Search PubMedSearch

PubMed · 42551759

Fatty acids and breast cancer: Epidemiology, subtype-specific metabolism, immune regulation, and clinical translation.

Abstract

Fatty acids (FAs) are bioactive dietary and metabolic molecules that participate in membrane architecture, energy homeostasis, inflammatory signaling, gene regulation and immune function, all of which intersect with breast cancer (BC) risk, progression and treatment response. In this narrative review we integrate epidemiological, clinical, translational and mechanistic evidence on the role of FAs in BC. Saturated, monounsaturated, trans- and polyunsaturated FAs (PUFAs) are treated as distinct biological exposures rather than interchangeable measures of total fat intake. Similarly, evidence from dietary assessment, circulating biomarkers, erythrocyte membrane composition, adipose tissue stores and tumor lipid signatures is interpreted separately, because each captures exposure and biology at a different level. BC subtypes differ in FA synthesis, uptake, oxidation, storage and remodeling: luminal tumors are frequently linked to hormone-regulated lipogenesis, human epidermal growth factor receptor 2 (HER2)-positive tumors to growth-factor-driven lipid metabolism, and triple-negative tumors to exogenous FA uptake, inflammatory lipid mediators and ferroptosis-related vulnerabilities. FA-derived mediators also shape immune-cell polarization, cytokine signaling and the tumor microenvironment, and dietary FAs may reshape the gut microbiota; the fiber-derived short-chain FAs it produces, distinct from dietary FAs, likewise help regulate immune and inflammatory tone. Clinical data suggest possible roles for fat-quality modification and selected n-3 PUFA interventions, but findings are heterogeneous and not yet sufficient to support routine biomarker-guided precision onco-nutrition. Candidate biomarkers, such as erythrocyte n-6:n-3 composition, require prospective validation before clinical implementation. FA biology thus represents a modifiable but complex axis in BC prevention, tumor biology and supportive care.

Explore related subjects

Keep this discovery

BibTeXRIS

Camila Farias, Giulio Martinelli, Benjamín Walbaum, Francisco Acevedo, M Loreto Bravo, Mauricio P Pinto, María Jesús Vera, Milana Bergamino, Cesar Sánchez. 2026-08-04. Fatty acids and breast cancer: Epidemiology, subtype-specific metabolism, immune regulation, and clinical translation.. https://doi.org/10.1016/j.mce.2026.112881

Cite the original work for its findings. Save a collection to share your selection of sources.

Discover connections

Connections use source metadata and explicit phrase matches, not verified experimental comparisons.

KEEP EXPLORING

Related citations

UNCX/SIN3A-Mediated H4K8 decrotonylation suppresses FOXO3 to drive TNBC progression and docetaxel resistance.

Triple-negative breast cancer (TNBC) remains a clinically challenging subtype characterized by aggressive behavior and limited treatment options. Though docetaxel remains a cornerstone chemotherapy for TNBC, the frequent emergence of resistance highlights the urgent need to identify novel therapeutic targets. In this study, we report that uncoordinated homeobox (UNCX) is upregulated in docetaxel-resistant breast cancer cells, genomically amplified in breast cancer, and associated with poor survival in breast carcinoma patients. Functional studies revealed that UNCX promotes breast cancer cell proliferation, migration and reduces the docetaxel sensitivity. Mechanistically, UNCX functions as a transcriptional repressor by recruiting the SIN3A complex. Genome-wide profiling indicated that the UNCX/SIN3A complex directly binds to the promoters of tumor-suppressor genes including FOXO3, and represses their transcription by removing histone H4K8 crotonylation (H4K8cr). Additionally, the UNCX/SIN3A complex enhances FOXO3 phosphorylation and inhibits its nuclear translocation, further inhibiting its activity. Notably, SIN3A knockdown, FOXO3 overexpression, or crotonylation restoration effectively reverses UNCX-induced malignant phenotypes. These findings collectively establish the UNCX/SIN3A-H4K8cr-FOXO3 axis as a pivotal epigenetic regulator of TNBC progression and chemoresistance, revealing new avenues for targeted therapeutic development against this aggressive breast cancer subtype.

Humans

Beyond predictive performance: A systematic review and critical methodological appraisal of AI/ML and conventional modelling strategies in breast, colorectal, and pancreatic Cancer.

BACKGROUND: Predictive modelling for cancer risk, treatment-related complications, and survival is central to precision oncology. Conventional logistic regression (LR) and Cox proportional hazards (CoxPH) regression remain widely used but are limited when modelling nonlinear interactions, high-dimensional imaging features, and multimodal clinical-metabolic predictors. Artificial intelligence (AI) and machine learning (ML) methods offer expanded capability through automated feature extraction, ensemble learning, and flexible survival modelling, but the evidence on when AI/ML adds value over conventional models across cancer sites and predictive tasks remains fragmented. OBJECTIVE: To systematically evaluate the methodological performance, validation strategies, and translational limitations of AI/ML models compared with conventional statistical models in published predictive-modelling studies for breast, colorectal, or pancreatic cancer. METHODS: PubMed, Scopus, and Web of Science were searched for studies published between January 2019 and March 2025. Two reviewers independently conducted title-and-abstract screening, full-text eligibility assessment, and PROBAST risk-of-bias assessment. Sixty-five studies (n = 907,567 participants) were narratively synthesised by cancer site, predictive task, model family, comparator, validation strategy, predictor modality, and calibration or explainability reporting. RESULTS: The 65 studies comprised breast cancer (n = 35), colorectal cancer (n = 21), and pancreatic cancer (n = 9). AI/ML superiority over LR and CoxPH was task- and data-dependent. CNN- and U-Net-based models predominated in imaging and body-composition tasks, tree-based ensembles consistently outperformed LR for tabular perioperative complication prediction, and CoxPH remained competitive, and in the largest pancreatic risk study, superior to XGBoost (C-index 0.802 vs 0.723) in well-structured datasets. PROBAST analysis-domain risk was moderate in 54 of 65 studies (83%), driven by limited external validation, sparse calibration reporting (11/65), and few decision-curve analyses (7/65). CONCLUSION: AI/ML adds the most methodological value in imaging-derived feature extraction and nonlinear perioperative prediction, while conventional regression remains preferable in large, structured datasets with linear predictors. Clinical translation requires standardised body-composition definitions, external validation, calibration assessment, decision-curve analysis, and explainability, in line with TRIPOD+AI and CLAIM standards.

Humans

AI Health message intervention: The role of message customization and message source in breast cancer screening among women of color.

OBJECTIVES: To examine the effectiveness of breast cancer screening messages with varying levels of customization (generic, targeted, and tailored) and to compare AI-generated versus human-generated messages. METHODS: A between-subjects experimental design with a control condition was employed. Message content followed a standardized structure and varied by level of customization: generic, targeted (demographic-based), and tailored (perceived susceptibility- and barrier-based). Messages were developed by either the authors or GenAI (ChatGPT-4o). A total of 391 participants recruited via Prolific were randomly assigned to five groups (generic, targeted-human, targeted-AI, tailored-human, and tailored-AI). Self-efficacy, behavioral intentions, attitudes, and message believability were measured using different scales. RESULTS: Customized (tailoring and targeting) health messages performed comparably to generic messages in shaping positive health outcomes. GenAI-generated messages also produced outcomes comparable to those of human-generated messages under standardized conditions. Significant negative indirect effects through message believability for the human-tailored condition was found relative to the generic condition. CONCLUSIONS: GenAI may be a useful tool for developing and customizing scalable health messages. Its effectiveness depends not only on customization but also on maintaining message quality, including readability, clarity, coherence, naturalness, and credibility. PRACTICAL IMPLICATIONS: GenAI may support health practitioners in developing customized and scalable breast cancer messages. However, professional review remains necessary to ensure that the message is culturally appropriate, responsive to patient concerns, and suitable for use alongside patient-provider communication.

Humans