Identification of exosome-related gene signature as a promising diagnostic and therapeutic tool for breast cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38665551", "kind": "science-record", "revision": 3, "content_hash": "sha256:80f13b4a2004f9d80cbdbbd72e9b9d8bcfe2ba81a6bfc4ebaa986d53986c9060", "created_at": "2026-11-01T19:20:38Z", "updated_at": "2026-11-01T22:11:46Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of exosome-related gene signature as a promising diagnostic and therapeutic tool for breast cancer.", "doi": "10.1016/j.heliyon.2024.e29551", "pmid": "38665551", "authors": ["Qitong Chen", "Qin Zhou"], "journal": "Heliyon", "year": 2024, "abstract": "Background: Exosomes are promising tools for the development of new diagnostic and therapeutic approaches. Exosomes possess the ability to activate signaling pathways that contribute to the remodeling of the tumor microenvironment, angiogenesis, and the regulation of immune responses. We aimed to develop a prognostic score based on exosomes derived from breast cancer.\nMaterials and methods: Training was conducted on the TCGA-BRCA dataset, while validation was conducted on GSE20685, GSE5764, GSE7904, and GSE29431. A total of 121 genes related to exosomes were retrieved from the ExoBCD database. The Cox proportional hazards model is used to develop risk score model. The GSVA package was utilized to analyze single-sample gene sets and identify exosome signatures, while the WGCNA package was utilized to identify gene modules associated with clinical outcomes. The clusterProfiler and GSVA R packages facilitated gene set enrichment and variation analyses. Furthermore, CIBERSORT quantified immune infiltration, and a correlation between gene expression and drug sensitivity was assessed using the TIDE algorithm.\nResults: An exosome-related prognostic score was established using the following selected genes: ABCC9, PIGR, CXCL13, DOK7, CD24, and IVL. Various immune cells that promote cancer immune evasion were associated with a high-risk prognostic score, which was an independent predictor of outcome. High-risk and low-risk groups exhibited significantly different infiltration abundances (p < 0.05). By conducting a sensitivity comparison, we found that patients with high-risk scores exhibited more favorable responses to immunotherapy than those with low-risk scores.\nConclusion: The exosome-related gene signature exhibits outstanding performance in predicting the prognosis and cancer status of patients with breast cancer and guiding immunotherapy.", "claims": [{"id": "claim-method", "statement": "The Cox proportional hazards model is used to develop risk score model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38665551", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "The Cox proportional hazards model is used to develop risk score model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The Cancer Genome Atlas Breast Cancer (TCGA-BRCA) dataset, Gene Expression Omnibus (GEO) datasets GSE20685, GSE5764, GSE7904, and GSE29431. Access to the TCGA-BRCA dataset can be obtained through the National Cancer Institute's Genomic Data Commons. GEO datasets are publicly available and can be accessed through the GEO repository by searching the respective accession numbers. These datasets were used under the terms of access provided by their respective repositories.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38665551", "locator": "sections[27] \"Data availability statement\" paragraph 2", "quote": "The Cancer Genome Atlas Breast Cancer (TCGA-BRCA) dataset, Gene Expression Omnibus (GEO) datasets GSE20685, GSE5764, GSE7904, and GSE29431. Access to the TCGA-BRCA dataset can be obtained through the National Cancer Institute's Genomic Data Commons. GEO datasets are publicly available and can be accessed through the GEO repository by searching the respective accession numbers. These datasets were used under the terms of access provided by their respective repositories."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The exosome-related gene signature exhibits outstanding performance in predicting the prognosis and cancer status of patients with breast cancer and guiding immunotherapy.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38665551", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "The exosome-related gene signature exhibits outstanding performance in predicting the prognosis and cancer status of patients with breast cancer and guiding immunotherapy."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The exosome-related gene signature exhibits outstanding performance in predicting the prognosis and cancer status of patients with breast cancer and guiding immunotherapy.", "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:38665551"], "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:38665551", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1016/j.heliyon.2024.e29551", "sha256": "sha256:6e6313e8748f0a53378d59ac32514696bc1e03a49bc1a6dd3f94f0cf38bf4937", "bytes": 56748, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1016/j.heliyon.2024.e29551", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11043961", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "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": "The Cancer Genome Atlas Breast Cancer (TCGA-BRCA) dataset, Gene Expression Omnibus (GEO) datasets GSE20685, GSE5764, GSE7904, and GSE29431. Access to the TCGA-BRCA dataset can be obtained through the National Cancer Institute's Genomic Data Commons. GEO datasets are publicly available and can be accessed through the GEO repository by searching the respective accession numbers. These datasets were used under the terms of access provided by their respective repositories.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The Cancer Genome Atlas Breast Cancer (TCGA-BRCA) dataset, Gene Expression Omnibus (GEO) datasets GSE20685, GSE5764, GSE7904, and GSE29431. Access to the TCGA-BRCA dataset can be obtained through the National Cancer Institute's Genomic Data Commons. GEO datasets are publicly available and can be accessed through the GEO repository by searching the respective accession numbers. 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These datasets were used under the terms of access provided by their respective repositories.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM134584", "GSM134586", "GSM134587", "GSM134588", "GSM134589", "GSM134591", "GSM134687", "GSM134688", "GSM134689", "GSM134690", "GSM134691", "GSM134692", "GSM134693", "GSM134694", "GSM134695", "GSM134696", "GSM134697", "GSM134698", "GSM134699", "GSM134700", "GSM134701", "GSM134702", "GSM134703", "GSM134704", "GSM134705", "GSM134706", "GSM134707", "GSM134708", "GSM134709", "GSM134710"], "sample_count": 30, "group_sizes": null, "group_sizes_basis": null, "platform": "570", "taxon": "Homo sapiens", "title": "Analysis of microdissected invasive lobular and ductal breast carcinomas in relation to normal ductal and lobular cells", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE7904", "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=GSE7904", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE7904", "version": null, "note": "The Cancer Genome Atlas Breast Cancer (TCGA-BRCA) dataset, Gene Expression Omnibus (GEO) datasets GSE20685, GSE5764, GSE7904, and GSE29431. 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