Identification of mitophagy-related genes with potential clinical utility in myocardial infarction at transcriptional level.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37304955", "kind": "science-record", "revision": 4, "content_hash": "sha256:8e498a91229872be676782cc56efe93733f4fcd729a2110ac12ee025a0e4a0bb", "created_at": "2026-11-01T19:02:02Z", "updated_at": "2026-11-01T22:52:06Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of mitophagy-related genes with potential clinical utility in myocardial infarction at transcriptional level.", "doi": "10.3389/fcvm.2023.1166324", "pmid": "37304955", "authors": ["Zhikai Yang", "Liang Sun", "Hua Wang"], "journal": "Frontiers in cardiovascular medicine", "year": 2023, "abstract": "Background: Myocardial infarction (MI) ranks among the most prevalent cardiovascular diseases. Insufficient blood flow to the coronary arteries always leads to ischemic necrosis of the cardiac muscle. However, the mechanism of myocardial injury after MI remains unclear. This article aims to explore the potential common genes between mitophagy and MI and to construct a suitable prediction model.\nMethods: Two Gene Expression Omnibus (GEO) datasets (GSE62646 and GSE59867) were used to screen the differential expression genes in peripheral blood. SVM, RF, and LASSO algorithm were employed to find MI and mitophagy-related genes. Moreover, DT, KNN, RF, SVM and LR were conducted to build the binary models, and screened the best model to further external validation (GSE61144) and internal validation (10-fold cross validation and Bootstrap), respectively. The performance of various machine learning models was compared. In addition, immune cell infiltration correlation analysis was conducted with MCP-Counter and CIBERSORT.\nResults: We finally identified ATG5, TOMM20, MFN2 transcriptionally differed between MI and stable coronary artery diseases. Both internal and external validation supported that these three genes could accurately predict MI withAUC = 0.914 and 0.930 by logistic regression, respectively. Additionally, functional analysis suggested that monocytes and neutrophils might be involved in mitochondrial autophagy after myocardial infarction.\nConclusion: The data showed that the transcritional levels of ATG5, TOMM20 and MFN2 in patients with MI were significantly different from the control group, which might be helpful to further accurately diagnose diseases and have potential application value in clinical practice.", "claims": [{"id": "claim-method", "statement": "Using the limma package of R software (17), all genes (n = 18,837) between myocardial infarction group and stable coronary disease group were explored, then it intersects with the mitophagy genes.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37304955", "locator": "sections[4] \"Differences in the expression of all genes\" paragraph 2", "quote": "Using the limma package of R software (17), all genes (n = 18,837) between myocardial infarction group and stable coronary disease group were explored, then it intersects with the mitophagy genes."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material. Further queries can be sent to the corresponding author(s).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37304955", "locator": "sections[17] \"Data availability statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material. Further queries can be sent to the corresponding author(s)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Both internal and external validation supported that these three genes could accurately predict MI withAUC = 0.914 and 0.930 by logistic regression, respectively.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37304955", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Both internal and external validation supported that these three genes could accurately predict MI withAUC = 0.914 and 0.930 by logistic regression, respectively."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Both internal and external validation supported that these three genes could accurately predict MI withAUC = 0.914 and 0.930 by logistic regression, respectively.", "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:37304955"], "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": "complete", "blocked_on": null, "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": ["data:geo:GSE59867"], "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": ["data:geo:GSE59867"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37304955", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fcvm.2023.1166324", "sha256": "sha256:6310a6fec1c39aff3d94ff411bf23647aa06807c7667141ebd5d5defd943e8c6", "bytes": 25115, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fcvm.2023.1166324", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10250750", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE59867", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE59867", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE59867", "version": null, "note": "The datasets presented in this study can be found in online repositories. 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The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["GSE59867", "GSE62646", "GSE61144"], "resolved_accessions": ["GSE59867", "GSE62646", "GSE61144"], "statements": ["The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/supplementary material. Further queries can be sent to the corresponding author(s)."], "basis": "The paper names GSE59867, GSE62646, GSE61144 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "assets": ["source:pmid:37304955", "data:geo:GSE59867"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE59867"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T18:43:36Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 7069, "completion_tokens": 1412, "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:37304955", "type": "paper", "label": "pmid:37304955"}, {"id": "source:pmid:37304955", "type": "manuscript", "label": "PMID 37304955 full text"}, {"id": "data:geo:GSE59867", "type": "dataset", "label": "GEO GSE59867"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:37304955", "type": "paper", "label": "pmid:37304955"}, {"id": "source:pmid:37304955", "type": "manuscript", "label": "PMID 37304955 full text"}, {"id": "data:geo:GSE59867", "type": "dataset", "label": "GEO GSE59867"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-37304955", "type": "extraction", "used": ["source:pmid:37304955"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE59867", "type": "accession_resolution", "used": ["data:geo:GSE59867"], "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:37304955", "parent_hash": "sha256:96b842b67ad5aaefc781a02e74408eb189b97dec68dc9b60bd51c74a6670c466", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE59867 licence not read", "asset:data:geo:GSE59867 not downloaded", "asset:data:geo:GSE61144 licence not read", "asset:data:geo:GSE61144 not downloaded", "asset:data:geo:GSE62646 licence not read", "asset:data:geo:GSE62646 not downloaded", "execution.not_run", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"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": ["Using the limma package of R software (17), all genes (n = 18,837) between myocardial infarction group and stable coronary disease group were explored, then it intersects with the mitophagy genes."], "order_basis": "analysis_only", "id": "assay:1"}]}