Human Plasma Transcriptome Implicates Dysregulated S100A12 Expression: A Strong, Early-Stage Prognostic Factor in ST-Segment Elevated Myocardial Infarction: Bioinformatics Analysis and Experimental Verification.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35722095", "kind": "science-record", "revision": 4, "content_hash": "sha256:ed4d7e658b5e011a53529d978c1890645d0156d63bed5bbe3782f8cc9c86ad16", "created_at": "2026-11-01T18:14:44Z", "updated_at": "2026-11-01T22:06:50Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Human Plasma Transcriptome Implicates Dysregulated S100A12 Expression: A Strong, Early-Stage Prognostic Factor in ST-Segment Elevated Myocardial Infarction: Bioinformatics Analysis and Experimental Verification.", "doi": "10.3389/fcvm.2022.874436", "pmid": "35722095", "authors": ["Hu Zhai", "Lei Huang", "Yijie Gong", "Yingwu Liu", "Yu Wang", "Bojiang Liu", "Xiandong Li", "Chunyan Peng", "Tong Li"], "journal": "Frontiers in cardiovascular medicine", "year": 2022, "abstract": "The ability of blood transcriptome analysis to identify dysregulated pathways and outcome-related genes following myocardial infarction remains unknown. Two gene expression datasets (GSE60993 and GSE61144) were downloaded from Gene Expression Omnibus (GEO) Datasets to identify altered plasma transcriptomes in patients with ST-segment elevated myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention. GEO2R, Gene Ontology/Kyoto Encyclopedia of Genes and Genomes annotations, protein-protein interaction analysis, etc., were adopted to determine functional roles and regulatory networks of differentially expressed genes (DEGs). Dysregulated expressomes were verified at transcriptional and translational levels by analyzing the GSE49925 dataset and our own samples, respectively. A total of 91 DEGs were identified in the discovery phase, consisting of 15 downregulated genes and 76 upregulated genes. Two hub modules consisting of 12 hub genes were identified. In the verification phase, six of the 12 hub genes exhibited the same variation patterns at the transcriptional level in the GSE49925 dataset. Among them, S100A12 was shown to have the best discriminative performance for predicting in-hospital mortality and to be the only independent predictor of death during follow-up. Validation of 223 samples from our center showed that S100A12 protein level in plasma was significantly lower among patients who survived to discharge, but it was not an independent predictor of survival to discharge or recurrent major adverse cardiovascular events after discharge. In conclusion, the dysregulated expression of plasma S100A12 at the transcriptional level is a robust early prognostic factor in patients with STEMI, while the discrimination power of the protein level in plasma needs to be further verified by large-scale, prospective, international, multicenter studies.", "claims": [{"id": "claim-method", "statement": "Using median values as the cutoff values for the markers, receiver operating characteristic curve analysis was used to measure their discriminative performances for promising early biomarkers of mortality. Both univariate (unadjusted) and multivariate (adjusted) logistic regression were performed to determine independent clinical factors and biomarkers that were associated with death. A multivariate regression analysis was performed via a Cox proportional hazard regression model, aiming to identify factors that might independently influence survival.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35722095", "locator": "sections[10] \"Statistical Analyses\" paragraph 2", "quote": "Using median values as the cutoff values for the markers, receiver operating characteristic curve analysis was used to measure their discriminative performances for promising early biomarkers of mortality."}, {"asset": "source:pmid:35722095", "locator": "sections[10] \"Statistical Analyses\" paragraph 2", "quote": "Both univariate (unadjusted) and multivariate (adjusted) logistic regression were performed to determine independent clinical factors and biomarkers that were associated with death."}, {"asset": "source:pmid:35722095", "locator": "sections[10] \"Statistical Analyses\" paragraph 2", "quote": "A multivariate regression analysis was performed via a Cox proportional hazard regression model, aiming to identify factors that might independently influence survival."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated and/or analyzed during the current study are available in the GEO Datasets [GSE61144 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE61144), GSE60993 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE60993), and GSE49925 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE49925)]. As open access to individual-level data was not specified in the original application approved by the ethics committee, the underlying data are only available upon reasonable request. Please contact the corresponding authors.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35722095", "locator": "sections[22] \"Data Availability Statement\" paragraph 2", "quote": "The datasets generated and/or analyzed during the current study are available in the GEO Datasets [GSE61144 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE61144), GSE60993 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE60993), and GSE49925 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE49925)]. As open access to individual-level data was not specified in the original application approved by the ethics committee, the underlying data are only available upon reasonable request. Please contact the corresponding authors."}], "state": "complete"}, {"id": "claim-target-result", "statement": "S100A12 showed the best discriminative performance, with an area under the curve of 0.768 (95% CI, 0.588–0.949; P = 0.032), and the sensitivity and specificity were 83.3 and 69.1%, respectively.", "kind": "quantitative", "target": {"value": 0.032, "unit": "p-value", "tolerance": null, "source": "Overall, based on the median values as the cutoff value for the markers, we found that S100A12 showed the best discriminative performance, with an area under the curve of 0.768 (95% CI, 0.588–0.949; P = 0.032), and the sensitivity and specificity were 83.3 and 69.1%, respectively (Supplementary Table 7)."}, "evidence": [{"asset": "source:pmid:35722095", "locator": "sections[18] \"Discriminative Performance of the Early Biomarkers to Mortality\" paragraph 2", "quote": "Overall, based on the median values as the cutoff value for the markers, we found that S100A12 showed the best discriminative performance, with an area under the curve of 0.768 (95% CI, 0.588–0.949; P = 0.032), and the sensitivity and specificity were 83.3 and 69.1%, respectively (Supplementary Table 7)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "S100A12 showed the best discriminative performance, with an area under the curve of 0.768 (95% CI, 0.588–0.949; P = 0.032), and the sensitivity and specificity were 83.3 and 69.1%, 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:35722095"], "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. 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"title": "Gene expression profiles associated with acute myocardial infarction and risk of cardiovascular death", "resolution_checked_at": "2026-11-01T18:06:44Z", "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": "The datasets generated and/or analyzed during the current study are available in the GEO Datasets [GSE61144 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE61144), GSE60993 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE60993), and GSE49925 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE49925)]. As open access to individual-level data was not specified in the original application approved by the ethics committee, the underlying data are only available upon reasonable request. Please contact the corresponding authors.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets generated and/or analyzed during the current study are available in the GEO Datasets [GSE61144 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE61144), GSE60993 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE60993), and GSE49925 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE49925)]. As open access to individual-level data was not specified in the original application approved by the ethics committee, the underlying data are only available upon reasonable request. 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A multivariate regression analysis was performed via a Cox proportional hazard regression model, aiming to identify factors that might independently influence survival."}}, {"id": "method-2", "family": "survival_analysis", "specific": "cox_proportional_hazards", "label": "Survival analysis", "dsl_operations": ["cox_ph"], "missing_operations": [], "expressible": true, "parameters": {"hazard_ratio": "low-up, with a value of HR of 4"}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.8, "band": "high", "quote": "Cox regression"}}, {"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": 0.7, "band": "medium", "quote": "protein-protein interaction"}}], "targets": [{"id": "target-published-estimate", "claim_id": "claim-target-result", "operation": "build_graph", "expected_value": 0.032, "unit": "p-value", "tolerance": null, "state": "complete", "note": "The threshold the paper applied to its own result, as the paper states it. 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The last follow-up date for all these part", "their S100A12 plasma levels were detected by enzyme-linked immunosorbent assay (ELISA). The last follow-up date for all the"], "order_basis": "paper_text_order:4360", "id": "assay:2"}, {"role": "analysis", "definition_id": "regression_modelling", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["glm_logit_fit"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Using median values as the cutoff values for the markers, receiver operating characteristic curve analysis was used to measure their discriminative performances for promising early biomarkers of mortality. Both univariate (unadjusted) and multivariate (adjusted) logistic regression were performed to determine independent clinical factors and biomarkers that were associated with death. 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