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pmid:37507655
Identification of crucial genes related to heart failure based on GEO database.
Yongliang Chen · Jing Xue · Xiaoli Yan · Da-Guang Fang · Fangliang Li · Xuefei Tian · Peng Yan · Zengbin Feng
BMC cardiovascular disorders · 2023 doi:10.1186/s12872-023-03400-x
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Experiment
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Analysis
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Execution
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Complete file
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Therefore, it is imperative to use innovative approaches, such as high-throughput sequencing and artificial intelligence, to investigate the pathogenesis, diagnosis, and potential treatment of HF.\nMETHODS: First, we initially screened Two data sets (GSE3586 and GSE5406) from the GEO database containing HF and control samples from the GEO database to establish the Train group, and selected another dataset (GSE57345) to construct the Test group for verification. Next, we identified the genes with significantly different expression levels in patients with or without HF and performed functional and pathway enrichment analyses. HF-specific genes were identified, and an artificial neural network was constructed by Random Forest. The ROC curve was used to evaluate the accuracy and reliability of the constructed model in the Train and Test groups. Finally, immune cell infiltration was analyzed to determine the role of the inflammatory response and the immunological microenvironment in the pathogenesis of HF.\nRESULTS: In the Train group, 153 significant differentially expressed genes (DEGs) associated with HF were found to be abnormal, including 81 down-regulated genes and 72 up-regulated genes. GO and KEGG enrichment analyses revealed that the down-regulated genes were primarily enriched in organic anion transport, neutrophil activation, and the PI3K-Akt signaling pathway. The upregulated genes were mainly enriched in neutrophil activation and the calcium signaling. DEGs were identified using Random Forest, and finally, 16 HF-specific genes were obtained. In the ROC validation and evaluation, the area under the curve (AUC) of the Train and Test groups were 0.996 and 0.863, respectively.\nCONCLUSIONS: Our research revealed the potential functions and pathways implicated in the progression of HF, and designed an RNA diagnostic model for HF tissues using machine learning and artificial neural networks. Sensitivity, specificity, and stability were confirmed by ROC curves in the two different cohorts.", "claims": [{"id": "claim-method", "statement": "The R package “limma” was used to identify differentially expressed genes (DEGs) in HF, and “heatmap” was used to plot heat and volcano maps.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37507655", "locator": "sections[4] \"Analysis of differential gene expressions and functional enrichment in HF\" paragraph 2", "quote": "The R package “limma” was used to identify differentially expressed genes (DEGs) in HF, and “heatmap” was used to plot heat and volcano maps."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. 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"platform": "11532;9052", "taxon": "Homo sapiens", "title": "RNA-Seq Identifies Novel Myocardial Gene Expression Signatures of Heart Failure", "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:GSE3586 not downloaded", "asset:data:geo:GSE3586 licence not read", "asset:data:geo:GSE5406 not downloaded", "asset:data:geo:GSE5406 licence not read", "asset:data:geo:GSE57345 not downloaded", "asset:data:geo:GSE57345 licence not read"]}, "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": "The R package “limma” was used to identify differentially expressed genes (DEGs) in HF, and “heatmap” was used to plot heat and volcano maps."}}, {"id": "method-2", "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-3", "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": 0.8, "band": "high", "quote": "protein-protein interaction"}}, {"id": "method-4", "family": "diagnostic_evaluation", "specific": "roc", "label": "Diagnostic test evaluation", "dsl_operations": ["confusion_counts", "roc_curve", "sensitivity_specificity", "solve_confusion_matrix", "classification_metrics", "published_precision_envelope"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["reference_standard_and_test"], "evidence": {"score": 0.7, "band": "medium", "quote": "ROC curve"}}], "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": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "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": "R1", "reachable": true, "accessions": ["GSE5406", "GSE3586", "GSE57345"], "resolved_accessions": ["GSE5406", "GSE3586", "GSE57345"], "statements": ["Publicly available datasets were analyzed in this study. This data can be found here:\nGEO website: https://www.ncbi.nlm.nih.gov/geo/.\nWebGestalt 2019: http://www.webgestalt.org/.\nMetascape (version 3.5): http://metascape.org/.\nR software (version 4.0.4): https://www.R-project.org.\nString website (version 11.5): https://cn.string-db.org/.\nCIBERSORT algorithm: https://cibersort.stanford.edu/runcibersort.php."], "basis": "The paper names GSE5406, GSE3586, GSE57345 in 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:37507655", "data:geo:GSE3586"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE3586"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T18:48:05Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 14300, "completion_tokens": 2091, "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:37507655", "type": "paper", "label": "pmid:37507655"}, {"id": "source:pmid:37507655", "type": "manuscript", "label": "PMID 37507655 full text"}, {"id": "data:geo:GSE3586", "type": "dataset", "label": "GEO GSE3586"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:37507655", "type": "paper", "label": "pmid:37507655"}, {"id": "source:pmid:37507655", "type": "manuscript", "label": "PMID 37507655 full text"}, {"id": "data:geo:GSE3586", "type": "dataset", "label": "GEO GSE3586"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-37507655", "type": "extraction", "used": ["source:pmid:37507655"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE3586", "type": "accession_resolution", "used": ["data:geo:GSE3586"], "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:37507655", "parent_hash": "sha256:056517d61ae5f2f24d81dabe8ca89ca3afa2b5b2b73cb586dbdafa62573d21b5", "fork_of": null, "generation": 4}, "gaps": ["asset:data:geo:GSE3586 licence not read", "asset:data:geo:GSE3586 not downloaded", "asset:data:geo:GSE5406 licence not read", "asset:data:geo:GSE5406 not downloaded", "asset:data:geo:GSE57345 licence not read", "asset:data:geo:GSE57345 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": ["The R package “limma” was used to identify differentially expressed genes (DEGs) in HF, and “heatmap” was used to plot heat and volcano maps."], "order_basis": "analysis_only", "id": "assay:1"}]}