Integrated bioinformatics and experiment validation reveal cuproptosis-related biomarkers and therapeutic targets in sepsis-induced myocardial dysfunction.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40165133", "kind": "science-record", "revision": 4, "content_hash": "sha256:b682a5776ad46f8208c1352a06b454b41be530ac478075c44095e2bbcc3e654f", "created_at": "2026-11-01T20:30:21Z", "updated_at": "2026-11-01T22:56:18Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrated bioinformatics and experiment validation reveal cuproptosis-related biomarkers and therapeutic targets in sepsis-induced myocardial dysfunction.", "doi": "10.1186/s12879-025-10822-9", "pmid": "40165133", "authors": ["Xuemei Shi", "Zhonghan Yan", "Ruilin Ding", "Fenfen Xia", "Yan Du", "Xiaojie Wang", "Qing Peng"], "journal": "BMC infectious diseases", "year": 2025, "abstract": "BACKGROUND: Sepsis-induced myocardial dysfunction (SIMD) is a serious sepsis complication with high mortality, yet current diagnostic and therapeutic approaches remain limited. The lack of early, specific biomarkers and effective treatments necessitates exploration of novel mechanisms. Recently, cuproptosis has been implicated in various diseases, but its role in SIMD is unclear. This study aimed to identify cuproptosis-related biomarkers and potential therapeutic agents, supported by animal model validation.\nMETHODS: Four GEO datasets (GSE79962, GSE267388, GSE229925, GSE229298) were analyzed using Limma and WGCNA to identify overlapping genes from differentially expressed genes (DEGs), cuproptosis-related DEGs (DE-CRGs), and module-associated genes. Gene Set Enrichment Analysis (GSEA) and single-sample GSEA (ssGSEA) were performed to assess biological functions and immune cell infiltration, respectively. ceRNA and transcription factor networks were constructed to explore gene regulatory mechanisms, while consensus clustering was employed to define cuproptosis-related subtypes. Diagnostic genes were selected through SVM-RFE, LASSO, and random forest models. Additionally, potential gene-targeting agents were predicted using drug-gene interaction analysis. The findings were validated in SIMD animal models through qPCR and immunohistochemical analysis to confirm gene expression.\nRESULTS: PDHB and DLAT emerged as key cuproptosis-related biomarkers. GSEA indicated upregulation of oxidative phosphorylation and downregulation of chemokine signaling. ssGSEA revealed negative correlations with several immune cell types. A ceRNA network (51 nodes, 56 edges) was constructed. Machine learning identified PDHB, NDUFA9, and TIMMDC1 as diagnostic genes, with PDHB showing high accuracy (AUC = 0.995 in GSE79962; AUC = 0.960, 0.864, and 0.984 in external datasets). Using the DSigDB database, we predicted six drugs that exhibit significant binding activity with PDHB. qPCR and immunohistochemistry confirmed reduced PDHB and DLAT expression in SIMD animal models.\nCONCLUSION: This study identifies PDHB and DLAT as cuproptosis-related biomarkers, addressing the diagnostic and therapeutic gaps in SIMD by unveiling novel molecular insights for early intervention and targeted treatment.\nCLINICAL TRIAL NUMBER: Not applicable.", "claims": [{"id": "claim-method", "statement": "Four GEO datasets (GSE79962, GSE267388, GSE229925, GSE229298) were analyzed using Limma and WGCNA to identify overlapping genes from differentially expressed genes (DEGs), cuproptosis-related DEGs (DE-CRGs), and module-associated genes.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40165133", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Four GEO datasets (GSE79962, GSE267388, GSE229925, GSE229298) were analyzed using Limma and WGCNA to identify overlapping genes from differentially expressed genes (DEGs), cuproptosis-related DEGs (DE-CRGs), and module-associated genes."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated and analysed during the current study are available in the GEO repository (https://www.ncbi.nlm.nih.gov/geo/). We searched MiRanda. (http://www.microRNA.org) miRDB. (https://mirdb.org/), and TargetScan. (http://www.TargetScan.org/vert_71/) to find miRNAs. We used TRRUST. (http://www.grnpedia.org/trrust) databases to predict TFs. Drug prediction passed DSigDB. (http://dsigdb.tanlab.org/DSigDBv1.0/), small molecule drug structure comes from PubChem. (https://pubchem.ncbi.nlm.nih.gov/). PDHB protein structure from PDB. (https://www.rcsb.org/). Molecular docking data from CB-Dock2. (https://cadd.labshare.cn/cb-dock2/index.php).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40165133", "locator": "sections[53] \"Data availability\" paragraph 2", "quote": "The datasets generated and analysed during the current study are available in the GEO repository (https://www.ncbi.nlm.nih.gov/geo/). We searched MiRanda. (http://www.microRNA.org) miRDB. (https://mirdb.org/), and TargetScan. (http://www.TargetScan.org/vert_71/) to find miRNAs. We used TRRUST. (http://www.grnpedia.org/trrust) databases to predict TFs. Drug prediction passed DSigDB. (http://dsigdb.tanlab.org/DSigDBv1.0/), small molecule drug structure comes from PubChem. (https://pubchem.ncbi.nlm.nih.gov/). PDHB protein structure from PDB. (https://www.rcsb.org/). Molecular docking data from CB-Dock2. (https://cadd.labshare.cn/cb-dock2/index.php)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Machine learning identified PDHB, NDUFA9, and TIMMDC1 as diagnostic genes, with PDHB showing high accuracy (AUC = 0.995 in GSE79962; AUC = 0.960, 0.864, and 0.984 in external datasets).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40165133", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Machine learning identified PDHB, NDUFA9, and TIMMDC1 as diagnostic genes, with PDHB showing high accuracy (AUC = 0.995 in GSE79962; AUC = 0.960, 0.864, and 0.984 in external datasets)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Machine learning identified PDHB, NDUFA9, and TIMMDC1 as diagnostic genes, with PDHB showing high accuracy (AUC = 0.995 in GSE79962; AUC = 0.960, 0.864, and 0.984 in external datasets).", "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:40165133"], "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:40165133", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12879-025-10822-9", "sha256": "sha256:d97720a7d1ee339db8845b4f68801ec97b420c1d8a6f0c37f857ca4e34ef6ac1", "bytes": 61965, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12879-025-10822-9", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11956201", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}], "gaps": []}, "analysis": {"state": "partial", "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": "Four GEO datasets (GSE79962, GSE267388, GSE229925, GSE229298) were analyzed using Limma and WGCNA to identify overlapping genes from differentially expressed genes (DEGs), cuproptosis-related DEGs (DE-CRGs), and module-associated genes."}}, {"id": "method-2", "family": "principal_component_analysis", "specific": "pca", "label": "Principal component analysis", "dsl_operations": ["pca_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["wide_numeric_matrix"], "evidence": {"score": 0.8, "band": "high", "quote": "PCA"}}, {"id": "method-3", "family": "machine_learning", "specific": "random_forest", "label": "Machine learning", "dsl_operations": [], "missing_operations": ["machine_learning_random_forest"], "expressible": false, "parameters": {}, "data_shapes": ["labelled_feature_matrix"], "evidence": {"score": 0.7, "band": "medium", "quote": "random forest"}}, {"id": "method-4", "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": "SVM"}}, {"id": "method-5", "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-6", "family": "network_analysis", "specific": "pathway_enrichment", "label": "Network / pathway analysis", "dsl_operations": ["pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.8, "band": "high", "quote": "pathway enrichment"}}], "targets": [], "gaps": ["method.reader_differs_from_extractor"]}, "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": [], "resolved_accessions": [], "statements": ["The datasets generated and analysed during the current study are available in the GEO repository (https://www.ncbi.nlm.nih.gov/geo/). We searched MiRanda. (http://www.microRNA.org) miRDB. (https://mirdb.org/), and TargetScan. (http://www.TargetScan.org/vert_71/) to find miRNAs. We used TRRUST. (http://www.grnpedia.org/trrust) databases to predict TFs. Drug prediction passed DSigDB. (http://dsigdb.tanlab.org/DSigDBv1.0/), small molecule drug structure comes from PubChem. (https://pubchem.ncbi.nlm.nih.gov/). PDHB protein structure from PDB. (https://www.rcsb.org/). Molecular docking data from CB-Dock2. (https://cadd.labshare.cn/cb-dock2/index.php)."], "basis": "The paper names no accession to its source data, so the data has to come from the authors."}}, "can_run": {"dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "assets": ["source:pmid:40165133"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:None"]}, "gaps": ["data:accession_absent"]}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:49:31Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 17305, "completion_tokens": 1575, "cached_tokens": 128}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:40165133", "type": "paper", "label": "pmid:40165133"}, {"id": "source:pmid:40165133", "type": "manuscript", "label": "PMID 40165133 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:40165133", "type": "paper", "label": "pmid:40165133"}, {"id": "source:pmid:40165133", "type": "manuscript", "label": "PMID 40165133 full text"}, {"id": "data:geo:None", "type": "dataset", "label": "GEO None"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-40165133", "type": "extraction", "used": ["source:pmid:40165133"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-None", "type": "accession_resolution", "used": ["data:geo:None"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:40165133", "parent_hash": "sha256:bd877b375aeb5db772ae8b283e9fb75b3dcee94e8a7dfba1fe4bd0925502a4a2", "fork_of": null, "generation": 3}, "gaps": ["execution.not_run", "method.reader_differs_from_extractor", "review.human_critique", "review.independent_reader", "review.machine_critique", "step:obtain_source_data blocked on data:accession_absent"], "assays": [{"role": "data_generation", "definition_id": "qpcr", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["were validated in SIMD animal models through qPCR and immunohistochemical analysis to confirm", "ibit significant binding activity with PDHB. qPCR and immunohistochemistry confirmed reduced P", "on was promptly frozen at -80 °C for further qPCR, and the remaining portion was preserved wit"], "order_basis": "paper_text_order:1444", "id": "assay:1"}, {"role": "data_generation", "definition_id": "ihc", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["dated in SIMD animal models through qPCR and immunohistochemical analysis to confirm gene expression. RESULTS", "ificant binding activity with PDHB. qPCR and immunohistochemistry confirmed reduced PDHB and DLAT expression i", "ubsequent histopathological examinations and immunohistochemistry. Enrichment analysis of functional pathways", "mouse and controls. **P < 0.01, ***P < 0.001 Immunohistochemical staining analysis of two feature genes. A Qu"], "order_basis": "paper_text_order:1453", "id": "assay:2"}, {"role": "data_generation", "definition_id": "animal_model", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["ving unlimited access to food and water. Six mice were randomly grouped into two separate groups fo", "After being administered LPS or saline, the mice were sedated with 1.0-1.5% isoflurane for 12 h. E", "J, K). Based on the above results, the SIMD mouse model was successfully established. Construction o", "ccessfully established. Construction of SIMD mouse model. A, B Representative echocardiographic image"], "order_basis": "paper_text_order:3449", "id": "assay:3"}, {"role": "data_generation", "definition_id": "elisa", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["iltration after LPS treatment (Fig. 16C, D). ELISA demonstrated elevated serum markers of myoca", "ion (EF) and (F) fractional shortening (FS). ELISA results for (G) CKMB, (H) cTnT, (I) IL-6, (J"], "order_basis": "paper_text_order:12736", "id": "assay:4"}, {"role": "analysis", "definition_id": "differential_expression", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Four GEO datasets (GSE79962, GSE267388, GSE229925, GSE229298) were analyzed using Limma and WGCNA to identify overlapping genes from differentially expressed genes (DEGs), cuproptosis-related DEGs (DE-CRGs), and module-associated genes."], "order_basis": "consumes_earlier_output", "id": "assay:5"}]}