Immune-related potential biomarkers and therapeutic targets in coronary artery disease.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36684561", "kind": "science-record", "revision": 4, "content_hash": "sha256:e2dbb6533fccaba9a96238b74062ac58391869a17b02299358d947e54b8883ac", "created_at": "2026-11-01T19:01:13Z", "updated_at": "2026-11-01T22:51:04Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Immune-related potential biomarkers and therapeutic targets in coronary artery disease.", "doi": "10.3389/fcvm.2022.1055422", "pmid": "36684561", "authors": ["Chaosheng Liu", "Jifeng Liu", "Yunshu Zhang", "Xi Wang", "Yue Guan"], "journal": "Frontiers in cardiovascular medicine", "year": 2022, "abstract": "Background: Coronary artery disease (CAD) is a complex illness with unknown pathophysiology. Peripheral biomarkers are a non-invasive method required to track the onset and progression of CAD and have unbeatable benefits in terms of early identification, prognostic assessment, and categorization of the diagnosis. This study aimed to identify and validate the diagnostic and therapeutic potential of differentially expressed immune-related genes (DE-IRGs) in CAD, which will aid in improving our knowledge on the etiology of CAD and in forming genetic predictions.\nMethods: First, we searched coronary heart disease in the Gene Expression Omnibus (GEO) database and identified GSE20680 (CAD = 87, Normal = 52) as the trial set and GSE20681 (CAD = 99, Normal = 99) as the validation set. Functional enrichment analysis using protein-protein interactions (PPIs), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) was carried out on the identified differentially expressed genes. Optimal feature genes (OFGs) were generated using the support vector machine recursive feature elimination algorithm and the least absolute shrinkage and selection operator (LASSO) algorithm. Furthermore, immune infiltration in CAD patients and healthy controls was compared using CIBERSORT, and the relationship between immune cells and OFGs was examined. In addition, we constructed potential targeted drugs for this model through the Drug-Gene Interaction database (DGIdb) database. Finally, we verify the expression of S100A8-dominated OFGs in the GSE20681 dataset to confirm the universality of our study.\nResults: We identified the ten best OFGs for CAD from the DE-IRGs. Functional enrichment analysis showed that these marker genes are crucial for receptor-ligand activity, signaling receptor activator activity, and positive control of the response to stimuli from the outside world. Additionally, CIBERSORT revealed that S100A8 could be connected to alterations in the immune microenvironment in CAD patients. Furthermore, with the help of DGIdb and Cytoscape, a total of 64 medicines that target five marker genes were subsequently discovered. Finally, we verified the expression of the OFGs genes in the GSE20681 dataset between CAD patients and normal patients and found that there was also a significant difference in the expression of S100A8.\nConclusion: We created a 10-gene immune-related prognostic model for CAD and confirmed its validity. The model can identify potential biomarkers for CAD prediction and more accurately gauge the progression of the disease.", "claims": [{"id": "claim-method", "statement": "We screened for DE-IRGs between CAD and controls using the “LIMMA” package in the R software using the combined datasets (P < 0.05).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36684561", "locator": "sections[4] \"2.2. Identification of DE-IRGs\" paragraph 2", "quote": "We screened for DE-IRGs between CAD and controls using the “LIMMA” package in the R software using the combined datasets (P < 0.05)."}], "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 below: https://www.ncbi.nlm.nih.gov/geo/, GSE20680 and https://www.ncbi.nlm.nih.gov/geo/, GSE20681.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36684561", "locator": "sections[20] \"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 below: https://www.ncbi.nlm.nih.gov/geo/, GSE20680 and https://www.ncbi.nlm.nih.gov/geo/, GSE20681."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Using the “glm” package, we created a logistic regression model based on the 10 OFGs mentioned above, which could distinguish between normal and CAD samples with an AUC of 0.889 [95% confidence interval (CI): 0.830–0.938], according to the ROC curve (Figure 3F).", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "Using the “glm” package, we created a logistic regression model based on the 10 OFGs mentioned above, which could distinguish between normal and CAD samples with an AUC of 0.889 [95% confidence interval (CI): 0.830–0.938], according to the ROC curve (Figure 3F)."}, "evidence": [{"asset": "source:pmid:36684561", "locator": "sections[13] \"3.3. Identification of OFGs for CAD\" paragraph 4", "quote": "Using the “glm” package, we created a logistic regression model based on the 10 OFGs mentioned above, which could distinguish between normal and CAD samples with an AUC of 0.889 [95% confidence interval (CI): 0.830–0.938], according to the ROC curve (Figure 3F)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Using the “glm” package, we created a logistic regression model based on the 10 OFGs mentioned above, which could distinguish between normal and CAD samples with an AUC of 0.889 [95% confidence interval (CI): 0.830–0.938], according to the ROC curve (Figure 3F).", "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:36684561"], "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:GSE20680"], "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:GSE20680"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36684561", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fcvm.2022.1055422", "sha256": "sha256:c43f41f4b955b7bb6408ff65195521d2fbc3e597cf3fda4407fc47317e474304", "bytes": 43562, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fcvm.2022.1055422", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9853173", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE20680", "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=GSE20680", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE20680", "version": null, "note": "The datasets presented in this study can be found in online repositories. 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No tolerance is published because the paper states none."}], "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": ["GSE20680", "GSE20681"], "resolved_accessions": ["GSE20680", "GSE20681"], "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 below: https://www.ncbi.nlm.nih.gov/geo/, GSE20680 and https://www.ncbi.nlm.nih.gov/geo/, GSE20681."], "basis": "The paper names GSE20680, GSE20681 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:36684561", "data:geo:GSE20680"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE20680"]}, "gaps": []}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T18:24:05Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 12614, "completion_tokens": 1693, "cached_tokens": 256}}, "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:36684561", "type": "paper", "label": "pmid:36684561"}, {"id": "source:pmid:36684561", "type": "manuscript", "label": "PMID 36684561 full text"}, {"id": "data:geo:GSE20680", "type": "dataset", "label": "GEO GSE20680"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:36684561", "type": "paper", "label": "pmid:36684561"}, {"id": "source:pmid:36684561", "type": "manuscript", "label": "PMID 36684561 full text"}, {"id": "data:geo:GSE20680", "type": "dataset", "label": "GEO GSE20680"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-36684561", "type": "extraction", "used": ["source:pmid:36684561"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE20680", "type": "accession_resolution", "used": ["data:geo:GSE20680"], "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:36684561", "parent_hash": "sha256:9577d3aaa293364b88f7c7d3f726c616490f5a0728e60f0d0601b81c15b0552a", "fork_of": null, "generation": 3}, "gaps": ["asset:data:geo:GSE20680 licence not read", "asset:data:geo:GSE20680 not downloaded", "asset:data:geo:GSE20681 licence not read", "asset:data:geo:GSE20681 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": ["We screened for DE-IRGs between CAD and controls using the “LIMMA” package in the R software using the combined datasets (P < 0.05)."], "order_basis": "analysis_only", "id": "assay:1"}]}