Integrating scRNA-seq to explore novel macrophage infiltration-associated biomarkers for diagnosis of heart failure.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37974098", "kind": "science-record", "revision": 4, "content_hash": "sha256:3c21e2817654630f3602e0097b8c8981eb1e1548ae7c8063f3ee73fdd94c6f27", "created_at": "2026-11-01T19:02:53Z", "updated_at": "2026-11-01T22:10:40Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrating scRNA-seq to explore novel macrophage infiltration-associated biomarkers for diagnosis of heart failure.", "doi": "10.1186/s12872-023-03593-1", "pmid": "37974098", "authors": ["Shengnan Li", "Tiantian Ge", "Xuan Xu", "Liang Xie", "Sifan Song", "Runqian Li", "Hao Li", "Jiayi Tong"], "journal": "BMC cardiovascular disorders", "year": 2023, "abstract": "OBJECTIVE: Inflammation and immune cells are closely intertwined mechanisms that contribute to the progression of heart failure (HF). Nonetheless, there is a paucity of information regarding the distinct features of dysregulated immune cells and efficient diagnostic biomarkers linked with HF. This study aims to explore diagnostic biomarkers related to immune cells in HF to gain new insights into the underlying molecular mechanisms of HF and to provide novel perspectives for the detection and treatment of HF.\nMETHOD: The CIBERSORT method was employed to quantify 22 types of immune cells in HF and normal subjects from publicly available GEO databases (GSE3586, GSE42955, GSE57338, and GSE79962). Machine learning methods were utilized to screen for important cell types. Single-cell RNA sequencing (GSE145154) was further utilized to identify important cell types and hub genes. WGCNA was employed to screen for immune cell-related genes and ultimately diagnostic models were constructed and evaluated. To validate these predictive results, blood samples were collected from 40 normal controls and 40 HF patients for RT-qPCR analysis. Lastly, key cell clusters were divided into high and low biomarker expression groups to identify transcription factors that may affect biomarkers.\nRESULTS: The study found a noticeable difference in immune environment between HF and normal subjects. Macrophages were identified as key immune cells by machine learning. Single-cell analysis further showed that macrophages differed dramatically between HF and normal subjects. This study revealed the existence of five subsets of macrophages that have different differentiation states. Based on module genes most relevant to macrophages, macrophage differentiation-related genes (MDRGs), and DEGs in HF and normal subjects from GEO datasets, four genes (CD163, RNASE2, LYVE1, and VSIG4) were identified as valid diagnostic markers for HF. Ultimately, a diagnostic model containing two hub genes was constructed and then validated with a validation dataset and clinical samples. In addition, key transcription factors driving or maintaining the biomarkers expression programs were identified.\nCONCLUSION: The analytical results and diagnostic model of this study can assist clinicians in identifying high-risk individuals, thereby aiding in guiding treatment decisions for patients with HF.", "claims": [{"id": "claim-method", "statement": "On the basis of the union of LASSO and random forest algorithms, four immune cell types were identified as closely associated with HF: neutrophils, CD4 naive T cells, M1 macrophages, and M2 macrophages (Fig. 3B-D).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37974098", "locator": "sections[20] \"Identification of key immune cell types associated with HF\" paragraph 2", "quote": "On the basis of the union of LASSO and random forest algorithms, four immune cell types were identified as closely associated with HF: neutrophils, CD4 naive T cells, M1 macrophages, and M2 macrophages (Fig. 3B-D)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets used for analysis in this study (GSE3586, GSE42955, GSE57338, GSE79962, GSE116250, and GSE145154) were derived from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/), a publicly available repository.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37974098", "locator": "sections[30] \"Data availability statement\" paragraph 2", "quote": "The datasets used for analysis in this study (GSE3586, GSE42955, GSE57338, GSE79962, GSE116250, and GSE145154) were derived from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/), a publicly available repository."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A logistic regression diagnostic model containing CD163 and RNASE2 achieved AUCs of 0.919 and 0.876 in the training and validation datasets, respectively.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37974098", "locator": "sections[24] \"Construction and verification of the prognostic risk model\" paragraph 2", "quote": "According to ROC analysis, these hub genes were considerably sensitive and specific regarding diagnosing HF, with AUCs were 0.919 and 0.876 in the training and validation datasets, respectively (Fig. 7H)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "A logistic regression diagnostic model containing CD163 and RNASE2 achieved AUCs of 0.919 and 0.876 in the training and validation datasets, 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:37974098"], "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:GSE116250"], "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:GSE116250"], "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:37974098", "role": "manuscript", "format": "xml", "media_type": "application/xml", 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GSE57338, GSE79962, GSE116250, and GSE145154) were derived from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/), a publicly available repository.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets used for analysis in this study (GSE3586, GSE42955, GSE57338, GSE79962, GSE116250, and GSE145154) were derived from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/), a publicly available repository.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM3219558", "GSM3219559", "GSM3219560", "GSM3219561", "GSM3219562", "GSM3219563", "GSM3219564", "GSM3219565", "GSM3219566", "GSM3219567", "GSM3219568", "GSM3219569", "GSM3219570", "GSM3219571", "GSM3219572", "GSM3219573", "GSM3219574", "GSM3219575", "GSM3219576", "GSM3219577", "GSM3219578", "GSM3219579", "GSM3219580", "GSM3219581", "GSM3219582", "GSM3219583", "GSM3219584", "GSM3219585", "GSM3219586", 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"taxon": "Homo sapiens", "title": "Expression data from human heart", "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:GSE57338", "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=GSE57338", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE57338", "version": null, "note": "The datasets used for analysis in this study (GSE3586, GSE42955, GSE57338, GSE79962, GSE116250, and GSE145154) were derived from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/), a publicly available repository.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets used for analysis in this study (GSE3586, 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