Immunogenic cell death-related classification reveals prognosis and effectiveness of immunotherapy in breast cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38263419", "kind": "science-record", "revision": 3, "content_hash": "sha256:76b7f839b6b7b2d86c30fd742de96ece63df173f86db9c4834703d88a2d85240", "created_at": "2026-11-01T19:20:11Z", "updated_at": "2026-11-01T22:11:06Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Immunogenic cell death-related classification reveals prognosis and effectiveness of immunotherapy in breast cancer.", "doi": "10.1038/s41598-024-52353-4", "pmid": "38263419", "authors": ["Lei Zhu", "Yanmei Wu", "Haichun Zhao", "Zicheng Guo", "Biao Bo", "Li Zheng"], "journal": "Scientific reports", "year": 2024, "abstract": "Lack of specific biomarkers and effective drug targets constrains therapeutic research in breast cancer (BC). In this regard, therapeutic modulation of damage-associated molecular patterns (DAMPs)-induced immunogenic cell death (ICD) may help improve the effect of immunotherapy in individuals with BC. The aim of this investigation was to develop biomarkers for ICD and to construct ICD-related risk estimation models to predict prognosis and immunotherapy outcomes of BC. RNA-seq transcriptome information and medical data from individuals with BC (n = 943) were obtained from TCGA. Expression data from a separate BC cohort (GEO: GSE20685) were used for validation. We identified subtypes of high and low ICD gene expression by consensus clustering and assessed the connection between ICD subtypes and tumor microenvironment (TME). In addition, different algorithms were used to construct ICD-based prognostic models of BC. BC samples were categorized into subtypes of high and low ICD expression depending on the expression of genes correlated with ICD. The subtype of ICD high-expression subtypes are correlated with poor prognosis in breast cancer, while ICD low-expression subtypes may predict better clinical outcomes. We also created and verified a predictive signature model depending on four ICD-related genes (ATG5, CD8A, CD8B, and HSP90AA1), which correlates with TME status and predicts clinical outcomes of BC patients. We highlight the connection of ICD subtypes with the dynamic evolution of TME in BC and present a novel ICD-based prognostic model of BC. In clinical practice, distinction of ICD subtype and assessment of ICD-related biomarkers should help guide treatment planning and improve the effectiveness of tumor immunotherapy.", "claims": [{"id": "claim-method", "statement": "Lasso regression analysis was utilized to examine and select ICD-related genes for evaluation of predictive models (Fig. 6B).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38263419", "locator": "sections[27] \"Construction and verification of ICD risk signatures\" paragraph 2", "quote": "Lasso regression analysis was utilized to examine and select ICD-related genes for evaluation of predictive models (Fig. 6B)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. This data can be found here: https://portal.gdc.cancer.gov/ (TCGA-BRCA) and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE20685 (GEO).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38263419", "locator": "sections[35] \"Data availability\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. This data can be found here: https://portal.gdc.cancer.gov/ (TCGA-BRCA) and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE20685 (GEO)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "We also created and verified a predictive signature model depending on four ICD-related genes (ATG5, CD8A, CD8B, and HSP90AA1), which correlates with TME status and predicts clinical outcomes of BC patients.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38263419", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "We also created and verified a predictive signature model depending on four ICD-related genes (ATG5, CD8A, CD8B, and HSP90AA1), which correlates with TME status and predicts clinical outcomes of BC patients."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "We also created and verified a predictive signature model depending on four ICD-related genes (ATG5, CD8A, CD8B, and HSP90AA1), which correlates with TME status and predicts clinical outcomes of BC patients.", "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:38263419"], "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:GSE20685"], "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:GSE20685"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38263419", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1038/s41598-024-52353-4", "sha256": "sha256:387b854a20d46d544889efe8b865befba15a9d3d5e4259e33cb193a11944cd28", "bytes": 42957, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1038/s41598-024-52353-4", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10805874", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE20685", "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=GSE20685", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE20685", "version": null, "note": "Publicly available datasets were analyzed in this study. This data can be found here: https://portal.gdc.cancer.gov/ (TCGA-BRCA) and https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE20685 (GEO).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "Publicly available datasets were analyzed in this study. 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RNA-seq transcriptome information and medical data f", "nd methods Datasets and statistical analysis RNA-seq transcriptome information and corresponding", "is and immunotherapy outcomes of BC. RNA-seq transcriptome information and medical data from individual", "ds Datasets and statistical analysis RNA-seq transcriptome information and corresponding clinicopatholo"], "order_basis": "paper_text_order:547", "id": "assay:1"}, {"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": ["l breast (MDA-KB2) and BC cells (MDA-MB-231, MCF-7, and BT549) were acquired from Procell Life"], "order_basis": "paper_text_order:3080", "id": "assay:2"}, {"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": ["Lasso regression analysis was utilized to examine and select ICD-related genes for evaluation of predictive models (Fig. 6B)."], "order_basis": "consumes_earlier_output", "id": "assay:3"}]}