RNA sequencing and bioinformatics analysis of tissue biopsy of abdominal fat in obesity associated with cardio-metabolic diseases

RNA sequencing and bioinformatics analysis of tissue biopsy of abdominal fat in obesity associated with cardio-metabolic 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 Research Article RNA sequencing and bioinformatics analysis of tissue biopsy of abdominal fat in obesity associated with cardio-metabolic diseases Basavaraj Mallikarjunayya Vastrad, Chanabasayya Vastrad This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8076420/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 Obesity associated with cardiometabolic diseases is a major metabolic disorder and a significant global health issue. However, the specific molecular mechanisms of obesity associated with cardiometabolic diseases remain unclear. This study aims to identify key genes and signaling pathways associated with obesity using bioinformatics. Next generation sequencing (NGS) dataset (GSE244118) including those from 39 obesity volunteers and 15 lean volunteers was downloaded from the Gene Expression Omnibus (GEO) database and the differentially expressed genes (DEGs) were screened using DESeq2. To better understand the functions and possible pathways of DEGs, we performed Gene Ontology (GO) and REACTOME pathway enrichment analysis. Protein-protein interaction (PPI) network and module analyses were performed based on the DEGs. MiRNA-hub gene regulatory network, TF-hub gene regulatory network and drug-hub gene interaction network were built by Cytoscape to predict the underlying microRNAs (miRNAs), transcription factors (TFs) and drug molecules associated with hub genes. The receiver operating characteristic (ROC) analyses were conducted to explore the value of hub genes for obesity diagnosis. GO and REACTOME pathway enrichment results showed that these genes were closely associated with multicellular organismal process, immune system process, Metabolism of water-soluble vitamins and cofactors and immune system. Hub genes (ESR1, MET, FKBP5, RPL9, MAP3K5, HTRA4, C3AR1, CEP55, TAFA3 and LAMP3), miRNAs (hsa-mir-30c-2-3p, hsa-miR-3149, hsa-miR-3119 and hsa-mir-449a) and TFs (TEAD1, BRCA1, SOX5 and RUNX2) were ultimately determined as common diagnostic markers for obesity associated with cardiometabolic diseases. Drug molecules (Methotrimeprazine, Dexfenfluramine, Clobazam and Eluxadoline) were predicted for treatment of obesity associated with cardiometabolic disease. ROC curve analysis also showed good diagnostic performance. After a series of bioinformatics analysis and validation, ESR1, MET, FKBP5, RPL9, MAP3K5, HTRA4, C3AR1, CEP55, TAFA3 and LAMP3 were identified as hub genes for the development of OA and AS. This study provides a new perspective on the common molecular mechanisms between OA and AS, and offers new insights into the potential pathogenesis obesity associated with cardiometabolic diseases and the direction of treatment. Bioinformatics Computational Biology Obesity associated with cardiometabolic diseases Bioinformatics analysis Molecular mechanisms Biomarker Differentially expressed genes Full Text Additional Declarations The authors declare no competing interests. Table 1 to 7 are available in the Supplementary Files section. Supplementary Files TablesF.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-8076420","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":542633245,"identity":"d9a91591-46d8-463e-b4ac-1269960e7874","order_by":0,"name":"Basavaraj Mallikarjunayya Vastrad","email":"","orcid":"https://orcid.org/0000-0003-2202-7637","institution":"Department of Pharmaceutical Chemistry, K.L.E. 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The receiver operating characteristic (ROC) analyses were conducted to explore the value of hub genes for obesity diagnosis. GO and REACTOME pathway enrichment results showed that these genes were closely associated with multicellular organismal process, immune system process, Metabolism of water-soluble vitamins and cofactors and immune system. Hub genes (ESR1, MET, FKBP5, RPL9, MAP3K5, HTRA4, C3AR1, CEP55, TAFA3 and LAMP3), miRNAs (hsa-mir-30c-2-3p, hsa-miR-3149, hsa-miR-3119 and hsa-mir-449a) and \u0026nbsp;TFs (TEAD1, BRCA1, SOX5 and RUNX2) were ultimately determined as common diagnostic markers for obesity associated with cardiometabolic diseases. Drug molecules (Methotrimeprazine, Dexfenfluramine, Clobazam and Eluxadoline) were predicted for treatment of obesity associated with cardiometabolic disease. 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