analyse the infiltration of 29 immune cells in diseased and normal samples. Spearman\u0026rsquo;s rank correlation tests was used to analyse the relationship between hub genes and immune cells.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Data processing\u003c/h2\u003e \u003cp\u003eTe strategy of bioinformatics analysis is performed as shown in Figure. S1. First, the raw materials of 6 GEO databases (2 for AF and 4 for DCM) were downloaded from GEO official website and the targets information was organized. Second, the gcrma method was used to normalize the data of 6 GEO databases. Third, we used the \u0026ldquo;sva\u0026rdquo; package to remove batch effects of the GEO datasets based on the production date of the chips. PCA plots were used to represent the clustering of data according to their groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Identification of DEGs\u003c/h2\u003e \u003cp\u003eFor GSE2240 (AF), 352 DEGs were identified based on the default standard we set (adjusted P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log FC| \u0026ge; 0.5), including 119 upregulated DEGs and 233 downregulated DEGs ( Fig.\u0026nbsp;1). For the 3 datasets of DCM as training set, the numbers of DEGs are 389, 1126 and 244 respectively. The RRA method was used to identify the common DEGs of these 3 DCM datasets, including top 150 common upregulated DEGs and top 150 common downregulated DEGs (Fig.\u0026nbsp;2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3. The construction of weighted gene co-expression network and the identification of key modules in AF\u003c/h2\u003e \u003cp\u003eThe weighted gene co-expression network analysis (WGCNA) was conducted to further explore the most relevant gene modules with AF. First, sample clustering tree was used to remove 4 outliers and 26 remaining samples were used in WGCNA analysis (Fig.\u0026nbsp;3a). In order to ensure as large scale independence and average connectivity as possible, we choose 10 as the most appropriate soft-thresholding power (Fig.\u0026nbsp;3b). We initially divided all of the genes into 28\u003c/p\u003e \u003cp\u003emodules and merged modules with similarities greater than 0.75. Finally, 12 gene modules were generated and the correlation degree between each module and AF is shown in Fig.\u0026nbsp;4d. The result showed that the skyblue2 module has the strongest positive correlation with AF (r\u0026thinsp;=\u0026thinsp;0.81, p\u0026lt;0.001), while the darkorange module has the strongest negative correlation with AF(r=-0.63, p\u0026lt;0.001). The intersection of the DEGs and AF hub modules was shown by a Venn diagram, and 60 intersection upregulated genes and 134 intersection downregulated genes were obtained(Fig.\u0026nbsp;4g).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Identification of shared genes and pathway enrichment\u003c/h2\u003e \u003cp\u003eA total of 194 AF DEGs were selected by WGCNA analysis, while 300 DCM DEGs were identified by RRA method. The 20 intersection of AF and DCM DEGs was shown by a Venn diagram (Fig.\u0026nbsp;4a). They are ACE2, ASPN, COMP, FRZB, LOXL1, LTBP2, NAV2, NES, PLCE1,\u003c/p\u003e \u003cp\u003eRGCC, TNNT1, AMD1, CD14, GOS2, KCNN2, MID1IP1, PPP1R1A, RARRES1, SELENBP1, TGFBR3. GO and KEGG enrichment analyses were conducted on the above 20 genes to explore their shared pathways. The GO analysis indicated that these genes may have relationship with muscle contraction, collagen-containing extracellular matrix, collagen binding, SMAD binding and glycosaminoglycan binding(Fig.\u0026nbsp;4b-d). The KEGG analysis showed that these common genes were strongly associated with phagosome and renin-angiotensin system(Fig.\u0026nbsp;4e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Potential shared diagnostic genes selection by least absolute shrinkage and selection operator\u003c/h2\u003e \u003cp\u003eThe 20 genes we screened out in the previous step were put into LASSO regression algorithm to further identify the most meaningful shared diagnostic genes of AF and DCM datasets. A λ corresponding to the lowest binomial deviance is the most appropriate λ value (Fig.\u0026nbsp;5a-b). By using this algorithm, 8 AF diagnostic genes and 9 DCM diagnostic genes were selected. Finally, the Venn diagram showed 3 common shared genes (RGCC, KCNN2, and TGFBR3) have the greatest diagnostic value.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Immune cell infiltration analysis\u003c/h2\u003e \u003cp\u003eThe infiltration of 28 types of immune cells in AF and DCM was analysed in the study. We found that Th2, Th17 and B cells are obviously upregulated in both groups, while Tregs are obviously downregulated in both groups (Fig.\u0026nbsp;6a-b). In addition, the biomarker RGCC has strong relationship with these immune cell changes (Fig.\u0026nbsp;6c-d).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.7. Model performance and internal validation\u003c/h2\u003e \u003cp\u003eThe results showed that the AUC values of the diagnostic LASSO model based on 3 common shared genes are 1.000 and 0.907 for AF and DCM respectively (Fig.\u0026nbsp;7a-b). Clinical decision curves and clinical impact curves were drawn to evaluate the actual clinical application of the model. The results showed that the lasso model has a definite large net benefit for patients under all threshold probabilities and good risk stratification accuracy (Fig.\u0026nbsp;7c-f). In addition, we used the consistency curve to verify the model internally (Fig.\u0026nbsp;7g-h). The consistency curve shows that the model is in great agreement with the actual situation. In addition, the bias corrected C-index of the lasso model are 0.995 and 0.878.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.8. Model visualization and external validation\u003c/h2\u003e \u003cp\u003eNomograms were used to visualize the diagnostic model constructed by 3 common shared genes (RGCC, KCNN2, TGFBR3) in AF and DCM. 2 GEO datasets (GSE41177 and GSE42955), one\u003c/p\u003e \u003cp\u003efor AF and the other for DCM, were used as validation set to test the extrapolation of the model.\u003c/p\u003e \u003cp\u003eCompared to the training set, the AUC for the validation set remain high, which are 0.891 and 0.976 respectively. This result showed that the findings of this study may also be applied to other populations (Fig.\u0026nbsp;8a-d).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eFor the past few years, the applications of microarray and sequencing methods have been very popular in exploring the hidden mechanisms behind various diseases, which marks the rapid development of bioinformatics technology\u003csup\u003e13, 14\u003c/sup\u003e. In the meantime, machine learning tools are increasingly performed to variables screening and diagnosis model construction based on big data, which could help to find novel genes and potential diagnostic/prognostic biomarkers\u003csup\u003e15\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this article, we combine traditional bioinformatics with machine learning to explore the common mechanism behind AF and DCM. The results indicated that 20 genes ( ACE2, ASPN, COMP, FRZB, LOXL1, LTBP2, NAV2, NES, PLCE1, RGCC, TNNT1, AMD1, CD14, GOS2, KCNN2, MID1IP1, PPP1R1A ,RARRES1, SELENBP1, TGFBR3) were the most vital cross-talk genes between AF and DCM, suggesting a strong association with muscle contraction, collagen interactions, and the renin-angiotensin system. Among them, RGCC, KCNN2, and TGFBR3 emerged as the most significant diagnostic markers. The diagnostic model constructed by these 3 genes is highly accurate and has also been validated in external datasets. In addition, the immune infiltration results showed that Th2 cells and the imbalance of Th17/Treg cells may have strong relationship with both of the diseases.\u003c/p\u003e \u003cp\u003eThe result of GO and KEGG analysis indicates that the shared genes are associated with muscle contraction, collagen-containing extracellular matrix and renin-angiotensin system. Myocardial contraction is the most basic manifestation of cardiac circulatory function and is regulated by Ca\u003csup\u003e2+\u003c/sup\u003e, which is a fundamental second messenger in all cell types. In the heart, abnormal contraction caused by abnormal regulation of Ca2\u0026thinsp;+\u0026thinsp;have strong relationship with the occurrence of AF and DCM\u003csup\u003e16\u003c/sup\u003e. Moreover, extracellular matrix production and fibrosis are the common mechanism of these two diseases. Some studies indicate that the degree of atrial fibrosis is highly correlated with the progression and prognosis of atrial fibrillation\u003csup\u003e17, 18\u003c/sup\u003e. Cardiac fibrosis plays an important role in dilated cardiomyopathy, increasing its morbidity and mortality\u003csup\u003e19\u003c/sup\u003e. In addition,\u003c/p\u003e \u003cp\u003erenin-angiotensin system alter the extracellular matrix by impacting the synthesis of type 1 and type 3 collagen, fibronectin, and α-smooth muscle actin. Therefore, the pathways enriched by these 20 genes are highly associated with both diseases.\u003c/p\u003e \u003cp\u003eIn this article, the possible common immune mechanism of AF and DCM was elucidated based\u003c/p\u003e \u003cp\u003eon the results of immune infiltration analysis. We observed a notable upregulation of Th2 and Th17 cells in both conditions, coupled with a significant downregulation of Treg cells. Th2 cells is a subset of CD4\u0026thinsp;+\u0026thinsp;T cells which could lead to inflammatory responses and fibrosis by secreting Th2 cytokines\u003csup\u003e20\u003c/sup\u003e. Some studies indicate that Th2 immune response may be related to ventricular remodeling which promotes the progression of DCM\u003csup\u003e21\u003c/sup\u003e. Th17 cells and Treg cells share a common precursor cell and have the same signal for initial differentiation\u003csup\u003e22\u003c/sup\u003e. Studies have shown that the imbalance of Th17/Treg cells induces many inflammatory diseases\u003csup\u003e23\u003c/sup\u003e. Th17 cells increase tissue inflammation and promote autoimmune activation while Treg cells antagonize this biological process, which could play a protective role\u003csup\u003e21\u003c/sup\u003e. What's more, the elevated plasma levels of Th17-related cytokines may increase the risk of atrial fibrillation\u003csup\u003e24\u003c/sup\u003e. These results show that Th2 cells and the imbalance of Th17/Treg cells may play an important role in the progression of AF and DCM.\u003c/p\u003e \u003cp\u003eRGCC, also known as RGC-32, is a kind of cell cycle regulator which is positively regulated by\u003c/p\u003e \u003cp\u003eTGF-β\u003csup\u003e25\u003c/sup\u003e. The results of our immunoinfiltration analysis showed a strong association between RGCC and the above-mentioned immune cells, especially for Th17 and Treg cells, and Treg/Th17\u003c/p\u003e \u003cp\u003eplays an important role in DCM\u003csup\u003e26\u003c/sup\u003e. Li et al. have shown that RGCC could accelerate the progression of DCM by inducing the imbalance of Treg/Th17 ratio, which is consistent with our findings\u003csup\u003e27\u003c/sup\u003e. However, RGCC has been less reported in AF and little is known about how these subpopulations of immune cells influence the progression of AF\u003csup\u003e28\u003c/sup\u003e. In our study,we suggest that the biological process of RGCC leads to the imbalance of Treg/Th17 ratio may be a common mechanism of AF and DCM.\u003c/p\u003e \u003cp\u003eTGFBR3, also known as betaglycan, is the most highly expressed TGF-βreceptors in the cell.\u003c/p\u003e \u003cp\u003eWhen TGF-βbinds to TGFBR3 it will not bind to TGFBR1 and TGFBR2. While TGFBR1 and TGFBR2 work through canonical pathway, TGFBR3 works via a pathway that is noncanonical\u003csup\u003e29\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTGF-βcould activate smad2/3 expression by binding to TGFBR1/2, which could cause fibrosis and extracellular-matrix remodeling in AF and DCM\u003csup\u003e18\u003c/sup\u003e. TGFBR3 is acted as an antagonist of TGF-βwhich could inhibit this biological process to prevent the occurrence of these 2 diseases\u003csup\u003e30\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eKCNN2 encodes a kind of small-conductance Ca2+-activated K\u0026thinsp;+\u0026thinsp;channel named SK2. SK channels, particularly SK2 channels, are expressed in different kinds of cardiomyocytes\u003csup\u003e31\u003c/sup\u003e. In our study, this gene is significantly downregulated in both diseases. Abnormality of intracellular Ca2\u0026thinsp;+\u0026thinsp;handling has a strong relationship with the occurrence and progression of AF and KCNN2 could influence intracellular Ca2\u0026thinsp;+\u0026thinsp;handling and membrane electrical events. In mouse model, the knockout of KCNN2 could lead to atrial action potential prolongation and atrial arrhythmias\u003csup\u003e32, 33\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMoreover, mitochondrial SK channels may decrease the generation of ROS and Ca2\u0026thinsp;+\u0026thinsp;overload,\u003c/p\u003e \u003cp\u003ewhich could lead to the damage of myocardial cells\u003csup\u003e31\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur study has several strengths. In this study, a LASSO model was constructed for the first time to explore the common mechanism of AF and DCM. The results showed that the diagnostic model of these 3 hub genes based on LASSO has high accuracy and reliability, of which the auc is higher than 0.9. In the meantime, both internal and external verification prove that the model has high consistency and extrapolation. Moreover, the genes we screened have also been shown in other literature to be highly associated with AF and DCM. The results of immunoinfiltration analysis were also highly consistent with the conclusions of the model. However, the sample size of the article was relatively small. For this point, we have performed external validation to prove the reliability of the results.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study is the first to combine bioinformatics tools and machine learning to explore the commonpathogeneses between AF and DCM. RGCC, KCNN2 and TGFBR3 are the most important cross-talk genes between AF and DCM and have high diagnostic value. The imbalance of Th17/Treg and Th2 cells may be vital in the progression of these 2 diseases.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Hongdao li and Shuanglun Xie wrote the main manuscript text and Yuyang Chen prepared figures 1-3. Shumin Liang, Bifang Mai, Wenqing Tu1 and Yuhan Lin prepared figures 4-9.Qi Chen checked the details.All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The datasets generated and/or analysed during the current study are not publicly available due to its big amount but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOn behalf of all authors, the corresponding author states that there is no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFunding Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;This work was supported by the National Natural Science Foundation of China [grant no. 82070412].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHeymans, S.; Lakdawala, N. K.; Tsch\u0026ouml;pe, C.; Klingel, K. Dilated cardiomyopathy: causes, mechanisms, and current and future treatment approaches. \u003cem\u003eLancet \u003c/em\u003e\u003cstrong\u003e2023\u003c/strong\u003e, \u003cem\u003e402\u003c/em\u003e (10406), 998-1011. DOI: 10.1016/s0140-6736(23)01241-2 From NLM.\u003c/li\u003e\n\u003cli\u003eSaxon, L. A. Atrial fibrillation and dilated cardiomyopathy: therapeutic strategies when sinus rhythm cannot be maintained. \u003cem\u003ePacing Clin Electrophysiol \u003c/em\u003e\u003cstrong\u003e1997\u003c/strong\u003e, \u003cem\u003e20\u003c/em\u003e (3 Pt 1), 720-725. DOI: 10.1111/j.1540-8159.1997.tb03891.x From NLM.\u003c/li\u003e\n\u003cli\u003eVerma, A.; Kalman, J. M.; Callans, D. J. Treatment of Patients With Atrial Fibrillation and Heart Failure With Reduced Ejection Fraction. \u003cem\u003eCirculation \u003c/em\u003e\u003cstrong\u003e2017\u003c/strong\u003e, \u003cem\u003e135\u003c/em\u003e (16), 1547-1563. DOI: 10.1161/circulationaha.116.026054 From NLM.\u003c/li\u003e\n\u003cli\u003eCarlisle, M. A.; Fudim, M.; DeVore, A. D.; Piccini, J. P. Heart Failure and Atrial Fibrillation, Like Fire and Fury. \u003cem\u003eJACC Heart Fail \u003c/em\u003e\u003cstrong\u003e2019\u003c/strong\u003e, \u003cem\u003e7\u003c/em\u003e (6), 447-456. DOI: 10.1016/j.jchf.2019.03.005 From NLM.\u003c/li\u003e\n\u003cli\u003eGan, T.; Hu, J.; Aledan, A. K. O.; Liu, W.; Li, C.; Lu, S.; Wang, Y.; Xu, Q.; Wang, Y.; Wang, Z. Exploring the pathogenesis and immune infiltration in dilated cardiomyopathy complicated with atrial fibrillation by bioinformatics analysis. \u003cem\u003eFront Immunol \u003c/em\u003e\u003cstrong\u003e2023\u003c/strong\u003e, \u003cem\u003e14\u003c/em\u003e, 1049351. DOI: 10.3389/fimmu.2023.1049351 From NLM Medline.\u003c/li\u003e\n\u003cli\u003eLardizabal, J. A.; Deedwania, P. C. Atrial fibrillation in heart failure. \u003cem\u003eMed Clin North Am \u003c/em\u003e\u003cstrong\u003e2012\u003c/strong\u003e, \u003cem\u003e96\u003c/em\u003e (5), 987-1000. DOI: 10.1016/j.mcna.2012.07.007 From NLM.\u003c/li\u003e\n\u003cli\u003eYeung, C.; Enriquez, A.; Suarez-Fuster, L.; Baranchuk, A. Atrial fibrillation in patients with inherited cardiomyopathies. \u003cem\u003eEuropace \u003c/em\u003e\u003cstrong\u003e2019\u003c/strong\u003e, \u003cem\u003e21\u003c/em\u003e (1), 22-32. DOI: 10.1093/europace/euy064 From NLM.\u003c/li\u003e\n\u003cli\u003eBarrett, T.; Wilhite, S. E.; Ledoux, P.; Evangelista, C.; Kim, I. F.; Tomashevsky, M.; Marshall, K. A.; Phillippy, K. H.; Sherman, P. M.; Holko, M.; et al. NCBI GEO: archive for functional genomics data sets--update. \u003cem\u003eNucleic Acids Res \u003c/em\u003e\u003cstrong\u003e2013\u003c/strong\u003e, \u003cem\u003e41\u003c/em\u003e (Database issue), D991-995. DOI: 10.1093/nar/gks1193 From NLM Medline.\u003c/li\u003e\n\u003cli\u003eLeek, J. T.; Johnson, W. E.; Parker, H. S.; Jaffe, A. E.; Storey, J. D. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. \u003cem\u003eBioinformatics \u003c/em\u003e\u003cstrong\u003e2012\u003c/strong\u003e, \u003cem\u003e28\u003c/em\u003e (6), 882-883. DOI: 10.1093/bioinformatics/bts034 From NLM Medline.\u003c/li\u003e\n\u003cli\u003eRitchie, M. E.; Phipson, B.; Wu, D.; Hu, Y.; Law, C. W.; Shi, W.; Smyth, G. K. limma powers differential expression analyses for RNA-sequencing and microarray studies. \u003cem\u003eNucleic Acids Res \u003c/em\u003e\u003cstrong\u003e2015\u003c/strong\u003e, \u003cem\u003e43\u003c/em\u003e (7), e47. DOI: 10.1093/nar/gkv007 From NLM Medline.\u003c/li\u003e\n\u003cli\u003eLangfelder, P.; Horvath, S. WGCNA: an R package for weighted correlation network analysis. \u003cem\u003eBMC Bioinformatics \u003c/em\u003e\u003cstrong\u003e2008\u003c/strong\u003e, \u003cem\u003e9\u003c/em\u003e, 559. DOI: 10.1186/1471-2105-9-559 From NLM Medline.\u003c/li\u003e\n\u003cli\u003eJerome Friedman, T. H. Regularization Paths for Generalized Linear Models via Coordinate Descent. \u003cem\u003eJournal of Statistical Software \u003c/em\u003e\u003cstrong\u003e2010\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003ePetrik, J. Diagnostic applications of microarrays. \u003cem\u003eTransfus Med \u003c/em\u003e\u003cstrong\u003e2006\u003c/strong\u003e, \u003cem\u003e16\u003c/em\u003e (4), 233-247. DOI: 10.1111/j.1365-3148.2006.00673.x From NLM.\u003c/li\u003e\n\u003cli\u003eSu, Z.; Ning, B.; Fang, H.; Hong, H.; Perkins, R.; Tong, W.; Shi, L. Next-generation sequencing and its applications in molecular diagnostics. \u003cem\u003eExpert Rev Mol Diagn \u003c/em\u003e\u003cstrong\u003e2011\u003c/strong\u003e, \u003cem\u003e11\u003c/em\u003e (3), 333-343. DOI: 10.1586/erm.11.3 From NLM.\u003c/li\u003e\n\u003cli\u003eSajda, P. Machine learning for detection and diagnosis of disease. \u003cem\u003eAnnu Rev Biomed Eng \u003c/em\u003e\u003cstrong\u003e2006\u003c/strong\u003e, \u003cem\u003e8\u003c/em\u003e, 537-565. DOI: 10.1146/annurev.bioeng.8.061505.095802 From NLM.\u003c/li\u003e\n\u003cli\u003eDridi, H.; Kushnir, A.; Zalk, R.; Yuan, Q.; Melville, Z.; Marks, A. R. Intracellular calcium leak in heart failure and atrial fibrillation: a unifying mechanism and therapeutic target. \u003cem\u003eNat Rev Cardiol \u003c/em\u003e\u003cstrong\u003e2020\u003c/strong\u003e, \u003cem\u003e17\u003c/em\u003e (11), 732-747. DOI: 10.1038/s41569-020-0394-8 From NLM.\u003c/li\u003e\n\u003cli\u003eMalcolme-Lawes, L. C.; Juli, C.; Karim, R.; Bai, W.; Quest, R.; Lim, P. B.; Jamil-Copley, S.; Kojodjojo, P.; Ariff, B.; Davies, D. W.; et al. Automated analysis of atrial late gadolinium enhancement imaging that correlates with endocardial voltage and clinical outcomes: a 2-center study. \u003cem\u003eHeart Rhythm \u003c/em\u003e\u003cstrong\u003e2013\u003c/strong\u003e, \u003cem\u003e10\u003c/em\u003e (8), 1184-1191. DOI: 10.1016/j.hrthm.2013.04.030 From NLM.\u003c/li\u003e\n\u003cli\u003eNattel, S.; Heijman, J.; Zhou, L.; Dobrev, D. Molecular Basis of Atrial Fibrillation Pathophysiology and Therapy: A Translational Perspective. \u003cem\u003eCirc Res \u003c/em\u003e\u003cstrong\u003e2020\u003c/strong\u003e, \u003cem\u003e127\u003c/em\u003e (1), 51-72. DOI: 10.1161/circresaha.120.316363 From NLM.\u003c/li\u003e\n\u003cli\u003eCojan-Minzat, B. O.; Zlibut, A.; Agoston-Coldea, L. Non-ischemic dilated cardiomyopathy and cardiac fibrosis. \u003cem\u003eHeart Fail Rev \u003c/em\u003e\u003cstrong\u003e2021\u003c/strong\u003e, \u003cem\u003e26\u003c/em\u003e (5), 1081-1101. DOI: 10.1007/s10741-020-09940-0 From NLM.\u003c/li\u003e\n\u003cli\u003eKokubo, K.; Onodera, A.; Kiuchi, M.; Tsuji, K.; Hirahara, K.; Nakayama, T. Conventional and pathogenic Th2 cells in inflammation, tissue repair, and fibrosis. \u003cem\u003eFront Immunol \u003c/em\u003e\u003cstrong\u003e2022\u003c/strong\u003e, \u003cem\u003e13\u003c/em\u003e, 945063. DOI: 10.3389/fimmu.2022.945063 From NLM.\u003c/li\u003e\n\u003cli\u003eZheng, S. Y.; Dong, J. Z. Role of Toll-Like Receptors and Th Responses in Viral Myocarditis. \u003cem\u003eFront Immunol \u003c/em\u003e\u003cstrong\u003e2022\u003c/strong\u003e, \u003cem\u003e13\u003c/em\u003e, 843891. DOI: 10.3389/fimmu.2022.843891 From NLM.\u003c/li\u003e\n\u003cli\u003eLee, G. R. The Balance of Th17 versus Treg Cells in Autoimmunity. \u003cem\u003eInt J Mol Sci \u003c/em\u003e\u003cstrong\u003e2018\u003c/strong\u003e, \u003cem\u003e19\u003c/em\u003e (3). DOI: 10.3390/ijms19030730 From NLM.\u003c/li\u003e\n\u003cli\u003eThomas, R.; Qiao, S.; Yang, X. Th17/Treg Imbalance: Implications in Lung Inflammatory Diseases. \u003cem\u003eInt J Mol Sci \u003c/em\u003e\u003cstrong\u003e2023\u003c/strong\u003e, \u003cem\u003e24\u003c/em\u003e (5). DOI: 10.3390/ijms24054865 From NLM.\u003c/li\u003e\n\u003cli\u003eWu, N.; Xu, B.; Liu, Y.; Chen, X.; Tang, H.; Wu, L.; Xiang, Y.; Zhang, M.; Shu, M.; Song, Z.; et al. Elevated plasma levels of Th17-related cytokines are associated with increased risk of atrial fibrillation. \u003cem\u003eSci Rep \u003c/em\u003e\u003cstrong\u003e2016\u003c/strong\u003e, \u003cem\u003e6\u003c/em\u003e, 26543. DOI: 10.1038/srep26543 From NLM.\u003c/li\u003e\n\u003cli\u003eVlaicu, S. I.; Tatomir, A.; Anselmo, F.; Boodhoo, D.; Chira, R.; Rus, V.; Rus, H. RGC-32 and diseases: the first 20 years. \u003cem\u003eImmunol Res \u003c/em\u003e\u003cstrong\u003e2019\u003c/strong\u003e, \u003cem\u003e67\u003c/em\u003e (2-3), 267-279. DOI: 10.1007/s12026-019-09080-0 From NLM.\u003c/li\u003e\n\u003cli\u003eLi, J.; Wang, L.; Wang, S.; Zhu, H.; Ye, P.; Xie, A.; Shen, B.; Liu, C.; Guo, C.; Fu, Q.; et al. The Treg/Th17 imbalance in patients with idiopathic dilated cardiomyopathy. \u003cem\u003eScand J Immunol \u003c/em\u003e\u003cstrong\u003e2010\u003c/strong\u003e, \u003cem\u003e71\u003c/em\u003e (4), 298-303. DOI: 10.1111/j.1365-3083.2010.02374.x From NLM.\u003c/li\u003e\n\u003cli\u003eLi, B.; Zhou, W.; Tang, X.; Wang, W.; Pan, J.; Tan, M. Response Gene to Complement-32 Promotes the Imbalance of Treg/Th17 in Patients with Dilated Cardiomyopathy. \u003cem\u003eCell Physiol Biochem \u003c/em\u003e\u003cstrong\u003e2017\u003c/strong\u003e, \u003cem\u003e43\u003c/em\u003e (4), 1515-1525. DOI: 10.1159/000481975 From NLM.\u003c/li\u003e\n\u003cli\u003eHu, Y. F.; Chen, Y. J.; Lin, Y. J.; Chen, S. A. Inflammation and the pathogenesis of atrial fibrillation. \u003cem\u003eNat Rev Cardiol \u003c/em\u003e\u003cstrong\u003e2015\u003c/strong\u003e, \u003cem\u003e12\u003c/em\u003e (4), 230-243. DOI: 10.1038/nrcardio.2015.2 From NLM.\u003c/li\u003e\n\u003cli\u003eVander Ark, A.; Cao, J.; Li, X. TGF-\u0026beta; receptors: In and beyond TGF-\u0026beta; signaling. \u003cem\u003eCell Signal \u003c/em\u003e\u003cstrong\u003e2018\u003c/strong\u003e, \u003cem\u003e52\u003c/em\u003e, 112-120. DOI: 10.1016/j.cellsig.2018.09.002 From NLM.\u003c/li\u003e\n\u003cli\u003eYang, Z.; Xiao, Z.; Guo, H.; Fang, X.; Liang, J.; Zhu, J.; Yang, J.; Li, H.; Pan, R.; Yuan, S.; et al. Novel role of the clustered miR-23b-3p and miR-27b-3p in enhanced expression of fibrosis-associated genes by targeting TGFBR3 in atrial fibroblasts. \u003cem\u003eJ Cell Mol Med \u003c/em\u003e\u003cstrong\u003e2019\u003c/strong\u003e, \u003cem\u003e23\u003c/em\u003e (5), 3246-3256. DOI: 10.1111/jcmm.14211 From NLM.\u003c/li\u003e\n\u003cli\u003eLiu, T.; Li, T.; Xu, D.; Wang, Y.; Zhou, Y.; Wan, J.; Huang, C. L.; Tan, X. Small-conductance calcium-activated potassium channels in the heart: expression, regulation and pathological implications. \u003cem\u003ePhilos Trans R Soc Lond B Biol Sci \u003c/em\u003e\u003cstrong\u003e2023\u003c/strong\u003e, \u003cem\u003e378\u003c/em\u003e (1879), 20220171. DOI: 10.1098/rstb.2022.0171 From NLM.\u003c/li\u003e\n\u003cli\u003eLi, N.; Timofeyev, V.; Tuteja, D.; Xu, D.; Lu, L.; Zhang, Q.; Zhang, Z.; Singapuri, A.; Albert, T. R.; Rajagopal, A. V.; et al. Ablation of a Ca2+-activated K+ channel (SK2 channel) results in action potential prolongation in atrial myocytes and atrial fibrillation. \u003cem\u003eJ Physiol \u003c/em\u003e\u003cstrong\u003e2009\u003c/strong\u003e, \u003cem\u003e587\u003c/em\u003e (Pt 5), 1087-1100. DOI: 10.1113/jphysiol.2008.167718 From NLM.\u003c/li\u003e\n\u003cli\u003eYu, T.; Deng, C.; Wu, R.; Guo, H.; Zheng, S.; Yu, X.; Shan, Z.; Kuang, S.; Lin, Q. Decreased expression of small-conductance Ca2+-activated K+ channels SK1 and SK2 in human chronic atrial fibrillation. \u003cem\u003eLife Sci \u003c/em\u003e\u003cstrong\u003e2012\u003c/strong\u003e, \u003cem\u003e90\u003c/em\u003e (5-6), 219-227. DOI: 10.1016/j.lfs.2011.11.008 From NLM.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"atrial fibrillation, dilated cardiomyopathy, bioinformatics, differentially expressed genes, hub immune-related genes, immune infiltration","lastPublishedDoi":"10.21203/rs.3.rs-4494391/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4494391/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e It is well known that atrial fibrillation (AF) is a serious complication of dilated\u003c/p\u003e\n\u003cp\u003ecardiomyopathy (DCM) and they could affect each other. This study endeavors to uncover the shared diagnostic markers and immune pathways contributing to both disorders.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We downloaded AF and DCM data from the Gene Expression Omnibus (GEO) database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were used to clarify shared signaling pathways of DEGs. Further, the least absolute shrinkage and selection operator (LASSO) regression was applied to pinpoint key hub genes and construct a diagnostic model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e A total of 20 common DEGs in AF and DCM were identified for subsequent analyses.\u003c/p\u003e\n\u003cp\u003eThe LASSO analysis results showed that RGCC, KCNN2 and TGFBR3 are 3 most important cross-talk genes between these 2 diseases. The immune infiltration results revealed that Th17/Treg cells may play an important role in the pathogeneses of AF and DCM.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e This pioneering study merges bioinformatics and machine learning to map the shared molecular landscape of AF and DCM, highlighting RGCC, KCNN2, and TGFBR3 as key diagnostic biomarkers. The imbalance of Th17/Treg may be vital in the progression of these 2 diseases.\u003c/p\u003e","manuscriptTitle":"LAASO algorithm-based bioinformatics analysis for exploring the pathogenesis and immune infiltration of atrial fibrillation and dilated cardiomyopathy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-12 20:06:10","doi":"10.21203/rs.3.rs-4494391/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"048ef8d0-fe05-406d-a259-2927d0adab90","owner":[],"postedDate":"June 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-29T17:53:19+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-12 20:06:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4494391","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4494391","identity":"rs-4494391","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}