Analysis of the role of glucose metabolism-related genes in dilated cardiomyopathy based on bioinformatics.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37559624", "kind": "science-record", "revision": 4, "content_hash": "sha256:a817ab5baf2a4ad6b6be92a8a01cb64abc87418d3f651052946d4e2f2ce49650", "created_at": "2026-11-01T19:02:20Z", "updated_at": "2026-11-01T22:52:29Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Analysis of the role of glucose metabolism-related genes in dilated cardiomyopathy based on bioinformatics.", "doi": "10.21037/jtd-23-906", "pmid": "37559624", "authors": ["Keping Chen", "Yan Shi", "Haijie Zhu"], "journal": "Journal of thoracic disease", "year": 2023, "abstract": "Background: Dilated cardiomyopathy (DCM) is a prevalent condition with diverse etiologies, including viral infection, autoimmune response, and genetic factors. Despite the crucial role of energy metabolism in cardiac function, therapeutic targets for key genes in DCM's energy metabolism remain scarce.\nMethods: Our study employed the GSE79962 and GSE42955 datasets from the Gene Expression Omnibus (GEO) database for myocardial tissue sample collection and target gene identification via differential gene expression screening. Using various R packages, GSEA software, and the STRING database, we conducted data analysis, gene set enrichment, and protein-protein interaction predictions. The least absolute shrinkage and selection operator (LASSO) and Support Vector Machine (SVM) algorithms aided in feature gene selection, while the predictive model's efficiency was evaluated via the receiver operating characteristic (ROC) curve analysis. We used the non-negative matrix factorization (NMF) method for molecular typing and the cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm for predicting immune cell infiltration.\nResults: The DLAT and LDHA genes may regulate the immune microenvironment of DCM by influencing activated dendritic cells, activated mast cells, and M0 macrophages, respectively. The BPGM, DLAT, PGM2, ADH1A, ADH1C, LDHA, and PFKM genes may regulate m6A methylation in DCM by affecting the ZC3H13, ALKBH5, RBMX, HNRNPC, METTL3, and YTHDC1 genes. Further regulatory mechanism analysis suggested that PFKM, DLAT, PKLR, PGM2, LDHA, BPGM, ADH1A, and ADH1C could be involved in the development of cardiomyopathy by regulating the Toll-like receptor signaling pathway.\nConclusions: PFKM, DLAT, PKLR, PGM2, LDHA, BPGM, ADH1A, and ADH1C may serve as potential targets for guiding the diagnosis, treatment, and follow-up of DCM.", "claims": [{"id": "claim-method", "statement": "DEGs were screened in normal myocardial tissue and DCM tissue with the “limma” package in R software (The R Foundation for Statistical Computing), and the DEGs were defined with |log2FC (fold change)| >0.05 and P<0.05 as the screening criteria.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37559624", "locator": "sections[5] \"Differentially expressed gene (DEGs) screening\" paragraph 2", "quote": "DEGs were screened in normal myocardial tissue and DCM tissue with the “limma” package in R software (The R Foundation for Statistical Computing), and the DEGs were defined with |log2FC (fold change)| >0.05 and P<0.05 as the screening criteria."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The GSE79962 (16) and GSE42955 (17) datasets were downloaded from the Gene Expression Omnibus (GEO) database. GSE42955 included 5 normal myocardial tissues and 12 DCM tissues. GSE79962 contained 11 normal myocardial tissues and 9 DCM tissues. The normal tissues and DCM samples of the 2 data sets were corrected in batches by the SVA program package (Bioconductor) and merged (18) to obtain a merged GEO-GSE79962 + GSE42955 (Merge) data set for subsequent analysis, including 16 normal myocardial tissues and 21 DCM tissues. A total of 23,306 genes were finally annotated in the GEO-Merge data set.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37559624", "locator": "sections[4] \"Data sources\" paragraph 2", "quote": "The GSE79962 (16) and GSE42955 (17) datasets were downloaded from the Gene Expression Omnibus (GEO) database. GSE42955 included 5 normal myocardial tissues and 12 DCM tissues. GSE79962 contained 11 normal myocardial tissues and 9 DCM tissues. The normal tissues and DCM samples of the 2 data sets were corrected in batches by the SVA program package (Bioconductor) and merged (18) to obtain a merged GEO-GSE79962 + GSE42955 (Merge) data set for subsequent analysis, including 16 normal myocardial tissues and 21 DCM tissues. A total of 23,306 genes were finally annotated in the GEO-Merge data set."}], "state": "complete"}, {"id": "claim-target-result", "statement": "PFKM, DLAT, PKLR, PGM2, LDHA, BPGM, ADH1A, and ADH1C may serve as potential targets for guiding the diagnosis, treatment, and follow-up of DCM.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37559624", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "PFKM, DLAT, PKLR, PGM2, LDHA, BPGM, ADH1A, and ADH1C may serve as potential targets for guiding the diagnosis, treatment, and follow-up of DCM."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "PFKM, DLAT, PKLR, PGM2, LDHA, BPGM, ADH1A, and ADH1C may serve as potential targets for guiding the diagnosis, treatment, and follow-up of DCM.", "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:37559624"], "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. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": [], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": [], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:obtain_source_data blocked on data:accession_absent"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37559624", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/jtd-23-906", "sha256": "sha256:11808d187efbce7550e16f744485a2e6bad0f7048a4b5aa8484cae4cbe86a138", "bytes": 36327, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.21037/jtd-23-906", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10407475", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}], "gaps": []}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "differential_expression", "specific": "limma_voom", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "DEGs were screened in normal myocardial tissue and DCM tissue with the “limma” package in R software (The R Foundation for Statistical Computing), and the DEGs were defined with |log2FC (fold change)| >0.05 and P<0.05 as the screening criteria."}}, {"id": "method-2", "family": "machine_learning", "specific": "support_vector_machine", "label": "Machine learning", "dsl_operations": ["train_classifier", "cross_validate", "roc_auc"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 0.7, "band": "medium", "quote": "Support Vector Machine"}}, {"id": "method-3", "family": "correlation_analysis", "specific": "pearson", "label": "Correlation analysis", "dsl_operations": ["pearson_r"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["paired_numeric_columns"], "evidence": {"score": 0.8, "band": "high", "quote": "Pearson correlation"}}, {"id": "method-4", "family": "clustering", "specific": "hierarchical", "label": "Clustering", "dsl_operations": ["hierarchical_cluster"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["feature_matrix"], "evidence": {"score": 0.8, "band": "high", "quote": "hierarchical clustering"}}, {"id": "method-5", "family": "network_analysis", "specific": "ppi_network", "label": "Network / pathway analysis", "dsl_operations": ["build_graph", "centrality_scores", "pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.7, "band": "medium", "quote": "protein-protein interaction"}}], "targets": [], "gaps": []}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "blocked", "tier": "R4", "blocking_axis": "data", "blocked_on": "data:accession_absent", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: limma_voom."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R4", "reachable": false, "accessions": ["GSE79962 and GSE42955"], "resolved_accessions": [], "statements": ["The GSE79962 (16) and GSE42955 (17) datasets were downloaded from the Gene Expression Omnibus (GEO) database. GSE42955 included 5 normal myocardial tissues and 12 DCM tissues. GSE79962 contained 11 normal myocardial tissues and 9 DCM tissues. The normal tissues and DCM samples of the 2 data sets were corrected in batches by the SVA program package (Bioconductor) and merged (18) to obtain a merged GEO-GSE79962 + GSE42955 (Merge) data set for subsequent analysis, including 16 normal myocardial tissues and 21 DCM tissues. A total of 23,306 genes were finally annotated in the GEO-Merge data set."], "basis": "The paper names GSE79962 and GSE42955, but the accession does not resolve in the repository (no reason recorded), so the data cannot be reached from here. 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