Pyroptosis-related signatures predict immune characteristics and prognosis in IPF.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38192798", "kind": "science-record", "revision": 2, "content_hash": "sha256:c6b7f23584039f4938936a767f0745f5e39835aba96139a8d91c6e26fef48e06", "created_at": "2026-11-01T19:20:06Z", "updated_at": "2026-11-01T21:32:15Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Pyroptosis-related signatures predict immune characteristics and prognosis in IPF.", "doi": "10.1016/j.heliyon.2023.e23683", "pmid": "38192798", "authors": ["Yijun He", "Tingting Yao", "Yan Zhang", "Lingzhi Long", "Guoliang Jiang", "Xiangyu Zhang", "Xin Lv", "Yuanyuan Han", "Xiaoyun Cheng", "Mengyu Li", "Mao Jiang", "Zhangzhe Peng", "Lijian Tao", "Jie Meng"], "journal": "Heliyon", "year": 2024, "abstract": "The purpose of this work was to use integrated bioinformatics analysis to screen for pyroptosis-related genes (PRGs) and possible immunological phenotypes linked to the development and course of IPF. Transcriptome sequencing datasets GSE70866, GSE47460 and GSE150910 were obtained from GEO database. From the GSE70866 database, 34 PRGs with differential expression were found in IPF as compared to healthy controls. In addition, a diagnostic model containing 4 genes PRGs (CAMP, MKI67, TCEA3 and USP24) was constructed based on LASSO logistic regression. The diagnostic model showed good predictive ability to differentiate between IPF and healthy, with ROC-AUC ranging from 0.910 to 0.997 in GSE70866 and GSE150910 datasets. Moreover, based on a combined cohort of the Freiburg and the Siena cohorts from GSE70866 dataset, we identified ten PRGs that might predict prognosis for IPF. We constructed a prognostic model that included eight PRGs (CLEC5A, TREM2, MMP1, IRF2, SEZ6L2, ADORA3, NOS2, USP24) by LASSO Cox regression and validated it in the Leuven cohort. The risk model divided IPF patients from the combined cohort into high-risk and low-risk subgroups. There were significant differences between the two subgroups in terms of IPF survival and GAP stage. There is a close correlation between leukocyte migration, plasma membrane junction, and poor prognosis in a high-risk subgroup. Furthermore, a high-risk score was associated with more plasma cells, activated NK cells, monocytes, and activated mast cells. Additionally, we identified HDAC inhibitors in the cMAP database that might be therapeutic for IPF. To summarize, pyroptosis and its underlying immunological features are to blame for the onset and progression of IPF. PRG-based predictive models and drugs may offer new treatment options for IPF.", "claims": [{"id": "claim-method", "statement": "Based on LASSO Cox regression analysis, an eight PRG prediction model was derived (Fig. 4D and E).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38192798", "locator": "sections[15] \"Construction of a PRGs-related prognostic model in IPF\" paragraph 2", "quote": "Based on LASSO Cox regression analysis, an eight PRG prediction model was derived (Fig. 4D and E)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study are available in online repositories. Please refer to the article/supplementary material for the name of the repository(ies) and accession number(s).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38192798", "locator": "sections[21] \"Data availability statement\" paragraph 2", "quote": "The datasets presented in this study are available in online repositories. Please refer to the article/supplementary material for the name of the repository(ies) and accession number(s)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "We constructed a prognostic model that included eight PRGs (CLEC5A, TREM2, MMP1, IRF2, SEZ6L2, ADORA3, NOS2, USP24) by LASSO Cox regression and validated it in the Leuven cohort.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38192798", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "We constructed a prognostic model that included eight PRGs (CLEC5A, TREM2, MMP1, IRF2, SEZ6L2, ADORA3, NOS2, USP24) by LASSO Cox regression and validated it in the Leuven cohort."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "We constructed a prognostic model that included eight PRGs (CLEC5A, TREM2, MMP1, IRF2, SEZ6L2, ADORA3, NOS2, USP24) by LASSO Cox regression and validated it in the Leuven cohort.", "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:38192798"], "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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