Construction and evaluation of immune-related diagnostic model in patients with heart failure caused by idiopathic dilated cardiomyopathy.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38321374", "kind": "science-record", "revision": 3, "content_hash": "sha256:659b87858a9b12ba7e675665abc22f66de63074dae61dbf0474fc984955dcef0", "created_at": "2026-11-01T19:20:15Z", "updated_at": "2026-11-01T22:11:12Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Construction and evaluation of immune-related diagnostic model in patients with heart failure caused by idiopathic dilated cardiomyopathy.", "doi": "10.1186/s12872-023-03666-1", "pmid": "38321374", "authors": ["Sichi Xu", "Zhaogui Wu", "Haihua Chen"], "journal": "BMC cardiovascular disorders", "year": 2024, "abstract": "OBJECTIVE: The purpose of the study was to construct the potential diagnostic model of immune-related genes during the development of heart failure caused by idiopathic dilated cardiomyopathy.\nMETHOD: GSE5406 and GSE57338 were downloaded from the GEO website ( https://www.ncbi.nlm.nih.gov/geo/ ). CIBERSORT was used for the evaluation of immune infiltration in idiopathic dilated cardiomyopathy (DCM) of GSE5406. Differently expressed genes were calculated by the limma R package and visualized by the volcano plot. The immune-related genes were downloaded from Immport, TISIDB, and InnateDB. Then the immune-related differential genes (IRDGs) were acquired from the intersection. Protein-protein interaction network (PPI) and Cytoscape were used to visualize the hub genes. Three machine learning methods such as random forest, logical regression, and elastic network regression model were adopted to construct the prediction model. The diagnostic value was also validated in GSE57338.\nRESULTS: Our study demonstrated the obvious different ratio of T cell CD4 memory activated, T cell regulatory Tregs, and neutrophils between DCM and control donors. As many as 2139 differential genes and 274 immune-related different genes were identified. These genes were mainly enriched in lipid and atherosclerosis, human cytomegalovirus infection, and cytokine-cytokine receptor interaction. At the same time, as many as fifteen hub genes were identified as the IRDGs (IFITM3, IFITM2, IFITM1, IFIT3, IFIT1, HLA-A, HLA-B, HLA-C, ADAR, STAT1, SAMHD1, RSAD2, MX1, ISG20, IRF2). Moreover, we also discovered that the elastic network and logistic regression models had a higher diagnostic value than that of random forest models based on these hub genes.\nCONCLUSION: Our study demonstrated the pivotal role of immune function during the development of heart failure caused by DCM. This study may offer new opportunities for the detection and intervention of immune-related DCM.", "claims": [{"id": "claim-method", "statement": "Then, three different supervised machine learning algorithms were utilized to explore the diagnostic prediction model. The random forest model (RF), logical regression (LR), and elastic network model (EN) were respectively constructed.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38321374", "locator": "sections[7] \"Construction of diagnostic prediction models\" paragraph 2", "quote": "Then, three different supervised machine learning algorithms were utilized to explore the diagnostic prediction model. The random forest model (RF), logical regression (LR), and elastic network model (EN) were respectively constructed."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All the datasets analysed during the current study are available in the NCBI-GEO database (https://www.ncbi.nlm.nih.gov/geo/ GSE5406 and GSE57338). The data that support the findings of this study are available from the corresponding author, Haihua Chen, upon reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38321374", "locator": "sections[23] \"notes\" paragraph 1", "quote": "All the datasets analysed during the current study are available in the NCBI-GEO database (https://www.ncbi.nlm.nih.gov/geo/ GSE5406 and GSE57338). The data that support the findings of this study are available from the corresponding author, Haihua Chen, upon reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "In the external validation dataset GSE57338, the elastic network and logistic regression models achieved AUC values of 0.937 and 0.929, respectively, exceeding the random forest model AUC of 0.758.", "kind": "quantitative", "target": {"value": 0.758, "unit": "auc", "tolerance": null, "source": "In addition, the ROC curves of the validation set from GSE57338 also described that the AUC of the EN and LR was 0.937 and 0.929, which was higher than the AUC of 0.758 by the RF algorithms (Fig. 8)."}, "evidence": [{"asset": "source:pmid:38321374", "locator": "sections[14] \"Comparison and external validation of multiple models\" paragraph 2", "quote": "In addition, the ROC curves of the validation set from GSE57338 also described that the AUC of the EN and LR was 0.937 and 0.929, which was higher than the AUC of 0.758 by the RF algorithms (Fig. 8)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "In the external validation dataset GSE57338, the elastic network and logistic regression models achieved AUC values of 0.937 and 0.929, respectively, exceeding the random forest model AUC of 0.758.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:38321374"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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