Integrated Bioinformatics-Based Analysis of Hub Genes and the Mechanism of Immune Infiltration Associated With Acute Myocardial Infarction.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35463752", "kind": "science-record", "revision": 5, "content_hash": "sha256:c88c36b92d71998aff52f60e44e6ed0c7387b787034df92a3a871f636656266f", "created_at": "2026-11-01T18:14:19Z", "updated_at": "2026-11-01T22:48:55Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrated Bioinformatics-Based Analysis of Hub Genes and the Mechanism of Immune Infiltration Associated With Acute Myocardial Infarction.", "doi": "10.3389/fcvm.2022.831605", "pmid": "35463752", "authors": ["Yanze Wu", "Ting Jiang", "Jinghai Hua", "Zhiping Xiong", "Hui Chen", "Lei Li", "Jingtian Peng", "Wenjun Xiong"], "journal": "Frontiers in cardiovascular medicine", "year": 2022, "abstract": "Background: Acute myocardial infarction (AMI) is a fatal disease that causes high morbidity and mortality. It has been reported that AMI is associated with immune cell infiltration. Now, we aimed to identify the potential diagnostic biomarkers of AMI and uncover the immune cell infiltration profile of AMI.\nMethods: From the Gene Expression Omnibus (GEO) data set, three data sets (GSE48060, GSE60993, and GSE66360) were downloaded. Differentially expressed genes (DEGs) from AMI and healthy control samples were screened. Furthermore, DEGs were performed via gene ontology (GO) functional and kyoto encyclopedia of genes and genome (KEGG) pathway analyses. The Gene set enrichment analysis (GSEA) was used to analyze GO terms and KEGG pathways. Utilizing the Search Tool for Retrieval of Interacting Genes/Proteins (STRING) database, a protein-protein interaction (PPI) network was constructed, and the hub genes were identified. Then, the receiver operating characteristic (ROC) curves were constructed to analyze the diagnostic value of hub genes. And, the diagnostic value of hub genes was further validated in an independent data set GSE61144. Finally, CIBERSORT was used to represent the compositional patterns of the 22 types of immune cell fractions in AMI.\nResults: A total of 71 DEGs were identified. These DEGs were mainly enriched in immune response and immune-related pathways. Toll-like receptor 2 (TLR2), interleukin-1B (IL1B), leukocyte immunoglobulin-like receptor subfamily B2 (LILRB2), Fc fragment of IgE receptor Ig (FCER1G), formyl peptide receptor 1 (FPR1), and matrix metalloproteinase 9 (MMP9) were identified as diagnostic markers with the value of p < 0.05. Also, the immune cell infiltration analysis indicated that TLR2, IL1B, LILRB2, FCER1G, FPR1, and MMP9 were correlated with neutrophils, monocytes, resting natural killer (NK) cells, gamma delta T cells, and CD4 memory resting T cells. The fractions of monocytes and neutrophils were significantly higher in AMI tissues than in control tissues.\nConclusion: TLR2, IL1B, LILRB2, FCER1G, FPR1, and MMP9 are involved in the process of AMI, which can be used as molecular biomarkers for the screening and diagnosis of AMI. In addition, the immune system plays a vital role in the occurrence and progression of AMI.", "claims": [{"id": "claim-method", "statement": "Furthermore, the three data sets were merged into a metadata cohort and the batch effects were preprocessed and adjusted by the ComBat function of the SVA package (14). In addition, the “limma” of R software was used to perform background correction and array normalization. Meanwhile, DEGs between patients with AMI and healthy controls were identified using the “limma” package (15).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35463752", "locator": "sections[4] \"Identification of DEGs\" paragraph 3", "quote": "Furthermore, the three data sets were merged into a metadata cohort and the batch effects were preprocessed and adjusted by the ComBat function of the SVA package (14). In addition, the “limma” of R software was used to perform background correction and array normalization. Meanwhile, DEGs between patients with AMI and healthy controls were identified using the “limma” package (15)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. This data can be found here: All the raw data used in this study are available in the public GEO database (https://www.ncbi.nlm.nih.gov/geo/; Accession numbers: GSE66360, GSE48060, GSE60993, and GSE61144).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35463752", "locator": "sections[21] \"Data Availability Statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. This data can be found here: All the raw data used in this study are available in the public GEO database (https://www.ncbi.nlm.nih.gov/geo/; Accession numbers: GSE66360, GSE48060, GSE60993, and GSE61144)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The six hub genes TLR2, IL1B, LILRB2, FCER1G, FPR1, and MMP9 showed high diagnostic ability for distinguishing AMI samples from controls, with AUC values of 0.833, 0.806, 0.825, 0.770, 0.808, and 0.816, respectively.", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "Figure 8 shows the AUC for TLR2 was 0.833 (95% CI, 0.769–0.896; p = 0.000), AUC for IL1B was 0.806 (95% CI, 0.739–0.872; p = 0.000), AUC for LILRB2 was 0.825 (95% CI, 0.760–0,889; p = 0.000), FCER1G was 0.770 (95% CI, 0.698–0.841; p = 0.000), AUC for FPR1 was 0.808 (95% CI, 0.740–0.877; p = 0.000), and AUC for MMP9 was 0.816 (95% CI, 0.748–0.884; p = 0.000), indicating that the hub genes had high diagnostic ability."}, "evidence": [{"asset": "source:pmid:35463752", "locator": "sections[16] \"Validation of Diagnostic Values of Hub Genes\" paragraph 2", "quote": "Figure 8 shows the AUC for TLR2 was 0.833 (95% CI, 0.769–0.896; p = 0.000), AUC for IL1B was 0.806 (95% CI, 0.739–0.872; p = 0.000), AUC for LILRB2 was 0.825 (95% CI, 0.760–0,889; p = 0.000), FCER1G was 0.770 (95% CI, 0.698–0.841; p = 0.000), AUC for FPR1 was 0.808 (95% CI, 0.740–0.877; p = 0.000), and AUC for MMP9 was 0.816 (95% CI, 0.748–0.884; p = 0.000), indicating that the hub genes had high diagnostic ability."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The six hub genes TLR2, IL1B, LILRB2, FCER1G, FPR1, and MMP9 showed high diagnostic ability for distinguishing AMI samples from controls, with AUC values of 0.833, 0.806, 0.825, 0.770, 0.808, and 0.816, 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:35463752"], "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:GSE48060"], "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:GSE48060"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35463752", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fcvm.2022.831605", "sha256": "sha256:728c986b2e3486fd1de6003255f535d330a0d53599c3337f97a2fce4449b200d", "bytes": 28987, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fcvm.2022.831605", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9019083", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE48060", "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=GSE48060", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE48060", "version": null, "note": "Publicly available datasets were analyzed in this study. This data can be found here: All the raw data used in this study are available in the public GEO database (https://www.ncbi.nlm.nih.gov/geo/; Accession numbers: GSE66360, GSE48060, GSE60993, and GSE61144).", "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: All the raw data used in this study are available in the public GEO database (https://www.ncbi.nlm.nih.gov/geo/; Accession numbers: GSE66360, GSE48060, GSE60993, and GSE61144).", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1167072", "GSM1167073", "GSM1167074", "GSM1167075", "GSM1167076", "GSM1167077", "GSM1167078", "GSM1167079", "GSM1167080", "GSM1167081", "GSM1167082", "GSM1167083", "GSM1167084", "GSM1167085", "GSM1167086", "GSM1167087", "GSM1167088", "GSM1167089", "GSM1167090", "GSM1167091", "GSM1167092", "GSM1167093", "GSM1167094", "GSM1167095", "GSM1167096", "GSM1167097", "GSM1167098", "GSM1167099", "GSM1167100", "GSM1167101", "GSM1167102", "GSM1167103", "GSM1167104", "GSM1167105", "GSM1167106", "GSM1167107", "GSM1167108", "GSM1167109", "GSM1167110", "GSM1167111", "GSM1167112", "GSM1167113", "GSM1167114", "GSM1167115", "GSM1167116", "GSM1167117", "GSM1167118", "GSM1167119", "GSM1167120", "GSM1167121", "GSM1167122", "GSM1167123"], "sample_count": 52, "group_sizes": null, "group_sizes_basis": null, "platform": "570", "taxon": "Homo sapiens", "title": "Transcriptome from circulating cells suggests dysregulated pathways associated with long-term recurrent events following first-time myocardial infarction.", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE60993", "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=GSE60993", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE60993", "version": null, "note": "Publicly available datasets were analyzed in this study. This data can be found here: All the raw data used in this study are available in the public GEO database (https://www.ncbi.nlm.nih.gov/geo/; Accession numbers: GSE66360, GSE48060, GSE60993, and GSE61144).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Publicly available datasets were analyzed in this study. 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This data can be found here: All the raw data used in this study are available in the public GEO database (https://www.ncbi.nlm.nih.gov/geo/; Accession numbers: GSE66360, GSE48060, GSE60993, and GSE61144).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Publicly available datasets were analyzed in this study. 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This data can be found here: All the raw data used in this study are available in the public GEO database (https://www.ncbi.nlm.nih.gov/geo/; Accession numbers: GSE66360, GSE48060, GSE60993, and GSE61144).", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1620819", "GSM1620820", "GSM1620821", "GSM1620822", "GSM1620823", "GSM1620824", "GSM1620825", "GSM1620826", "GSM1620827", "GSM1620828", "GSM1620829", "GSM1620830", "GSM1620831", "GSM1620832", "GSM1620833", "GSM1620834", "GSM1620835", "GSM1620836", "GSM1620837", "GSM1620838", "GSM1620839", "GSM1620840", "GSM1620841", "GSM1620842", "GSM1620843", "GSM1620844", "GSM1620845", "GSM1620846", "GSM1620847", "GSM1620848", "GSM1620849", "GSM1620850", "GSM1620851", "GSM1620852", "GSM1620853", "GSM1620854", "GSM1620855", "GSM1620856", "GSM1620857", "GSM1620858", "GSM1620859", "GSM1620860", "GSM1620861", "GSM1620862", "GSM1620863", "GSM1620864", "GSM1620865", "GSM1620866", "GSM1620867", "GSM1620868", "GSM1620869", "GSM1620870", "GSM1620871", "GSM1620872", "GSM1620873", "GSM1620874", "GSM1620875", "GSM1620876", "GSM1620877", "GSM1620878", "GSM1620879", "GSM1620880", "GSM1620881", "GSM1620882", "GSM1620883", "GSM1620884", "GSM1620885", "GSM1620886", "GSM1620887", "GSM1620888", "GSM1620889", "GSM1620890", "GSM1620891", "GSM1620892", "GSM1620893", "GSM1620894", "GSM1620895", "GSM1620896", "GSM1620897", "GSM1620898", "GSM1620899", "GSM1620900", "GSM1620901", "GSM1620902", "GSM1620903", "GSM1620904", "GSM1620905", "GSM1620906", "GSM1620907", "GSM1620908", "GSM1620909", "GSM1620910", "GSM1620911", "GSM1620912", "GSM1620913", "GSM1620914", "GSM1620915", "GSM1620916", "GSM1620917"], "sample_count": 99, "group_sizes": null, "group_sizes_basis": null, "platform": "570", "taxon": "Homo sapiens", "title": "A Whole Blood Molecular Signature for the Identification of Acute Myocardial Infarction Without Relying Upon Myonecrosis (microarray)", "resolution_checked_at": "2026-11-01T15:39:55Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE48060 not downloaded", "asset:data:geo:GSE48060 licence not read", "asset:data:geo:GSE60993 not downloaded", "asset:data:geo:GSE60993 licence not read", "asset:data:geo:GSE61144 not downloaded", "asset:data:geo:GSE61144 licence not read", "asset:data:geo:GSE66360 not downloaded", "asset:data:geo:GSE66360 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": "Furthermore, the three data sets were merged into a metadata cohort and the batch effects were preprocessed and adjusted by the ComBat function of the SVA package (14). In addition, the “limma” of R software was used to perform background correction and array normalization. Meanwhile, DEGs between patients with AMI and healthy controls were identified using the “limma” package (15)."}}, {"id": "method-2", "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"}}, {"id": "method-3", "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": "GO enrichment"}}], "targets": [{"id": "target-published-estimate", "claim_id": "claim-target-result", "operation": "build_graph", "expected_value": 95.0, "unit": "per cent", "tolerance": null, "state": "complete", "note": "The published estimate as the reader quoted it from the paper. No tolerance is published because the paper states none."}, {"id": "target-published-estimate-p-value", "claim_id": "claim-target-result", "operation": "build_graph", "expected_value": 0.0, "unit": "p-value", "tolerance": null, "state": "complete", "note": "The threshold the paper applied to its own result, as the paper states it. A run that reproduces the effect is judged against this threshold."}], "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": ["GSE48060", "GSE60993", "GSE66360", "GSE61144"], "resolved_accessions": ["GSE48060", "GSE60993", "GSE66360", "GSE61144"], "statements": ["Publicly available datasets were analyzed in this study. This data can be found here: All the raw data used in this study are available in the public GEO database (https://www.ncbi.nlm.nih.gov/geo/; Accession numbers: GSE66360, GSE48060, GSE60993, and GSE61144)."], "basis": "The paper names GSE48060, GSE60993, GSE66360, GSE61144 in Gene Expression Omnibus (GEO) database 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:35463752", "data:geo:GSE48060"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE48060"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T17:43:20Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 8640, "completion_tokens": 1681, "cached_tokens": 188}}, "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:35463752", "type": "paper", "label": "pmid:35463752"}, {"id": "source:pmid:35463752", "type": "manuscript", "label": "PMID 35463752 full text"}, {"id": "data:geo:GSE48060", "type": "dataset", "label": "GEO GSE48060"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:35463752", "type": "paper", "label": "pmid:35463752"}, {"id": "source:pmid:35463752", "type": "manuscript", "label": "PMID 35463752 full text"}, {"id": "data:geo:GSE48060", "type": "dataset", "label": "GEO GSE48060"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-35463752", "type": "extraction", "used": ["source:pmid:35463752"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE48060", "type": "accession_resolution", "used": ["data:geo:GSE48060"], "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:35463752", "parent_hash": "sha256:dece5f17384b81aacd7a57a45cb6940c08984308c466b95b87f46f1a4ef9b203", "fork_of": null, "generation": 4}, "gaps": ["asset:data:geo:GSE48060 licence not read", "asset:data:geo:GSE48060 not downloaded", "asset:data:geo:GSE60993 licence not read", "asset:data:geo:GSE60993 not downloaded", "asset:data:geo:GSE61144 licence not read", "asset:data:geo:GSE61144 not downloaded", "asset:data:geo:GSE66360 licence not read", "asset:data:geo:GSE66360 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": ["Furthermore, the three data sets were merged into a metadata cohort and the batch effects were preprocessed and adjusted by the ComBat function of the SVA package (14). In addition, the “limma” of R software was used to perform background correction and array normalization. Meanwhile, DEGs between patients with AMI and healthy controls were identified using the “limma” package (15)."], "order_basis": "analysis_only", "id": "assay:1"}]}