Single-cell RNA sequencing reveals age-related heterogeneity in the tumor microenvironment of breast cancer patients.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41266463", "kind": "science-record", "revision": 2, "content_hash": "sha256:21eebf57629ca06c04493dc59a5f10da28d1d5d8059c247f1d610beff3f07f39", "created_at": "2026-11-01T20:31:17Z", "updated_at": "2026-11-01T21:36:04Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Single-cell RNA sequencing reveals age-related heterogeneity in the tumor microenvironment of breast cancer patients.", "doi": "10.1038/s41598-025-24720-2", "pmid": "41266463", "authors": ["Zihao Li", "Ji Feng", "Mengyang An", "Shuming Kou", "Zengren Zhao", "Jingjing Yu", "Bo Liu", "Fangjian Shang"], "journal": "Scientific reports", "year": 2025, "abstract": "Breast cancer (BC) is the most common malignancy among women, with its progression and prognosis significantly influenced by the tumor microenvironment (TME). Age-related differences in TME composition lead to distinct tumor behaviors: young patients (≤ 40 years) exhibit aggressive tumors, while elderly patients (> 70 years) experience immunosenescence and reduced therapy responses. We performed single-cell RNA sequencing (scRNA-seq) analysis on tumors from 10 breast cancer patients (5 ≤ 40 years, 5 ≥ 70 years), encompassing 33,664 high-quality cells. After cell annotation and batch correction, malignant epithelial cells were identified using inferCNV. We applied pseudotime trajectory analysis, pathway enrichment, and cell-cell communication profiling to investigate age-specific TME dynamics. Survival relevance was assessed using a GEO cohort (GSE20685) of young breast cancer patients, and immunohistochemical staining was performed on clinical tumor and fibroadenoma tissues to validate protein-level expression of key ISGs. In young patients, malignant epithelial cells showed gradual upregulation of interferon-stimulated genes (ISGs) such as IFI44, IFI44L, IFIT1, and IFIT3 along the pseudotime trajectory, suggesting their involvement in early tumorigenesis. High expression of these ISGs was significantly associated with poor overall survival in a young BC cohort (GSE20685). Immunohistochemical validation further confirmed elevated IFIT3 protein levels in young tumor tissues. In contrast, elderly patients had a TME enriched in macrophages and fibroblasts, with activation of immunosuppressive pathways (e.g., SPP1, COMPLEMENT). Our integrative analysis identifies ISGs as key transcriptional drivers of tumorigenesis in young breast cancer, with potential prognostic and therapeutic value. Despite limited sample size, the combination of single-cell transcriptomics, clinical survival data, and protein-level validation provides robust evidence of age-specific TME remodeling. These findings support the development of age-tailored immunotherapy strategies targeting interferon signaling in young patients and immune checkpoint pathways (e.g., LAG3, CTLA4) in elderly individuals.", "claims": [{"id": "claim-method", "statement": "Kaplan–Meier survival curves were generated using the “survival” and “survminer” R packages, and group differences in overall survival (OS) were assessed using the log-rank test.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41266463", "locator": "sections[6] \"GEO data processing and survival analysis\" paragraph 3", "quote": "Kaplan–Meier survival curves were generated using the “survival” and “survminer” R packages, and group differences in overall survival (OS) were assessed using the log-rank test."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets analyzed in this study were downloaded from the GEO database (GSE161529, GSE176078, and GSE20685) and are publicly available at [https://www.ncbi.nlm.nih.gov/geo/].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41266463", "locator": "sections[34] \"Data availability\" paragraph 2", "quote": "The datasets analyzed in this study were downloaded from the GEO database (GSE161529, GSE176078, and GSE20685) and are publicly available at [https://www.ncbi.nlm.nih.gov/geo/]."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "", "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:41266463"], "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:41266463", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1038/s41598-025-24720-2", "sha256": 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We performed single-cell RNA sequencing (scRNA-seq) analysis on tumors from 10 breas", "a processing This study utilized single-cell RNA sequencing (scRNA-seq) data from 5 young (≤ 40 years ol", "sample size, the combination of single-cell transcriptomics, clinical survival data, and protein-level v"], "order_basis": "paper_text_order:0", "id": "assay:1"}, {"role": "data_generation", "definition_id": "scrna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "unsupported", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["Single-cell RNA sequencing reveals age-related heterogeneity in the tum", "and reduced therapy responses. We performed single-cell RNA sequencing (scRNA-seq) analysis on tumors from 10 breas", "pite limited sample size, the combination of single-cell transcriptomics, clinical survival data, and protein-level v", "es. 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