Integrating Single-cell and Machine Learning with Experimental Validation Reveals FASN Conferring Breast Cancer Stem cell-like Properties: Therapeutic Insights and Prognostic Implications
Abstract
Abstract Breast cancer stem cells (BCSCs) are critical drivers of tumor recurrence, metastasis, and resistance to standard therapies, posing significant challenges in breast cancer management. Fatty acid synthase (FASN) has emerged as a key metabolic enzyme, not only supporting the lipid biosynthesis essential for tumor growth but also contributing to the maintenance of BCSC-like properties. In this study, we explored the multidimensional role of FASN in breast cancer by integrating single-cell transcriptomic analysis, mechanistic experiments, and clinical modeling. We demonstrated that FASN is highly expressed and active in BCSCs, and its overexpression promotes stemness through the activation of the Wnt/β-catenin signaling pathway. Functional assays revealed that modulating FASN levels significantly impacts BCSC proliferation, clonogenicity, and self-renewal capabilities. Moreover, we identified lansoprazole as a potent inhibitor of FASN, which effectively reduced BCSC growth and Wnt/β-catenin activity in vitro and in vivo. To further validate the clinical significance of FASN, we developed a prognostic model using 101 machine-learning algorithm combinations, achieving high predictive accuracy for breast cancer outcomes. These findings not only position FASN as a promising therapeutic target in breast cancer but also underscore the potential of FASN inhibition, through agents like lansoprazole, as a novel therapeutic strategy.
How strong is the evidence?
Evidence Hub reads this as a preprint, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
Read the paper
Related studies
- The Prediction of the In Vitro Release Curves for PLGA-Based Drug Delivery Systems with Neural Networks
- Promising Ursolic Acid as a Novel Antituberculosis Agent: Current Progress and Challenges
- Proof of principle concept for the analysis and functional prediction of rare genetic variants in the CYP2C19 and CYP2D6 genes
- Fabrication, characterization, and docking studies of furosemide-loaded nanosponges using the emulsion solvent diffusion method
- The Effect of Concomitant Administration of Proton Pump Inhibitors on the Pharmacokinetics of CDK4/6 Inhibitors in Rats: Implications for the Evaluation of Hepatic and Transporter-Mediated Drug–Drug Interactions
- Mining the Metabolic Capacity of Clostridium sporogenes Aided by Machine Learning
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.