{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T02:46:27Z","timestamp":1789008387747,"version":"build-2803163510"},"reference-count":35,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,2,8]],"date-time":"2024-02-08T00:00:00Z","timestamp":1707350400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Endocrinol."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Diabetic cardiomyopathy (DCM) lacks specific and sensitive biomarkers, and its diagnosis remains a challenge. Therefore, there is an urgent need to develop useful biomarkers to help diagnose and evaluate the prognosis of DCM. This study aims to find specific diagnostic markers for diabetic cardiomyopathy.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Two datasets (GSE106180 and GSE161827) from the GEO database were integrated to identify differentially expressed genes (DEGs) between control and type 2 diabetic cardiomyopathy. We assessed the infiltration of immune cells and used weighted coexpression network analysis (WGCNA) to construct the gene coexpression network. Then we performed a clustering analysis. Finally, a diagnostic model was built by the least absolute shrinkage and selection operator (LASSO).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A total of 3066 DEGs in the GSE106180 and GSE161827 datasets. There were differences in immune cell infiltration. According to gene significance (GS) &amp;gt; 0.2 and module membership (MM) &amp;gt; 0.8, 41 yellow Module genes and 1474 turquoise Module genes were selected. Hub genes were mainly related to the \u201cproteasomal protein catabolic process\u201d, \u201cmitochondrial matrix\u201d and \u201cprotein processing in endoplasmic reticulum\u201d pathways. LASSO was used to construct a diagnostic model composed of OXCT1, CACNA2D2, BCL7B, EGLN3, GABARAP, and ACADSB and verified it in the GSE163060 and GSE175988 datasets with AUCs of 0.9333 (95% CI: 0.7801-1) and 0.96 (95% CI: 0.8861-1), respectively. H9C2 cells were verified, and the results were similar to the bioinformatics analysis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>We constructed a diagnostic model of DCM, and OXCT1, CACNA2D2, BCL7B, EGLN3, GABARAP, and ACADSB were potential biomarkers, which may provide new insights for improving the ability of early diagnosis and treatment of diabetic cardiomyopathy.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fendo.2024.1185062","type":"journal-article","created":{"date-parts":[[2024,2,8]],"date-time":"2024-02-08T16:40:59Z","timestamp":1707410459000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":20,"title":["Identification of important modules and biomarkers in diabetic cardiomyopathy based on WGCNA and LASSO analysis"],"prefix":"10.3389","volume":"15","author":[{"given":"Min","family":"Cui","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yajuan","family":"An","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liping","family":"Wei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Qi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,2,8]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCRESAHA.117.311586","article-title":"Diabetic cardiomyopathy: an update of mechanisms contributing to this clinical entity","volume":"122","author":"Jia","year":"2018","journal-title":"Circ Res"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1007\/s00125-017-4390-4","article-title":"Diabetic cardiomyopathy: a hyperglycaemia- and insulin-resistance-induced heart disease","volume":"61","author":"Jia","year":"2018","journal-title":"Diabetologia"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1016\/j.hfc.2019.02.003","article-title":"Diabetic cardiomyopathy: definition, diagnosis, and therapeutic implications","volume":"15","author":"Paolillo","year":"2019","journal-title":"Heart Fail Clin"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1007\/s12265-022-10217-0","article-title":"Biological functions and clinical prospects of extracellular non-coding RNAs in diabetic cardiomyopathy: an updated review","volume":"15","author":"Yin","year":"2022","journal-title":"J Cardiovasc Transl Res"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-9-559","article-title":"WGCNA: an R package for weighted correlation network analysis","volume":"9","author":"Langfelder","year":"2008","journal-title":"BMC Bioinf"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2022.832739","article-title":"Comprehensive genetic analysis of tuberculosis and identification of candidate biomarkers","volume":"13","author":"Wen","year":"2022","journal-title":"Front Genet"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the lasso","volume":"58","author":"Robert","year":"1996","journal-title":"R Statist. 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