Evaluating the role of IER3+ macrophages in the prognosis of liver fibrosis by bulk and single-cell transcriptional analyses.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40635861", "kind": "science-record", "revision": 3, "content_hash": "sha256:cf01ee04cba0a9d8d82a09f73a0e73d7d2b525d9db66eaa110abea441cfd14ec", "created_at": "2026-11-01T20:30:44Z", "updated_at": "2026-11-01T22:14:20Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Evaluating the role of IER3+ macrophages in the prognosis of liver fibrosis by bulk and single-cell transcriptional analyses.", "doi": "10.1016/j.iliver.2024.100132", "pmid": "40635861", "authors": ["Ling Chu", "Junbo Zhang", "Ruoyu Meng", "Quan Zhuang"], "journal": "iLIVER", "year": 2024, "abstract": "Background and aims: Liver fibrosis (LF) is the common pathological process of almost all liver diseases, but the pathogenesis is extremely complicated and has not been fully clarified. Therefore, we want to explore LF's complex pathogenesis and identify key genetic markers that can predict LF prognosis.\nMethods: From Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/), we contained datasets GSE84044 and GSE130970. And involved GSE15654 whose survival data were for the validation of our model's prognostic value. Then, Bulk RNA sequencing was used to establish a LF-related prognostic model and to determine key genes.The key genes were later analyzed using single-cell RNA sequencing (scRNA-seq) and the role of macrophages in LF was investigated.\nResults: We established a comprehensive LF-related prognostic model (FMSig, i.e., fibrosis macrophage-related prognostic signature) containing four genes (IER3, AKR1B10, ADCY1, and PGM1). FMSig can distinguish between survival outcomes in dataset GSE15654. IER3 showed a higher hazard ratio than the other three FMSig genes. Moreover, further scRNA-seq analysis showed that IER3 is highly expressed in myeloid cells while the other three genes were rarely expressed in six immune cell types and other cell clusters. After reclustering the myeloid cells into seven clusters, we found that IER3 was highly expressed in myeloid cell cluster 3, shown to be related to anti-inflammation and lipid metabolism functions. Based on pseudotime analysis, we suggest that myeloid cell type 3 is transformed from myeloid cell type 0 in the liver cirrhosis microenvironment. On immunohistochemical staining, LF showed significantly higher IER3 expression than healthy controls.\nConclusion: This study established a predictive model, FMSig, to evaluate LF prognosis, and showed that IER3 and macrophages are related to LF.", "claims": [{"id": "claim-method", "statement": "In GSE15654, potential prognostic factors were screened using univariate Cox analysis, and factors with p < 0.05 were retained. Then, the least absolute shrinkage and selection operator (LASSO) was applied to retain valuable variables as key genes through ten cross-validations and 1000 iterations, achieving a high-dimensional prediction and avoiding overfitting [13]. Machine learning methods were applied to select optimal combinations from critical gene lists based on multivariate Cox regression models, and forward and backward variable selection was performed with the R package “MASS”.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40635861", "locator": "sections[8] \"Identification of prognostic genes and construction of prognostic models\" paragraph 2", "quote": "In GSE15654, potential prognostic factors were screened using univariate Cox analysis, and factors with p < 0.05 were retained. Then, the least absolute shrinkage and selection operator (LASSO) was applied to retain valuable variables as key genes through ten cross-validations and 1000 iterations, achieving a high-dimensional prediction and avoiding overfitting [13]. Machine learning methods were applied to select optimal combinations from critical gene lists based on multivariate Cox regression models, and forward and backward variable selection was performed with the R package “MASS”."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Not applicable.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40635861", "locator": "sections[30] \"Data availability statement\" paragraph 2", "quote": "Not applicable."}], "state": "complete"}, {"id": "claim-target-result", "statement": "We established a comprehensive LF-related prognostic model (FMSig, i.e., fibrosis macrophage-related prognostic signature) containing four genes (IER3, AKR1B10, ADCY1, and PGM1).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40635861", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "We established a comprehensive LF-related prognostic model (FMSig, i.e., fibrosis macrophage-related prognostic signature) containing four genes (IER3, AKR1B10, ADCY1, and PGM1)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "We established a comprehensive LF-related prognostic model (FMSig, i.e., fibrosis macrophage-related prognostic signature) containing four genes (IER3, AKR1B10, ADCY1, and PGM1).", "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:40635861"], "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:GSE130970"], "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:GSE130970"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40635861", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1016/j.iliver.2024.100132", "sha256": 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Then, Bulk RNA sequencing was used to establish a LF-related prognosti", "genes were later analyzed using single-cell RNA sequencing (scRNA-seq) and the role of macrophages in L", "ation and projection; scRNA-seq, single-cell RNA sequencing.Fig. 5 Preliminary validation in LF patients"], "order_basis": "paper_text_order:624", "id": "assay:1"}, {"role": "data_generation", "definition_id": "scrna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "unsupported", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["enes.The key genes were later analyzed using single-cell RNA sequencing (scRNA-seq) and the role of macrophages in L", "r analyzed using single-cell RNA sequencing (scRNA-seq) and the role of macrophages in LF was inves", "e other three FMSig genes. Moreover, further scRNA-seq analysis showed that IER3 is highly expresse", "old approximation and projection; scRNA-seq, single-cell RNA sequencing.Fig. 5 Preliminary validation in LF patients"], "order_basis": "paper_text_order:757", "id": "assay:2"}, {"role": "data_generation", "definition_id": "ihc", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["in the liver cirrhosis microenvironment. On immunohistochemical staining, LF showed significantly higher IER", "ion levels in healthy and fibrotic livers by immunohistochemical staining using ten HC and 14 LF samples. Exc", "itive while HC samples were IER3 negative on immunohistochemical staining (Fig. 8C, D). To show the expressio", "sion of IER3 more clearly, we quantified the immunohistochemical images using ImageJ, and as expected, LF sam"], "order_basis": "paper_text_order:1713", "id": "assay:3"}, {"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["cox_ph"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["In GSE15654, potential prognostic factors were screened using univariate Cox analysis, and factors with p < 0.05 were retained. Then, the least absolute shrinkage and selection operator (LASSO) was applied to retain valuable variables as key genes through ten cross-validations and 1000 iterations, achieving a high-dimensional prediction and avoiding overfitting [13]. 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