{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T16:53:35Z","timestamp":1793465615242,"version":"4.5.0"},"reference-count":69,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,4,24]],"date-time":"2023-04-24T00:00:00Z","timestamp":1682294400000},"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. Mol. Biosci."],"abstract":"<jats:p><jats:bold>Background:<\/jats:bold> Dilated cardiomyopathy (DCM) is one of the significant causes of heart failure, and the mechanisms of metabolic ventricular remodelling due to disturbances in energy metabolism are still poorly understood in cardiac pathology. Understanding the biological mechanisms of cuproptosis in DCM is critical for drug development.<\/jats:p><jats:p><jats:bold>Methods:<\/jats:bold> The DCM datasets were downloaded from Gene Expression Omnibus, their relationships with cuproptosis-related genes (CRGs) and immune signatures were analyzed. LASSO, RF, and SVM-RFE machine learning algorithms were used to identify signature genes and the eXtreme Gradient Boosting (XGBoost) model was used to assess diagnostic efficacy. Molecular clusters of CRGs were identified, and immune Infiltration analysis was performed. The WGCNA algorithm was used to identify specific genes in different clusters. In addition, AUCell was used to analyse the cuproptosis scores of different cell types in the scRNA-seq dataset. Finally, herbal medicines were predicted from an online database, and molecular docking and molecular dynamics simulations were used to support the confirmation of the potential of the selected compounds.<\/jats:p><jats:p><jats:bold>Results:<\/jats:bold> We identified dysregulated cuproptosis genes and activated immune responses between DCM and healthy controls. Two signature genes (FDX1, SLC31A1) were identified and performed well in an external validation dataset (AUC = 0.846). Two molecular clusters associated with cuproptosis were further defined in DCM, and immune infiltration analysis showed B-cell naive, Eosinophils, NK cells activated and T-cell CD4 memory resting is significant immune heterogeneity in the two clusters. AUCell analysis showed that cardiomyocytes had a high cuproposis score. In addition, 19 and 3 herbal species were predicted based on FDX1 and SLC31A1. Based on the molecular docking model, the natural compounds Rutin with FDX1 (-9.3\u00a0kcal\/mol) and Polydatin with SLC31A1 (-5.5\u00a0kcal\/mol) has high stability and molecular dynamics simulation studies further validated this structural stability.<\/jats:p><jats:p><jats:bold>Conclusion:<\/jats:bold> Our study systematically illustrates the complex relationship between cuproptosis and the pathological features of DCM and identifies two signature genes (FDX1 and SLC31A1) and two natural compounds (Rutin and Polydatin). This may enhance our diagnosis of the disease and facilitate the development of clinical treatment strategies for DCM.<\/jats:p>","DOI":"10.3389\/fmolb.2023.1154920","type":"journal-article","created":{"date-parts":[[2023,4,24]],"date-time":"2023-04-24T04:26:36Z","timestamp":1682310396000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":21,"title":["Identification of cuproptosis-related biomarkers in dilated cardiomyopathy and potential therapeutic prediction of herbal medicines"],"prefix":"10.3389","volume":"10","author":[{"given":"Rutao","family":"Bian","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yakuan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zishuang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuegong","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,4,24]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/s1474-4422(14)70190-5","article-title":"Wilson's disease and other neurological copper disorders","volume":"14","author":"Bandmann","year":"2015","journal-title":"Lancet Neurol."},{"key":"B2","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1007\/s00018-007-7439-6","article-title":"Menkes disease","volume":"65","author":"Bertini","year":"2008","journal-title":"Cell Mol. 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