ArticleTranslational cancer research2026
Novel fatty acid metabolism-related molecular subtyping and prognostic signature for breast cancer.
Article in Translational cancer research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Breast cancer (BRCA) is one of the most prevalent malignant tumors in women worldwide, characterized by significant heterogeneity. Fatty acid metabolism (FAM) plays a crucial biological role in the initiation and progression of cancer. This study aims to identify novel, effective biomarkers related to FAM for improved risk stratification and treatment selection in BRCA patients. Methods: Gene expression data from 1,217 BRCA patients were obtained from The Cancer Genome Atlas (TCGA) database. A comprehensive machine learning approach, incorporating ten different methods, was used to develop a FAM-related gene prognostic model (FAMGM). The Kaplan-Meier method and correlation analysis were employed to assess differences in overall survival (OS) and immune characteristics between high- and low-risk groups. External validation was performed using independent datasets. Single-cell RNA sequencing (scRNA-seq) data from 26 BRCA patients were analyzed, and the potential functions and mechanisms of the model genes were investigated using single-sample gene set enrichment analysis (ssGSEA), CellChat, and other algorithms. Finally, spatial transcriptomics (ST) analysis was conducted to examine the expression of model genes in the malignant regions of tumors. Results: The FAMGM, developed using CoxBoost and random survival forest (RSF) methods, was identified as the optimal prognostic model. FAMGM demonstrated stable and robust performance in predicting clinical outcomes for BRCA. The high-risk group showed poor survival prognosis, typically associated with advanced clinical stages, reduced immune cell infiltration, and increased tumor mutational burden (TMB). Model genes were predominantly enriched in macrophages and appeared to influence tumor progression through the upregulation of multiple signaling pathways. Additionally, these model genes exhibited higher expression in malignant tumor regions. Conclusions: FAMGM holds significant potential as a prognostic marker and could be used in the subsequent diagnosis, treatment, prognostic prediction, and mechanistic research of BRCA.
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