A novel fatty-acid metabolism-based classification for triple negative breast cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36880837", "kind": "science-record", "revision": 3, "content_hash": "sha256:613523517a945c646fb8f7e05079520b9313a74196de6f83e9b02625bd8d98f3", "created_at": "2026-11-01T19:01:31Z", "updated_at": "2026-11-01T22:09:00Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A novel fatty-acid metabolism-based classification for triple negative breast cancer.", "doi": "10.18632/aging.204552", "pmid": "36880837", "authors": ["Xia Yang", "Wen Tang", "Yongtao He", "Huimin An", "Jin Wang"], "journal": "Aging", "year": 2023, "abstract": "BACKGROUND: The high heterogeneity of triple negative breast cancer (TNBC) is the main clinical challenge for individualized therapy. Considering that fatty acid metabolism (FAM) plays an indispensable role in tumorigenesis and development of TNBC, we proposed a novel FAM-based classification to characterize the tumor microenvironment immune profiles and heterogeneous for TNBC.\nMETHODS: Weighted gene correlation network analysis (WGCNA) was performed to identify FAM-related genes from 221 TNBC samples in Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset. Then, non-negative matrix factorization (NMF) clustering analysis was applied to determine FAM clusters based on the prognostic FAM-related genes, which chosen from the univariate/multivariate Cox regression model and the least absolute shrinkage and selection operator (LASSO) regression algorithm. Then, a FAM scoring scheme was constructed to further quantify FAM features of individual TNBC patient based on the prognostic differentially expressed genes (DEGs) between different FAM clusters. Systematically analyses were performed to evaluate the correlation between the FAM scoring system (FS) with survival outcomes, genomic characteristics, tumor microenvironment (TME) features and immunotherapeutic response for TNBC, which were further validated in the Cancer Genome Atlas (TCGA) and GSE58812 datasets. Moreover, the expression level and clinical significancy of the selected FS gene signatures were further validated in our cohort.\nRESULTS: 1860 FAM-genes were screened out using WGCNA. Three distinct FAM clusters were determined by NMF clustering analysis, which allowed to distinguish different groups of patients with distinct clinical outcomes and tumor microenvironment (TME) features. Then, prognostic gene signatures based on the DEGs between different FAM clusters were identified using univariate Cox regression analysis and Lasso regression algorithm. A FAM scoring scheme was constructed, which could divide TNBC patients into high and low-FS subgroups. Low FS subgroup, characterized by better prognosis and abundance with effective immune infiltration. While patients with higher FS were featured with poorer survival and lack of effective immune infiltration. In addition, two independent immunotherapy cohorts (Imvigor210 and GSE78220) confirmed that patients with lower FS demonstrated significant therapeutic advantages from anti-PD-1/PD-L1 immunotherapy and durable clinical benefits. Further analyses in our cohort found that the differential expression of CXCL13, FBP1 and PLCL2 were significantly associated with clinical outcomes of TNBC samples.\nCONCLUSIONS: This study revealed FAM plays an indispensable role in formation of TNBC heterogeneity and TME diversity. The novel FAM-based classification could provide a promising prognostic predictor and guide more effective immunotherapy strategies for TNBC.", "claims": [{"id": "claim-method", "statement": "The prognostic significance of the FAM-related genes which obtained from the WGCNA was analyzed by univariate Cox regression model using the R package “survival”. Then, LASSO Cox regression algorithm was applied to further select prognostic FAM genes using the R package “glmet” [21]. Subsequently, the most robust prognostic gene signatures for OS were chosen by performing multivariate Cox regression model in METABRIC-TNBC patients.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36880837", "locator": "sections[5] \"Prognostic value analysis\" paragraph 2", "quote": "The prognostic significance of the FAM-related genes which obtained from the WGCNA was analyzed by univariate Cox regression model using the R package “survival”. Then, LASSO Cox regression algorithm was applied to further select prognostic FAM genes using the R package “glmet” [21]. Subsequently, the most robust prognostic gene signatures for OS were chosen by performing multivariate Cox regression model in METABRIC-TNBC patients."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data used in this work can be acquired from the Gene-Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) under the accession number GSE78220 and GSE58812, METABRIC and the TCGA portal (https://portal.gdc.cancer.gov/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36880837", "locator": "sections[17] \"Availability of data and materials\" paragraph 2", "quote": "All data used in this work can be acquired from the Gene-Expression Omnibus (GEO; https://www.ncbi.nlm.nih.gov/geo/) under the accession number GSE78220 and GSE58812, METABRIC and the TCGA portal (https://portal.gdc.cancer.gov/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study identified three distinct fatty acid metabolism clusters and constructed a FAM scoring system that divided TNBC patients into low- and high-FS subgroups with different survival, immune-infiltration, and immunotherapy-response characteristics.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36880837", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Three distinct FAM clusters were determined by NMF clustering analysis, which allowed to distinguish different groups of patients with distinct clinical outcomes and tumor microenvironment (TME) features."}, {"asset": "source:pmid:36880837", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "A FAM scoring scheme was constructed, which could divide TNBC patients into high and low-FS subgroups."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study identified three distinct fatty acid metabolism clusters and constructed a FAM scoring system that divided TNBC patients into low- and high-FS subgroups with different survival, immune-infiltration, and immunotherapy-response characteristics.", "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:36880837"], "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. 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