Identification of key programmed cell death-related genes and immune infiltration in extracorporeal membrane oxygenation treatment for acute myocardial infarction based on bioinformatics analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36531699", "kind": "science-record", "revision": 4, "content_hash": "sha256:91bb58c10c3f4cd688305189f102631128924e552ef96c9a6d41c9491dbdc23c", "created_at": "2026-11-01T19:01:03Z", "updated_at": "2026-11-01T22:50:51Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of key programmed cell death-related genes and immune infiltration in extracorporeal membrane oxygenation treatment for acute myocardial infarction based on bioinformatics analysis.", "doi": "10.3389/fcvm.2022.1018662", "pmid": "36531699", "authors": ["Jingqi Yang", "Xiaochao Ouyang", "Ming Yang", "Guobo Xie", "Qianqiang Cao"], "journal": "Frontiers in cardiovascular medicine", "year": 2022, "abstract": "Background: Extracorporeal membrane oxygenation (ECMO) is an important clinical treatment for acute myocardial infarction (AMI) combined with cardiogenic shock, but the role of programmed cell death (PCD)-related genes in prognostication has not yet been investigated. Therefore, we explored the key prognostic biomarkers and immune infiltration in ECMO treatment in AMI combined with cardiogenic shock.\nMethods: The GSE93101 dataset was analyzed from the Gene Expression Omnibus (GEO) database, and the expression levels of PCD-related genes in AMI under ECMO were identified. Differentially expressed PCD-related genes between successful and failed treatment samples were analyzed, and Least absolute shrinkage and selection operator (LASSO) logistic regression and random forest were used to screen PCD-related molecular markers for ECMO treatment in AMI combined with cardiogenic shock. Co-expressed regulatory network and enrichment functions of the hub PCD-related genes were performed. In addition, the single-sample gene set enrichment analysis (ssGSEA) algorithm was used to calculate the immune cell infiltration of the ECMO treatment samples.\nResults: A total of 115 differentially expressed genes were identified from the GSE93101 dataset, and 76 genes were associated with PCD. Then, two hub PCD-related genes, Cell division cycle associated 7 (CDCA7), ankyrin repeat and SOCS box containing 13 (ASB13) were identified as prognostic markers of ECMO treatment in AMI combined with cardiogenic shock. The most significant Gene Ontology (GO) enriched terms of the co-expressed protein of ASB13 are related to post-translational protein modification, cullin-RING ubiquitin ligase complex, and cullin family protein binding, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that ubiquitin mediated proteolysis is the most enriched pathway. The results of GO and KEGG analysis in CDCA7 were mainly involved in DNA and cell cycle related activities and pathways. Moreover, we found that the successful treatment samples contained a lower proportion of nature killer T cells using immune infiltration analysis. Immune cell infiltration analysis revealed that ASB13 was positively correlated with natural killer cell (r = 0.591, p = 0.026), monocyte (r = 0.586, p = 0.028), and gamma delta T cell (r = 0.562, p = 0.036).\nConclusion: The results of this study showed that ASB13 and CDCA7 may contribute to the occurrence and progression of AMI with cardiogenic shock under ECMO.", "claims": [{"id": "claim-method", "statement": "Least absolute shrinkage and selection operator (LASSO) logistic regression (20), random forest (21), and support vector machine (SVM) (22) algorithms were used to screen PCD-related molecular markers for AMI under ECMO.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36531699", "locator": "sections[5] \"Screening and verification of hub genes\" paragraph 2", "quote": "Least absolute shrinkage and selection operator (LASSO) logistic regression (20), random forest (21), and support vector machine (SVM) (22) algorithms were used to screen PCD-related molecular markers for AMI under ECMO."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. This data can be found here: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE93101.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36531699", "locator": "sections[16] \"Data availability statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. This data can be found here: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE93101."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Two hub PCD-related genes, CDCA7 and ASB13, were identified as prognostic markers of ECMO treatment in AMI combined with cardiogenic shock.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36531699", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Then, two hub PCD-related genes, Cell division cycle associated 7 (CDCA7), ankyrin repeat and SOCS box containing 13 (ASB13) were identified as prognostic markers of ECMO treatment in AMI combined with cardiogenic shock."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Two hub PCD-related genes, CDCA7 and ASB13, were identified as prognostic markers of ECMO treatment in AMI combined with cardiogenic shock.", "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:36531699"], "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:GSE93101"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "blocked", "blocked_on": "method:machine_learning_random_forest", "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:GSE93101"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:machine_learning_random_forest"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36531699", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fcvm.2022.1018662", "sha256": "sha256:543a1855f9fe675c0cb3d63ca7b25a578d1b38adaa7643a499d4c3b7fa7c1176", "bytes": 28737, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fcvm.2022.1018662", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9755732", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE93101", "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=GSE93101", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE93101", "version": null, "note": "Publicly available datasets were analyzed in this study. This data can be found here: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE93101.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Publicly available datasets were analyzed in this study. This data can be found here: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE93101.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM2443799", "GSM2443800", "GSM2443801", "GSM2443802", "GSM2443803", "GSM2443804", "GSM2443805", "GSM2443806", "GSM2443807", "GSM2443808", "GSM2443809", "GSM2443810", "GSM2443811", "GSM2443812", "GSM2443813", "GSM2443814", "GSM2443815", "GSM2443816", "GSM2443817", "GSM2443818", "GSM2443819", "GSM2443820", "GSM2443821", "GSM2443822", "GSM2443823", "GSM2443824", "GSM2443825", "GSM2443826", "GSM2443827", "GSM2443828", "GSM2443829", "GSM2443830", "GSM2443831"], "sample_count": 33, "group_sizes": null, "group_sizes_basis": null, "platform": "14951", "taxon": "Homo sapiens", "title": "Molecular Prognosis of Cardiogenic Shock Patients under Extracorporeal Membrane Oxygenation", "resolution_checked_at": "2026-11-01T18:46:34Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE93101 not downloaded", "asset:data:geo:GSE93101 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "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": 1.0, "band": "verbatim", "quote": "Least absolute shrinkage and selection operator (LASSO) logistic regression (20), random forest (21), and support vector machine (SVM) (22) algorithms were used to screen PCD-related molecular markers for AMI under ECMO."}}, {"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": 0.8, "band": "high", "quote": "support vector machine"}}, {"id": "method-3", "family": "regression_modelling", "specific": "logistic", "label": "Regression modelling", "dsl_operations": ["glm_logit_fit"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["predictors_and_outcome"], "evidence": {"score": 0.7, "band": "medium", "quote": "logistic regression"}}, {"id": "method-4", "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"}}], "targets": [], "gaps": ["method.machine_learning_random_forest"]}, "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": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": false, "required_operations": [], "missing_operations": ["machine_learning_random_forest"], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: random_forest."}, "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": "R1", "reachable": true, "accessions": ["GSE93101"], "resolved_accessions": ["GSE93101"], "statements": ["Publicly available datasets were analyzed in this study. This data can be found here: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE93101."], "basis": "The paper names GSE93101 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": [], "assets": ["source:pmid:36531699", "data:geo:GSE93101"], "steps": []}, "must_build": {"dsl_operations": [{"operation": "machine_learning_random_forest", "unlocks_papers": null, "priority": null}], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE93101"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T18:20:07Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 8084, "completion_tokens": 1137, "cached_tokens": 264}}, "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:36531699", "type": "paper", "label": "pmid:36531699"}, {"id": "source:pmid:36531699", "type": "manuscript", "label": "PMID 36531699 full text"}, {"id": "data:geo:GSE93101", "type": "dataset", "label": "GEO GSE93101"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:36531699", "type": "paper", "label": "pmid:36531699"}, {"id": "source:pmid:36531699", "type": "manuscript", "label": "PMID 36531699 full text"}, {"id": "data:geo:GSE93101", "type": "dataset", "label": "GEO GSE93101"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-36531699", "type": "extraction", "used": ["source:pmid:36531699"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE93101", "type": "accession_resolution", "used": ["data:geo:GSE93101"], "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:36531699", "parent_hash": "sha256:73574817bb38147d1464d55123c3138f135e38c77d013475af96742fb3fe0a26", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE93101 licence not read", "asset:data:geo:GSE93101 not downloaded", "execution.not_run", "method.machine_learning_random_forest", "review.human_critique", "review.independent_reader", "review.machine_critique", "step:run_analysis blocked on method:machine_learning_random_forest"], "assays": [{"role": "analysis", "definition_id": "machine_learning", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Least absolute shrinkage and selection operator (LASSO) logistic regression (20), random forest (21), and support vector machine (SVM) (22) algorithms were used to screen PCD-related molecular markers for AMI under ECMO."], "order_basis": "analysis_only", "id": "assay:1"}]}