Exploring the role of PANoptosis in idiopathic pulmonary fibrosis based on scRNA-seq and bulk-seq.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41369928", "kind": "science-record", "revision": 3, "content_hash": "sha256:1d9a9ac9e1e64afb9ceeddd572086ec71c150337061f67d7848398d0c16427f5", "created_at": "2026-11-01T20:31:23Z", "updated_at": "2026-11-01T22:15:09Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Exploring the role of PANoptosis in idiopathic pulmonary fibrosis based on scRNA-seq and bulk-seq.", "doi": "10.1007/s10238-025-01990-8", "pmid": "41369928", "authors": ["Zhihua Wang", "Quanlei Li", "Yuntian Chen", "Lixing Gan", "Lifen Yuan", "Juan Liu", "Yu Xie", "Tianyu Zhou", "Xiahui Ge"], "journal": "Clinical and experimental medicine", "year": 2025, "abstract": "PANoptosis is a novel form of programmed cell death that integrates pyroptosis, apoptosis, and necroptosis; in this study, we combined the single-cell RNA sequencing (scRNA-seq) dataset GSE227136 with transcriptome data to elucidate its role in idiopathic pulmonary fibrosis (IPF) pathogenesis. PANoptosis-related genes were compiled from GeneCards and published literature. Consensus clustering was used to identify distinct PANoptosis-related clusters of IPF in the GEO dataset based on filtered PANoptosis-related differentially expressed genes (PRDEGs). Specific hub genes were identified using weighted gene co-expression network analysis (WGCNA) and two machine learning methodologies, which were used to develop predictive models. The inflammatory programmed cell death score (PANoptosis score, Ps) for each IPF patient was calculated based on nine PRDEGs, followed by analyses of these PRDEGs' expression differences and their ROC curves. PRDEG expression was confirmed in murine pulmonary tissues using quantitative real-time polymerase chain reaction (qRT-PCR). We successfully identified nine PRDEGs and two distinct PANoptosis-related clusters with these PRDEGs. Using WGCNA and machine learning approaches, we constructed a nomogram with robust predictive capacity for diagnosis of IPF. In addition, immune infiltration analysis among different molecular groups and single cell analysis revealed that increased PANoptosis activity was closely associated with immune activation. Finally, results from qRT-PCR showed a significant increase in the expression of MLKL and AIM2 in the lung tissue of the IPF animal model.", "claims": [{"id": "claim-data-access", "statement": "The raw data utilized for this research are available through downloading datasets from [https://www.ncbi.nlm.nih.gov/geo/](https:/www.ncbi.nlm.nih.gov/geo), including GSE10667, GSE24206, GSE53845 and GSE227136. The original contributions presented in the study are included in the article/supplementary material. Additional inquiries should be made to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41369928", "locator": "sections[36] \"Data availability\" paragraph 2", "quote": "The raw data utilized for this research are available through downloading datasets from [https://www.ncbi.nlm.nih.gov/geo/](https:/www.ncbi.nlm.nih.gov/geo), including GSE10667, GSE24206, GSE53845 and GSE227136. The original contributions presented in the study are included in the article/supplementary material. Additional inquiries should be made to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The nine-PRDEG logistic regression diagnostic model showed strong predictive performance between normal and IPF groups, with AUC > 0.9.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:41369928", "locator": "sections[21] \"Nomogram construction and clinical evaluation of nine PRDEGs\" paragraph 2", "quote": "The results showed that the diagnostic model exhibited strong predictive performance, achieving a high area under the curve (AUC > 0.9) between the normal and IPF group."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The nine-PRDEG logistic regression diagnostic model showed strong predictive performance between normal and IPF groups, with AUC > 0.9.", "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:41369928"], "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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PRDEG expression was confirmed in murine pulmonary tissues using quantitative real-ti", "r ROC curves. PRDEG expression was confirmed in murine pulmonary tissues using quantitative real-ti"], "order_basis": "paper_text_order:1032", "id": "assay:4"}, {"role": "analysis", "definition_id": "group_comparison", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 0.8, "evidence": ["Mann-Whitney"], "order_basis": "consumes_earlier_output", "id": "assay:5"}]}