Exploring PANoptosis in breast cancer 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:37455906", "kind": "science-record", "revision": 2, "content_hash": "sha256:9abfecbf5c9cb4a7bad258a243cf467b8e807986f3d3e54437a1ccdc27714f4f", "created_at": "2026-11-01T19:02:13Z", "updated_at": "2026-11-01T21:31:10Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Exploring PANoptosis in breast cancer based on scRNA-seq and bulk-seq.", "doi": "10.3389/fendo.2023.1164930", "pmid": "37455906", "authors": ["Puxing He", "Yixuan Ma", "Yaolu Wu", "Qing Zhou", "Huan Du"], "journal": "Frontiers in endocrinology", "year": 2023, "abstract": "Background: PANoptosis, a cell death pathway involving pyroptosis, apoptosis, and necroptosis, is pivotal in the development of malignancy. However, in the field of breast cancer, the interaction between PANoptosis and tumor cells has not been thoroughly explored.\nMethods: We downloaded breast cancer data and GSE176078 single-cell sequencing dataset from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases to obtain PANoptosis-associated genes. To construct prognostic models, COX and LASSO regression was used to identify PANoptosis-associated genes with prognostic value. Finally, immune infiltration analysis and differential analysis of biological functions were performed.\nResults: Risk grouping was performed according to the prognostic model constructed by COX regression and LASSO regression. The low-risk group showed a better prognosis (P < 0.05) and possessed higher levels of immune infiltration and expression of immune checkpoint-related genes. In addition, the lower the risk score, the higher the degree of microsatellite instability (MSI). Meanwhile, radixin (RDX), the gene with the highest hazard ratio (HR) value among PANoptosis prognosis-related genes, was explicitly expressed in artery Iendothelial cells (ECs) and was widely involved in signaling pathways such as immune response and cell proliferation, possessing rich biological functions.\nConclusion: We demonstrated the potential of PANoptosis-based molecular clustering and prognostic features in predicting the survival of breast cancer patients. Furthermore, this study has led to a deeper understanding of the role of PANoptosis in breast cancer and has the potential to provide new directions for immunotherapy of breast cancer.", "claims": [{"id": "claim-method", "statement": "First, univariate Cox regression analysis initially obtained PANoptosis-related genes with prognostic value. Subsequently, the genes with prognostic PANoptosis-related genes were further screened by counting least absolute shrinkage and selection operator (LASSO) regression analysis. Finally, the genes with prognostic value were identified by multivariate Cox regression analysis to identify genes with prognostic value and construct a prognostic model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37455906", "locator": "sections[6] \"Construction of a PANoptosis-related prognostic model and external validation\" paragraph 2", "quote": "First, univariate Cox regression analysis initially obtained PANoptosis-related genes with prognostic value. Subsequently, the genes with prognostic PANoptosis-related genes were further screened by counting least absolute shrinkage and selection operator (LASSO) regression analysis. Finally, the genes with prognostic value were identified by multivariate Cox regression analysis to identify genes with prognostic value and construct a prognostic model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. This data can be found here: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE176078; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE21653; https://portal.gdc.cancer.gov/.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37455906", "locator": "sections[20] \"Data availability statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. This data can be found here: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE176078; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE21653; https://portal.gdc.cancer.gov/."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The low-risk group showed a better prognosis and had higher immune infiltration and immune checkpoint-related gene expression.", "kind": "quantitative", "target": {"value": 0.05, "unit": "p-value", "tolerance": null, "source": "The low-risk group showed a better prognosis (P < 0.05) and possessed higher levels of immune infiltration and expression of immune checkpoint-related genes."}, "evidence": [{"asset": "source:pmid:37455906", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The low-risk group showed a better prognosis (P < 0.05) and possessed higher levels of immune infiltration and expression of immune checkpoint-related genes."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The low-risk group showed a better prognosis and had higher immune infiltration and immune checkpoint-related gene expression.", "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:37455906"], "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:GSE176078"], "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:GSE176078"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37455906", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fendo.2023.1164930", "sha256": "sha256:b726eef476ad81452b2375bab8883e7b051b080b96879bbd153e4fa4c31ceae5", "bytes": 37657, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fendo.2023.1164930", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10338225", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE176078", "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=GSE176078", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE176078", "version": null, "note": "Publicly available datasets were analyzed in this study. 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This data can be found here: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE176078; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE21653; https://portal.gdc.cancer.gov/.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Publicly available datasets were analyzed in this study. 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Subsequently, the genes with prognostic PANoptosis-related genes were further screened by counting least absolute shrinkage and selection operator (LASSO) regression analysis. Finally, the genes with prognostic value were identified by multivariate Cox regression analysis to identify genes with prognostic value and construct a prognostic model."}}], "targets": [{"id": "target-published-estimate", "claim_id": "claim-target-result", "operation": "cox_ph", "expected_value": 0.05, "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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Cell culture and transfection The human breast cancer cell line used in th", "′ (GenePharma, China) (26). According to the transfection manual, the previously synthesized siRNA tar", "usly synthesized siRNA targeting the RDX was transfected into cells using Lipo 2000 (Invitrogen, USA)", "bator at 37°C with 5% CO2 concentration. The siRNA sequence used in this study was RDX 5′- UAGU"], "order_basis": "paper_text_order:2380", "id": "assay:3"}, {"role": "data_generation", "definition_id": "cell_culture", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.77, "evidence": ["Center of Yan’an University. The cells were cultured in DMEM medium (BI, Israel) with 10% fetal bovi"], "order_basis": "paper_text_order:2549", "id": "assay:4"}, {"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": ["First, univariate Cox regression analysis initially obtained PANoptosis-related genes with prognostic value. Subsequently, the genes with prognostic PANoptosis-related genes were further screened by counting least absolute shrinkage and selection operator (LASSO) regression analysis. Finally, the genes with prognostic value were identified by multivariate Cox regression analysis to identify genes with prognostic value and construct a prognostic model."], "order_basis": "consumes_earlier_output", "id": "assay:5"}]}