Identification and Validation of a Necroptosis-Related Prognostic Model in Tumor Recurrence and Tumor Immune Microenvironment in Breast Cancer Management.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39081870", "kind": "science-record", "revision": 2, "content_hash": "sha256:c5d9e9d781e1509c9b324a33d795b695868a55b6b1caec62f105580864460749", "created_at": "2026-11-01T20:29:09Z", "updated_at": "2026-11-01T21:33:33Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and Validation of a Necroptosis-Related Prognostic Model in Tumor Recurrence and Tumor Immune Microenvironment in Breast Cancer Management.", "doi": "10.2147/JIR.S460551", "pmid": "39081870", "authors": ["Xiaobo Wang", "Zongyao Chen", "Jianing Tang", "Jing Cao"], "journal": "Journal of inflammation research", "year": 2024, "abstract": "Background: Breast cancer is the leading cause of cancer-related death in women. Necroptosis, a form of programmed necrotic cell death, occurs in many solid tumors, including breast cancer, and influences anti-tumor immunity. The role of necroptosis in managing breast cancer recurrence remains unclear.\nMethods: Gene expression profiles and clinical data of breast cancer patients were obtained from the GEO (GSE20685, GSE21653, GSE25055) and TCGA databases. Data analysis and visualization were performed using R. Unsupervised Consensus Clustering and LASSO-COX regression stratified breast cancer patients. GO, KEGG, GSVA, ESTIMATE, and ROC analyses were used to investigate necroptotic signatures. In vitro and in vivo experiments validated necroptosis's role in breast cancer immunity.\nResults: The potential function of necroptotic signature in immunity was first indicated with GO analysis in BRCA cohort. Next, two prognostic models based on the necroptotic profiles both suggested a link between low-risk group with a particular necroptotic immune signature. And a variety of immune cells and immune pathways were shown to be positively associated with a patient's risk score. As an altered immune checkpoint pattern was observed after regulating necroptotic genes, where TIM-3 and LAGLS9 elevated significantly in low-risk group, further validation in vitro and in vivo demonstrated that manipulating a subset of necroptotic gene set could sensitize tumor response to the co-blockade immunotherapy of anti-TIM-3 and anti-PD-1.\nConclusion: We demonstrated two strategies to stratify breast cancer patients based on their necroptotic profiles and showed that necroptotic signature could assign patients with different tumor immune microenvironment patterns and different recurrence-related prognosis. A subset of necroptotic gene set, composed of TLR3, RIPK3, NLRP3, CASP1, ALDH2 and EZH2, was identified as a biomarker set for predicting immunotherapy-response and recurrence-related prognosis. Targeting necroptosis could helpfacilitate the development of novel breast cancer treatments and tailor personalized medical treatment.", "claims": [{"id": "claim-method", "statement": "Lasso-Cox regression analysis was applied to identify the prognostic signature of genes of interest.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39081870", "locator": "sections[6] \"Construction of the Prognostic Gene Signature with LASSO-COX Regression\" paragraph 2", "quote": "Lasso-Cox regression analysis was applied to identify the prognostic signature of genes of interest."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data that support the findings of our study were derived from the following resources available in the public domain: Gene Expression Omnibus at https://www.ncbi.nlm.nih.gov/geo/ (GSE20685, GSE21653 and GSE25055) and the Cancer Genome Atlas Program at https://portal.gdc.cancer.gov/.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39081870", "locator": "sections[25] \"Data Sharing Statement\" paragraph 2", "quote": "The data that support the findings of our study were derived from the following resources available in the public domain: Gene Expression Omnibus at https://www.ncbi.nlm.nih.gov/geo/ (GSE20685, GSE21653 and GSE25055) and the Cancer Genome Atlas Program at https://portal.gdc.cancer.gov/."}], "state": "complete"}, {"id": "claim-target-result", "statement": "We demonstrated two strategies to stratify breast cancer patients based on their necroptotic profiles and showed that necroptotic signature could assign patients with different tumor immune microenvironment patterns and different recurrence-related prognosis.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39081870", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "We demonstrated two strategies to stratify breast cancer patients based on their necroptotic profiles and showed that necroptotic signature could assign patients with different tumor immune microenvironment patterns and different recurrence-related prognosis."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "We demonstrated two strategies to stratify breast cancer patients based on their necroptotic profiles and showed that necroptotic signature could assign patients with different tumor immune microenvironment patterns and different recurrence-related prognosis.", "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:39081870"], "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:39081870", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.2147/JIR.S460551", "sha256": 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