Comprehensive bioinformatics analysis identifies biomarkers for cardiovascular risk in end-stage renal diseases

Comprehensive bioinformatics analysis identifies biomarkers for cardiovascular risk in end-stage renal diseases | 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 Comprehensive bioinformatics analysis identifies biomarkers for cardiovascular risk in end-stage renal diseases Fangfang Lu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5292194/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 Objective End-stage renal disease (ESRD) can increase the risk of cardiovascular disease (CV). We aimed to investigate the pathways and mechanisms associated with potential protective genes linked to CV (CVP). Methods We conducted a systematic bioinformatics analysis using publicly available datasets from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were identified in patients with ESRD with and without arrhythmia using stringent statistical criteria. Functional enrichment analyses were performed using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways to elucidate the biological roles of these DEGs. Receiver Operating Characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of the identified biomarkers for CV risk prediction. Results Our analysis revealed a distinct set of DEGs in ESRD patients with arrhythmia compared to those without arrhythmia. GO and KEGG pathway analyses indicated that these DEGs were involved in key biological processes and pathways relevant to cardiovascular disorders and renal function, including wound healing, platelet activation, and fluid-level regulation. Moreover, this study identified four downregulated genes (ABLIM3, TREML1, VCL, and AVPR1A) and two upregulated genes (BHLHA15 and FZD8), which exhibited significant alterations in expression levels, with some showing robust discriminatory power, as evidenced by high Area Under the Curve (AUC) values in ROC analysis for predicting patients without CV risks. Conclusion This study identified a panel of genes (including a miRNA and an unknown gene) in the plasma that may serve as promising biomarkers for predicting arrhythmia risk in ESRD patients. These findings provide a foundation for future validation studies aimed at integrating plasma biomarkers into clinical practice to improve risk stratification and management of CV in patients with ESRD. Biological sciences/Computational biology and bioinformatics Health sciences/Biomarkers End-stage renal disease ESRD Arrhythmias circRNA Gene Expression Omnibus Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Arrhythmias pose a significant health burden for patients with end-stage renal disease (ESRD), contributing to increased morbidity and mortality rates 1 . Despite advances in dialysis techniques and supportive care, the incidence of arrhythmias remains high in this population, highlighting the need for effective predictive tools and targeted interventions 2 . Current studies have primarily focused on traditional risk factors such as electrolyte imbalance, inflammation, and autonomic dysfunction 3 . However, recent advancements in molecular biology have opened new avenues for exploring genetic markers that may predict arrhythmia risk in ESRD patients 4 , 5 . Genetic associations with arrhythmias in ESRD have been explored to varying degrees, and several studies have identified polymorphisms in related genes 6 , 7 . Recent studies have emphasized the importance of including diverse populations in genomic investigations to clarify the genetic landscape of cardiac arrhythmias and reduce health disparities in genomic medicine 8 . Other studies have identified genes related to ion channels and cardiac conduction pathways and highlighted gene regions associated with ion channel function, as well as cardiac development and sarcomere as important potential effectors of supraventricular tachycardia susceptibility 9 . Therefore, cardiac arrhythmia may be closely associated with genetic polymorphisms. However, the association between polymorphisms and arrhythmias remains unclear. Although single-nucleotide polymorphisms (SNPs) have been implicated, the role of non-coding RNAs, particularly circular RNAs (circRNAs), remains largely underexplored 10 , 11 . CircRNAs, a class of stable, covalently closed RNA molecules, have emerged as potential biomarkers for various diseases owing to their unique properties and stability in bodily fluids 12 , 13 . Given their involvement in gene regulation and cellular processes, understanding the role of circRNAs in arrhythmia risk could provide novel insights into disease mechanisms and offer new targets for therapeutic interventions. Identifying plasma biomarkers for arrhythmia risk in ESRD patients is of considerable significance. Plasma biomarkers offer a minimally invasive approach for patient monitoring and can potentially enable early detection and personalized treatment strategies 14 . Recent studies have shown that circulating circRNAs exhibit differential expression patterns under various cardiovascular conditions, suggesting their potential as diagnostic and prognostic markers 15 . By leveraging comprehensive bioinformatics analysis, including Gene Expression Omnibus (GEO) datasets, Gene Ontology (GO) analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis 16 , this study aimed to identify novel circRNA biomarkers that could predict arrhythmia risk in ESRD patients. Such biomarkers could enhance clinical decision making and improve patient outcomes by facilitating timely intervention and personalized medicine approaches. In this study, we characterized the plasma-specific transcriptome of patients with chronic kidney disease (CKD) and end-stage renal disease (ESRD) using RNA sequencing. Subsequently, integrated bioinformatics analysis was conducted to examine molecular alterations in plasma. Our findings revealed several candidate genes and pathways linked to non-cardiac conditions in patients with ESRD, and the differential expression of these genes was validated. Materials and methods Sample collection, data processing and RNA screening Three Gene Expression Omnibus (GEO) datasets, GSE133420 (including five normal subjects and five patients with atrial fibrillation) and GSE97709 (including eight healthy controls, nine ESRD with cardiovascular disease (CV) events, seven ESRD, and 10 ESRD without CV events) 17 were downloaded from the GEO website ( https://www.ncbi.nlm.nih.gov/geo/ ). GSE133420 was used as an external validation dataset. R packages were used to identify differences between the normal health groups and ESRD patients with or without CV. The differential screening parameter was set as p < 0.05, false discovery rate (FDR) 2 to obtain the differentially expressed genes (DEGs) for further analysis. Heatmaps, volcano plots, and boxplot charts were plotted using “heatmap” and “ggplot2” R packages. Functional enrichment analysis of CVP genes The GO and KEGG pathway enrichment analyses were conducted using the “enrichplot” R package. Cell composition, biological processes, and molecular functions were included in GO analysis. Protein-protein interaction (PPI) network construction and key genes identification To investigate the interactions among the DEGs and identify potential key genes, we established a PPI network using data from the STRING database 18 . PPI network construction was performed using a minimum required interaction score with high confidence (0.9). Differential expression validation of candidate genes and receiver operator characteristic (ROC) analysis To confirm the differential expression of the candidate key genes, we evaluated the expression levels of the top 20 regulated CVP genes. The validation was performed using external datasets. We drew the ROC curve of the expression levels of autophagy-related genes in DM using the “pROC” package. Statistical analysis All bioinformatic and Pearson’s correlation analyses were performed using R software ( http://www.R-project.org ). The ROC curve was conducted with “glmnet” package in R software to estimate the specificity and sensitivity of potential biomarkers. A p value less than .05 was considered at significant. Results Identification of differential expression genes (DEGs) associated with ESRD patients with (ESRD_CV) or without (ESRD) CV. To identify DEGs, we first identified the differentially expressed genes (DEGs) in GEO datasets (GSE97709). There were 5,705 DEGs between healthy controls (HC) and ESRD_CV (Fig. 1 A), and 4,029 DEGs between healthy controls (HC) and ESRD (Fig. 1 B) that met the criteria based on a difference multiple of |Fold Change| ≥ 2 and p-value < .05. Figure 2 shows the upregulated and downregulated DEGs in these groups (Fig. 2 ). Through Venn diagram analysis, unique DEGs with no CV events were identified. As shown in Fig. 3 A, there were 429 DEGs in the ESRD group, of which 97 were upregulated and 335 were downregulated (Fig. 3 B). CVP genes deviation, PPI network and identification of hub genes As shown in Fig. 4 A, the top 20 genes were identified (Fig. 4 A). The PPI network was built using the STRING software. Figure 5 B showed the PPI network, after removing genes that were not associated with others, three upregulated genes (VCL, POLE2, and AVPR1A) were among the top 20 regulated genes with a confidence level of 0.9 (Fig. 4 B). Among all network genes, ITGA2B had the most nodes (Fig. 4 C). GO analysis of differently expressed CVP genes Through GO enrichment analysis of differentially expressed CVP genes, we identified several biological functions, the CVP genes were highly enriched in wound healing, regulation of body fluid levels, and response to radiation in the BP group. In CC analysis, these genes were highly enriched in the secretory granule membrane, secretory granule lumen, and cytoplasmic vesicle lumen. Moreover, MF analysis indicated significant enrichment in GTPase activity, ubiquitin-like protein ligase binding, and guanyl nucleotide binding (Fig. 5 ). Results of the KEGG pathway analysis for CVP genes KEGG pathway enrichment analysis revealed that these differentially expressed CVP genes were significantly enriched in platelet activation, Kaposi sarcoma-associated herpesvirus infection, shigellosis, colorectal cancer, and Pathogenic Escherichia coli infection (Fig. 6 A). KEGG pathway cluster analysis validated the enrichment results (Fig. 6 B). Evaluation of 4 down- and up-regulated CVP genes in predicting patients with no CV risks ROC curves were used to assess the expression levels of CVP genes in plasma to demonstrate the diagnostic usefulness of CV risks. For the downregulated group, the area under the curve (AUC) values for patients with no CV events when compared with patients with ESRD with CV events and healthy controls ranged from 0.792 to 0.812 (p < .01), as shown in Fig. 7 . Figure 8 exhibited that for the up-regulated CVP genes, the area under the curve (AUC) values for patients with no CV events when compared with patients of ESRD with CV event and healthy controls were from 0.807 to 0.87 (p < .001) (Fig. 8 ). Discussion Ideal cardiovascular health has been associated with reduced CV risk and mortality; however, its association with genetic variations remains unclear. This study sheds light on the potential utility of a panel of DEGs as biomarkers for predicting CV risk in patients with ESRD. By leveraging bioinformatics tools and databases, we identified specific genes and pathways that warrant further investigation for their roles in CV protection and disease progression. Previous studies concluded that gene regions associated with ion channel function, cardiac development, and sarcomeres are important potential effectors of supraventricular tachycardia susceptibility 9 . Therefore, the genetic determinants and molecular mechanisms of CV are being gradually revealed in the progression of heart disease. The rate of removal of body fluid significantly correlated with prolongation of the total filtered P-wave duration (PWD), which is a predictor of arrhythmias 19 . Our GO analysis revealed that the identified CVP genes were significantly enriched in the biological processes regulating body fluid levels. Body fluid overload is a common complication in patients with chronic kidney disease (CKD) and ESRD 20 . Studies have revealed that the occurrence of CV is independently associated with fluid overload, which is common among ESRD patients 21 . It can exacerbate heart failure and increase the risk of arrhythmia. Genes that regulate fluid excretion and reabsorption can help prevent fluid overload, thereby reducing the risk of these complications 22 . Moreover, genes that enhance renal function by promoting diuresis or inhibiting fluid reabsorption can improve overall health of the cardiovascular system. Efficient kidney function ensures that excess fluids are removed, thereby reducing the workload on the heart and preventing fluid accumulation. Active systemic inflammation management might have favorable effects in reducing the arrhythmia burden 23 . Studies have also found that inflammatory activation is increasingly recognized as a non-conventional risk factor for arrhythmias 24 . Bi et al. explored the mechanistic links between inflammatory cytokine-induced molecular and cellular influences and inflammation-associated ventricular arrhythmias 25 . Our GO analysis revealed that CVP genes were also enriched in wound healing and response to radiation. These processes are involved in pro-inflammatory microparticles, pro-inflammatory cytokines, and oxidative stress, which may simultaneously induce and aggravate recurrent CV 26 . Additionally, cellular component analysis highlighted enrichment in the secretory granule membrane lumen, suggesting a role in extracellular signaling and transport. Molecular function (MF) enrichment in GTPase activity, ubiquitin-like protein ligase binding, and guanyl nucleotide binding indicates potential regulatory functions that could influence CV 27 , 28 . KEGG pathway enrichment analysis identified significant pathways, such as platelet activation, which play a crucial role in thrombotic events 29 and atherosclerosis 30 . For example, platelet aggregation and activation are pivotal in the development of acute coronary syndromes 31 , 32 . Furthermore, the involvement of pathways related to infections (e.g., Kaposi sarcoma-associated herpesvirus, shigellosis, and pathogenic Escherichia coli) underscores the importance of immune responses in CV disease progression 33 . Genes identified in our study, such as ABLIM3, TREML1, VCL, AVPR1A, BHLHA15, and FZD8, have been implicated in various CV conditions. For example, ABLIM3, which is involved in the cell adhesion molecules pathway 34 , has been potentially linked to myocardial function. VCL, a gene encoding vinculin, is essential for cell adhesion and migration, and plays a role in the development of hypertrophic cardiomyopathy 35 . The upregulation of FZD8, a receptor involved in the Wnt/β-catenin signaling pathway, could indicate a compensatory mechanism to promote cell survival and repair 36 . Furthermore, we identified upregulated miRNA-4683 and an unknown gene, LOC105372997. The upregulation of miRNA-4683 and LOC105372997 may be associated with the absence of CV in ESRD patients, and more studies are needed to confirm the relevant mechanism of these two genes in protecting against the non-occurrence of CV in ESD patients. Understanding the molecular mechanisms underlying these interactions provides valuable insights into the pathophysiology of CV risk in ESRD patients. Our findings could facilitate the development of targeted therapies and personalized medical approaches aimed at reducing CV complications. While our study provides a strong foundation for understanding the potential mechanisms of non-CV in patients with ESRD, it is important to acknowledge the limitations inherent in a purely bioinformatics approach. The lack of experimental validation and the potential for false positives owing to the complexity of biological systems are significant considerations. Future studies should focus on validating these findings through wet lab experiments and clinical trials to confirm the clinical utility of these biomarkers. Declarations Conflict of Interests The authors declare no conflicts of interest regarding the publication of this paper. Funding statement None. Author Contribution Fangfang Lu conceptualized, designed, and supervised the study. Fangfang Lu collected the data and performed the data analysis. Fangfang Lu wrote and revised the manuscript. Fangfang Lu was responsible for recruiting patients, collecting the samples, and recording clinical data. The author read and approved the fnal manuscript. Acknowledgments The authors would like to thank the GEO Project for its valuable contributions to cancer research. Data Availability The data presented in this study are openly available in the GEO database (https://www.ncbi.nlm.nih.gov/geo/). References Czifra, A. et al. [End stage renal disease and ventricular arrhythmia. 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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-5292194","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":377139027,"identity":"4c25a36e-1258-40cd-9ee0-6653111dc39f","order_by":0,"name":"Fangfang Lu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuElEQVRIiWNgGAWjYBAC9ubDDQwJFRJy/MzMhx8QpYXnWGIDw4MzFsaS7WxpBkRrYXzYVpG44TyPggRxWtgY2yQS2CQYNx/mYTBgqLGJJlILjwSz2WHeAw8YjqXlNhDSYi/fCNQiIcFmdpgvwYCx4TBhLRBbDCR4jJt5DCRI0AK0xoCZBC3NFgkHJAwkDgMDOYEYv/CwMR+8+fNfXX1//+HDDz7U2BDWggoSSFM+CkbBKBgFowAXAAB3nDlTCLkNowAAAABJRU5ErkJggg==","orcid":"","institution":"People's Hospital of Linquan County","correspondingAuthor":true,"prefix":"","firstName":"Fangfang","middleName":"","lastName":"Lu","suffix":""}],"badges":[],"createdAt":"2024-10-19 03:08:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5292194/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5292194/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70044041,"identity":"3d9b156a-20f8-428e-b540-952b20a85dca","added_by":"auto","created_at":"2024-11-27 18:46:02","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4176385,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of differential expression genes (DEGs) associated with ESRD patients with (ESRD_CV) or without (ESRD) CV.\u003c/strong\u003e(A) Heatmap of DEGs between Healthy Control (HC) and ESRD_CV groups; (B) Heatmap of DEGs between Healthy Control (HC) and ESRD groups; CV: cardiovascular diseases.\u003c/p\u003e","description":"","filename":"Figure1heatmap.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5292194/v1/d6e1e4ca5a4e2f67cebad17e.jpg"},{"id":70044580,"identity":"79943038-8aac-495d-bf05-dc136b25e571","added_by":"auto","created_at":"2024-11-27 18:54:01","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1632979,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVolcano plots of DEGs associated with ESRD_CV and ESRD.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Volcano plot of DEGs between HC and ESRD_CV; (B)Volcano plot of DEGs between HC and ESRD. Significantly (FDR \u0026lt; 0.05) differentially expressed genes (|fold change| \u0026gt; 2) are indicated in red (upregulated), or green (downregulated).\u003c/p\u003e","description":"","filename":"Figure2vol.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5292194/v1/f1f7a567418656c9374fb4e8.jpg"},{"id":70044582,"identity":"d70c7960-1e3d-4798-9ae8-bdef84b225ee","added_by":"auto","created_at":"2024-11-27 18:54:02","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1108977,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of potential protective genes linked to CV (CVP).\u003c/strong\u003e (A) Venn diagram of unique genes in ESRD group without CV event. (2) The vocano plot of CVP, Significantly (FDR \u0026lt; 0.05) differentially expressed genes (|fold change| \u0026gt; 2) are indicated in red (upregulated), or green (downregulated).\u003c/p\u003e","description":"","filename":"Figure3venn.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5292194/v1/4d4a659e970e76398147219e.jpg"},{"id":70044037,"identity":"60b23b47-6c72-426b-86db-37114812454c","added_by":"auto","created_at":"2024-11-27 18:46:01","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2117988,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRegulations and PPI network of these 429 unique CVP genes. \u003c/strong\u003e(A) Bar plot of the top 20 up- and down-regulated genes; (B) PPI network of the DEGs with the highest confidence of 0.9; (C) Bar plot of adjacent nodes genes. Common genes within the top 20 up- or down-regulated genes in the PPI network are featured in red and bold.\u003c/p\u003e","description":"","filename":"Figure4429uniquegeneDeviation.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5292194/v1/7d5413b1c2b1e714043bea85.jpg"},{"id":70044038,"identity":"1ab61b12-c1fa-4738-acf4-1a7bf588c831","added_by":"auto","created_at":"2024-11-27 18:46:01","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2449991,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGO analysis of unique CVP genes.\u003c/strong\u003e(A) Bubble plot shows that these genes are enriched in several biological processes (BP), cell components (CC), molecular functions (MF); (B) Bubble plot shows that these unique genes are enriched in each term\u003c/p\u003e","description":"","filename":"Figure5429go.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5292194/v1/15c339591a696d4ee465784f.jpg"},{"id":70044044,"identity":"25ce625f-9896-4d26-8aed-c8f4a61555af","added_by":"auto","created_at":"2024-11-27 18:46:02","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3077897,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResults of the KEGG pathway and KEGG cluster analysis for these unique 429 CVP genes. \u003c/strong\u003e(A)The circle plot of KEGG pathway; (B) The circle plot of KEGG clusters show the top 5 cluster of these genes.\u003c/p\u003e","description":"","filename":"Figure6429KEGGcircos.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5292194/v1/98fed3710f7ba26731b77630.jpg"},{"id":70044804,"identity":"2dcc8f38-e772-486d-b5b7-52225e13664a","added_by":"auto","created_at":"2024-11-27 19:02:02","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2158509,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC for diagnostic power analysis of four down-regulated biomarkers for predicting patients without CV event. \u003c/strong\u003e(A)ABLIM3, (B)TREML1; (C) VCL; and (D) AVPR1A.\u003c/p\u003e","description":"","filename":"Figure7downregulate.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5292194/v1/8eeab17c9ed7acade66c691e.jpg"},{"id":70045099,"identity":"36ccb928-4af1-4fa5-8b10-864283726a2a","added_by":"auto","created_at":"2024-11-27 19:10:02","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":2147013,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC for diagnostic power analysis of four up-regulated biomarkers for predicting patients without CV event. \u003c/strong\u003e(A)BHLHA15, (B)FZD8; (C)MIR4683; and (D) LOC105372997.\u003c/p\u003e","description":"","filename":"Figure8upregulate.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5292194/v1/d398cba41015d1d7d35d0450.jpg"},{"id":70045232,"identity":"a010cd22-ad83-45a2-b56e-026374768e1d","added_by":"auto","created_at":"2024-11-27 19:18:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":19536788,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5292194/v1/926fe0c9-99a3-4c52-a753-3ae6d0f7abc1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comprehensive bioinformatics analysis identifies biomarkers for cardiovascular risk in end-stage renal diseases","fulltext":[{"header":"Introduction","content":"\u003cp\u003eArrhythmias pose a significant health burden for patients with end-stage renal disease (ESRD), contributing to increased morbidity and mortality rates\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Despite advances in dialysis techniques and supportive care, the incidence of arrhythmias remains high in this population, highlighting the need for effective predictive tools and targeted interventions\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Current studies have primarily focused on traditional risk factors such as electrolyte imbalance, inflammation, and autonomic dysfunction\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. However, recent advancements in molecular biology have opened new avenues for exploring genetic markers that may predict arrhythmia risk in ESRD patients\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGenetic associations with arrhythmias in ESRD have been explored to varying degrees, and several studies have identified polymorphisms in related genes\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Recent studies have emphasized the importance of including diverse populations in genomic investigations to clarify the genetic landscape of cardiac arrhythmias and reduce health disparities in genomic medicine\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Other studies have identified genes related to ion channels and cardiac conduction pathways and highlighted gene regions associated with ion channel function, as well as cardiac development and sarcomere as important potential effectors of supraventricular tachycardia susceptibility\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Therefore, cardiac arrhythmia may be closely associated with genetic polymorphisms. However, the association between polymorphisms and arrhythmias remains unclear.\u003c/p\u003e \u003cp\u003eAlthough single-nucleotide polymorphisms (SNPs) have been implicated, the role of non-coding RNAs, particularly circular RNAs (circRNAs), remains largely underexplored\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. CircRNAs, a class of stable, covalently closed RNA molecules, have emerged as potential biomarkers for various diseases owing to their unique properties and stability in bodily fluids\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Given their involvement in gene regulation and cellular processes, understanding the role of circRNAs in arrhythmia risk could provide novel insights into disease mechanisms and offer new targets for therapeutic interventions.\u003c/p\u003e \u003cp\u003eIdentifying plasma biomarkers for arrhythmia risk in ESRD patients is of considerable significance. Plasma biomarkers offer a minimally invasive approach for patient monitoring and can potentially enable early detection and personalized treatment strategies\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Recent studies have shown that circulating circRNAs exhibit differential expression patterns under various cardiovascular conditions, suggesting their potential as diagnostic and prognostic markers\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBy leveraging comprehensive bioinformatics analysis, including Gene Expression Omnibus (GEO) datasets, Gene Ontology (GO) analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, this study aimed to identify novel circRNA biomarkers that could predict arrhythmia risk in ESRD patients. Such biomarkers could enhance clinical decision making and improve patient outcomes by facilitating timely intervention and personalized medicine approaches.\u003c/p\u003e \u003cp\u003eIn this study, we characterized the plasma-specific transcriptome of patients with chronic kidney disease (CKD) and end-stage renal disease (ESRD) using RNA sequencing. Subsequently, integrated bioinformatics analysis was conducted to examine molecular alterations in plasma. Our findings revealed several candidate genes and pathways linked to non-cardiac conditions in patients with ESRD, and the differential expression of these genes was validated.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSample collection, data processing and RNA screening\u003c/h2\u003e \u003cp\u003eThree Gene Expression Omnibus (GEO) datasets, GSE133420 (including five normal subjects and five patients with atrial fibrillation) and GSE97709 (including eight healthy controls, nine ESRD with cardiovascular disease (CV) events, seven ESRD, and 10 ESRD without CV events)\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e were downloaded from the GEO website (\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). GSE133420 was used as an external validation dataset.\u003c/p\u003e \u003cp\u003eR packages were used to identify differences between the normal health groups and ESRD patients with or without CV. The differential screening parameter was set as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and fold change (FC)\u0026thinsp;\u0026gt;\u0026thinsp;2 to obtain the differentially expressed genes (DEGs) for further analysis. Heatmaps, volcano plots, and boxplot charts were plotted using \u0026ldquo;heatmap\u0026rdquo; and \u0026ldquo;ggplot2\u0026rdquo; R packages.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFunctional enrichment analysis of CVP genes\u003c/h3\u003e\n\u003cp\u003eThe GO and KEGG pathway enrichment analyses were conducted using the \u0026ldquo;enrichplot\u0026rdquo; R package. Cell composition, biological processes, and molecular functions were included in GO analysis.\u003c/p\u003e\n\u003ch3\u003eProtein-protein interaction (PPI) network construction and key genes identification\u003c/h3\u003e\n\u003cp\u003eTo investigate the interactions among the DEGs and identify potential key genes, we established a PPI network using data from the STRING database\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. PPI network construction was performed using a minimum required interaction score with high confidence (0.9).\u003c/p\u003e\n\u003cdiv class=\"Heading\"\u003e\u003cb\u003eDifferential expression validation of candidate genes and receiver operator characteristic (ROC) analysis\u003c/b\u003e\u003c/div\u003e \u003cp\u003eTo confirm the differential expression of the candidate key genes, we evaluated the expression levels of the top 20 regulated CVP genes. The validation was performed using external datasets. We drew the ROC curve of the expression levels of autophagy-related genes in DM using the \u0026ldquo;pROC\u0026rdquo; package.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll bioinformatic and Pearson\u0026rsquo;s correlation analyses were performed using R software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.R-project.org\u003c/span\u003e\u003cspan address=\"http://www.R-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The ROC curve was conducted with \u0026ldquo;glmnet\u0026rdquo; package in R software to estimate the specificity and sensitivity of potential biomarkers. A p value less than .05 was considered at significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eIdentification of differential expression genes (DEGs) associated with ESRD patients with (ESRD_CV) or without (ESRD) CV.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo identify DEGs, we first identified the differentially expressed genes (DEGs) in GEO datasets (GSE97709). There were 5,705 DEGs between healthy controls (HC) and ESRD_CV (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), and 4,029 DEGs between healthy controls (HC) and ESRD (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB) that met the criteria based on a difference multiple of |Fold Change| \u0026ge; 2 and p-value\u0026thinsp;\u0026lt;\u0026thinsp;.05. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the upregulated and downregulated DEGs in these groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThrough Venn diagram analysis, unique DEGs with no CV events were identified. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, there were 429 DEGs in the ESRD group, of which 97 were upregulated and 335 were downregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eCVP genes deviation, PPI network and identification of hub genes\u003c/h3\u003e\n\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, the top 20 genes were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The PPI network was built using the STRING software. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB showed the PPI network, after removing genes that were not associated with others, three upregulated genes (VCL, POLE2, and AVPR1A) were among the top 20 regulated genes with a confidence level of 0.9 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Among all network genes, ITGA2B had the most nodes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eGO analysis of differently expressed CVP genes\u003c/h3\u003e\n\u003cp\u003eThrough GO enrichment analysis of differentially expressed CVP genes, we identified several biological functions, the CVP genes were highly enriched in wound healing, regulation of body fluid levels, and response to radiation in the BP group. In CC analysis, these genes were highly enriched in the secretory granule membrane, secretory granule lumen, and cytoplasmic vesicle lumen. Moreover, MF analysis indicated significant enrichment in GTPase activity, ubiquitin-like protein ligase binding, and guanyl nucleotide binding (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eResults of the KEGG pathway analysis for CVP genes\u003c/h2\u003e \u003cp\u003eKEGG pathway enrichment analysis revealed that these differentially expressed CVP genes were significantly enriched in platelet activation, Kaposi sarcoma-associated herpesvirus infection, shigellosis, colorectal cancer, and Pathogenic Escherichia coli infection (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). KEGG pathway cluster analysis validated the enrichment results (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of 4 down- and up-regulated CVP genes in predicting patients with no CV risks\u003c/h2\u003e \u003cp\u003eROC curves were used to assess the expression levels of CVP genes in plasma to demonstrate the diagnostic usefulness of CV risks. For the downregulated group, the area under the curve (AUC) values for patients with no CV events when compared with patients with ESRD with CV events and healthy controls ranged from 0.792 to 0.812 (p\u0026thinsp;\u0026lt;\u0026thinsp;.01), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e exhibited that for the up-regulated CVP genes, the area under the curve (AUC) values for patients with no CV events when compared with patients of ESRD with CV event and healthy controls were from 0.807 to 0.87 (p\u0026thinsp;\u0026lt;\u0026thinsp;.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIdeal cardiovascular health has been associated with reduced CV risk and mortality; however, its association with genetic variations remains unclear. This study sheds light on the potential utility of a panel of DEGs as biomarkers for predicting CV risk in patients with ESRD. By leveraging bioinformatics tools and databases, we identified specific genes and pathways that warrant further investigation for their roles in CV protection and disease progression.\u003c/p\u003e \u003cp\u003ePrevious studies concluded that gene regions associated with ion channel function, cardiac development, and sarcomeres are important potential effectors of supraventricular tachycardia susceptibility\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Therefore, the genetic determinants and molecular mechanisms of CV are being gradually revealed in the progression of heart disease. The rate of removal of body fluid significantly correlated with prolongation of the total filtered P-wave duration (PWD), which is a predictor of arrhythmias\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Our GO analysis revealed that the identified CVP genes were significantly enriched in the biological processes regulating body fluid levels.\u003c/p\u003e \u003cp\u003eBody fluid overload is a common complication in patients with chronic kidney disease (CKD) and ESRD \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Studies have revealed that the occurrence of CV is independently associated with fluid overload, which is common among ESRD patients\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. It can exacerbate heart failure and increase the risk of arrhythmia. Genes that regulate fluid excretion and reabsorption can help prevent fluid overload, thereby reducing the risk of these complications\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Moreover, genes that enhance renal function by promoting diuresis or inhibiting fluid reabsorption can improve overall health of the cardiovascular system. Efficient kidney function ensures that excess fluids are removed, thereby reducing the workload on the heart and preventing fluid accumulation.\u003c/p\u003e \u003cp\u003eActive systemic inflammation management might have favorable effects in reducing the arrhythmia burden\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Studies have also found that inflammatory activation is increasingly recognized as a non-conventional risk factor for arrhythmias\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Bi et al. explored the mechanistic links between inflammatory cytokine-induced molecular and cellular influences and inflammation-associated ventricular arrhythmias\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Our GO analysis revealed that CVP genes were also enriched in wound healing and response to radiation. These processes are involved in pro-inflammatory microparticles, pro-inflammatory cytokines, and oxidative stress, which may simultaneously induce and aggravate recurrent CV\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Additionally, cellular component analysis highlighted enrichment in the secretory granule membrane lumen, suggesting a role in extracellular signaling and transport. Molecular function (MF) enrichment in GTPase activity, ubiquitin-like protein ligase binding, and guanyl nucleotide binding indicates potential regulatory functions that could influence CV\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eKEGG pathway enrichment analysis identified significant pathways, such as platelet activation, which play a crucial role in thrombotic events\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e and atherosclerosis\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. For example, platelet aggregation and activation are pivotal in the development of acute coronary syndromes\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Furthermore, the involvement of pathways related to infections (e.g., Kaposi sarcoma-associated herpesvirus, shigellosis, and pathogenic Escherichia coli) underscores the importance of immune responses in CV disease progression\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGenes identified in our study, such as ABLIM3, TREML1, VCL, AVPR1A, BHLHA15, and FZD8, have been implicated in various CV conditions. For example, ABLIM3, which is involved in the cell adhesion molecules pathway\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, has been potentially linked to myocardial function. VCL, a gene encoding vinculin, is essential for cell adhesion and migration, and plays a role in the development of hypertrophic cardiomyopathy\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. The upregulation of FZD8, a receptor involved in the Wnt/β-catenin signaling pathway, could indicate a compensatory mechanism to promote cell survival and repair\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFurthermore, we identified upregulated miRNA-4683 and an unknown gene, LOC105372997. The upregulation of miRNA-4683 and LOC105372997 may be associated with the absence of CV in ESRD patients, and more studies are needed to confirm the relevant mechanism of these two genes in protecting against the non-occurrence of CV in ESD patients. Understanding the molecular mechanisms underlying these interactions provides valuable insights into the pathophysiology of CV risk in ESRD patients. Our findings could facilitate the development of targeted therapies and personalized medical approaches aimed at reducing CV complications.\u003c/p\u003e \u003cp\u003eWhile our study provides a strong foundation for understanding the potential mechanisms of non-CV in patients with ESRD, it is important to acknowledge the limitations inherent in a purely bioinformatics approach. The lack of experimental validation and the potential for false positives owing to the complexity of biological systems are significant considerations. Future studies should focus on validating these findings through wet lab experiments and clinical trials to confirm the clinical utility of these biomarkers.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflict of Interests\u003c/h2\u003e \u003cp\u003eThe authors declare no conflicts of interest regarding the publication of this paper.\u003c/p\u003e\u003ch2\u003eFunding statement\u003c/h2\u003e \u003cp\u003eNone.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eFangfang Lu conceptualized, designed, and supervised the study. Fangfang Lu collected the data and performed the data analysis. Fangfang Lu wrote and revised the manuscript. Fangfang Lu was responsible for recruiting patients, collecting the samples, and recording clinical data. The author read and approved the fnal manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThe authors would like to thank the GEO Project for its valuable contributions to cancer research.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data presented in this study are openly available in the GEO database (https://www.ncbi.nlm.nih.gov/geo/).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCzifra, A. et al. [End stage renal disease and ventricular arrhythmia. Hemodialysis and hemodiafiltration differently affect ventricular repolarization]. \u003cem\u003eOrv Hetil\u003c/em\u003e. \u003cb\u003e156\u003c/b\u003e (12), 463\u0026ndash;471 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaraboyas, A. et al. 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Macromol.\u003c/em\u003e \u003cb\u003e254\u003c/b\u003e (Pt 2), 127846 (2024).\u003c/span\u003e\u003c/li\u003e\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":"End-stage renal disease, ESRD, Arrhythmias, circRNA, Gene Expression Omnibus","lastPublishedDoi":"10.21203/rs.3.rs-5292194/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5292194/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eEnd-stage renal disease (ESRD) can increase the risk of cardiovascular disease (CV). We aimed to investigate the pathways and mechanisms associated with potential protective genes linked to CV (CVP).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a systematic bioinformatics analysis using publicly available datasets from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were identified in patients with ESRD with and without arrhythmia using stringent statistical criteria. Functional enrichment analyses were performed using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways to elucidate the biological roles of these DEGs. Receiver Operating Characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of the identified biomarkers for CV risk prediction.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOur analysis revealed a distinct set of DEGs in ESRD patients with arrhythmia compared to those without arrhythmia. GO and KEGG pathway analyses indicated that these DEGs were involved in key biological processes and pathways relevant to cardiovascular disorders and renal function, including wound healing, platelet activation, and fluid-level regulation. Moreover, this study identified four downregulated genes (ABLIM3, TREML1, VCL, and AVPR1A) and two upregulated genes (BHLHA15 and FZD8), which exhibited significant alterations in expression levels, with some showing robust discriminatory power, as evidenced by high Area Under the Curve (AUC) values in ROC analysis for predicting patients without CV risks.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study identified a panel of genes (including a miRNA and an unknown gene) in the plasma that may serve as promising biomarkers for predicting arrhythmia risk in ESRD patients. These findings provide a foundation for future validation studies aimed at integrating plasma biomarkers into clinical practice to improve risk stratification and management of CV in patients with ESRD.\u003c/p\u003e","manuscriptTitle":"Comprehensive bioinformatics analysis identifies biomarkers for cardiovascular risk in end-stage renal diseases","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-27 18:45:57","doi":"10.21203/rs.3.rs-5292194/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":"1ef73e83-6b30-48ed-9bec-f65d67be70ef","owner":[],"postedDate":"November 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":40147317,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":40147318,"name":"Health sciences/Biomarkers"}],"tags":[],"updatedAt":"2024-11-27T18:45:59+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-27 18:45:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5292194","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5292194","identity":"rs-5292194","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}