Integrating Single-cell and Machine Learning with Experimental Validation Reveals FASN Conferring Breast Cancer Stem cell-like Properties: Therapeutic Insights and Prognostic Implications

Integrating Single-cell and Machine Learning with Experimental Validation Reveals FASN Conferring Breast Cancer Stem cell-like Properties: Therapeutic Insights and Prognostic Implications | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Integrating Single-cell and Machine Learning with Experimental Validation Reveals FASN Conferring Breast Cancer Stem cell-like Properties: Therapeutic Insights and Prognostic Implications jingdi zhang, haihong hu, chao xia, hongxia zhu, wendi zhan, bo hao, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7324806/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 16 You are reading this latest preprint version 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. Biological sciences/Cancer Biological sciences/Cell biology Biological sciences/Computational biology and bioinformatics Biological sciences/Drug discovery Fatty Acid Synthase breast cancer stem cells Wnt/β-catenin lansoprazole Machine Learning Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Breast cancer is a malignant tumor that predominantly arises from breast tissue in women. According to the authoritative data released by the International Agency for Research on Cancer (IARC) of the World Health Organization (WHO), in 2020, there were 2.3 million new cases of breast cancer globally, accounting for 11.7% of all cancer diagnoses worldwide. Additionally, more than 685,000 women succumbed to the disease, with both the incidence and mortality rates continuing to rise [ 1 , 2 ]. Current treatment modalities for breast cancer include surgery, radiotherapy, chemotherapy, targeted biological therapies, and immunotherapy [ 3 ]. Despite the significant advancements in early detection and therapeutic strategies driven by progress in biotechnology and clinical medicine, which have led to improvements in the quality of life and survival rates for patients with early-stage breast cancer, substantial challenges persist [ 4 , 5 ]. Recurrence, metastasis, and the development of resistance to treatment remain the primary factors contributing to poor clinical outcomes, with the five-year survival rate for advanced breast cancer remaining as low as 20% [ 6 – 8 ]. The limited understanding of the molecular mechanisms underlying these processes has hindered the development of effective therapeutic interventions [ 9 , 10 ]. Therefore, elucidating the molecular mechanisms that drive breast cancer recurrence, metastasis, and therapeutic resistance, and identifying novel therapeutic targets, are critical challenges that must be addressed to improve clinical outcomes in breast cancer treatment. Recent studies have identified a small subpopulation within breast tumors, known as BCSCs, which are characterized by their self-renewal capacity, multipotent differentiation potential, high tumorigenicity, and marked resistance to conventional therapies, and are closely associated with breast cancer recurrence, metastasis, treatment resistance, and poor clinical outcomes [ 11 – 13 ]. The signaling pathways such as Notch, Wnt, and Hedgehog, which regulate cell self-renewal and differentiation, are often hyperactivated in BCSCs [ 14 ]. This overactivation enhances the stemness properties of BCSCs, making them highly tumorigenic and more prone to forming recurrent lesions [ 15 ]. Moreover, BCSCs are known to express high levels of various transport proteins, such as multidrug resistance protein 1 (MDR1), MRP1, and BCRP1, as well as DNA repair-related proteins like Ung, Uhrf1, and Xrcc5. These proteins contribute to the resistance of BCSCs to chemotherapy and radiotherapy, rendering conventional treatments less effective against this subpopulation [ 16 , 17 ]. In summary, breast cancer stem cells play an important role in the process of breast cancer recurrence, metastasis and treatment resistance, and are an important reason for breast cancer recurrence and metastasis. Therefore, targeted tumor stem cell therapy is expected to break through the bottleneck of current clinical breast cancer treatment. Fatty acid synthase (FASN) is a 270 kDa homodimeric enzyme composed of seven functional domains, primarily responsible for catalyzing the final steps in the endogenous biosynthesis of fatty acids, which is crucial for generating the lipids required to support membrane formation, energy storage, and signal transduction in rapidly proliferating cells [ 18 ]. In normal tissues, the expression of FASN is typically low, as most cells obtain fatty acids from dietary sources. However, in cancer cells, especially those with high proliferative rates, FASN is often overregulated to meet the increased metabolic demands for lipid biosynthesis [ 19 ]. Research has identified FASN as a key oncogenic driver and a tumor antigen in aggressive breast cancers, with its overexpression closely linked to various pathological processes, including enhanced cell proliferation, evasion of apoptosis, angiogenesis, and metabolic reprogramming [ 20 – 22 ]. More importantly, FASN overexpression is strongly correlated with recurrence, metastasis, and poor clinical outcomes, highlighting its role as a potential biomarker for breast cancer prognosis [ 23 , 24 ]. Additionally, FASN contributes to cancer cell resistance to radiotherapy and chemotherapy by regulating the non-homologous end joining (NHEJ) pathway, which is a critical mechanism for repairing DNA double-strand breaks (DSBs) [ 25 ]. The NHEJ pathway is a major contributor to genomic stability in cancer cells, allowing them to survive DNA damage inflicted by therapeutic interventions [ 26 , 27 ]. By promoting efficient DNA repair, FASN enables cancer cells to withstand DNA-damaging treatments and continue proliferating, thereby contributing to treatment resistance. This resistance to DNA damage and the ability to repair DSBs is a hallmark characteristic of cancer stem cells (CSCs), which are known for their enhanced survival, self-renewal, and ability to repopulate tumors after treatment [ 28 , 29 ]. Despite the well-established role of FASN in tumor metabolism and treatment resistance, there has been little specific research investigating the relationship between FASN and breast cancer stem cells (BCSCs). Given the unique characteristics of BCSCs, such as their high tumorigenic potential, ability to drive recurrence and metastasis, and resistance to conventional therapies, understanding how FASN contributes to these processes in the context of BCSCs could reveal new therapeutic targets. The overall logical structure and research content of this article are shown in Supplementary Fig. 1. In this study, we conducted an in-depth investigation of FASN from multiple dimensions, including single-cell RNA sequencing analysis, mechanistic studies, and clinical modeling. The innovative integration of single-cell analysis with machine learning algorithms provided a comprehensive and high-resolution understanding of the role of FASN across different breast cancer sub-populations. For the first time, we revealed that FASN not only exhibits abnormal increases in expression and activity within BCSCs but also plays a pivotal role in maintaining cancer stem cell-like properties by activating the Wnt/β-catenin pathway. This discovery highlights the broader function of FASN beyond its metabolic roles, positioning it as a key oncogenic driver in BCSCs. Additionally, we identified lansoprazole as a novel drug that effectively targets FASN, and through a series of in vitro and in vivo experiments, we validated its efficacy in inhibiting FASN activity. Lansoprazole significantly suppressed the proliferation and self-renewal capacity of BCSCs and reduced their tumorigenicity by downregulating the Wnt/β-catenin signaling pathway. Furthermore, our study utilized machine learning algorithms to develop and validate a clinical prognostic model based on FASN expression, confirming its independent predictive value in breast cancer prognosis. In conclusion, these findings not only provide new theoretical support for targeting FASN as a therapeutic strategy against BCSCs but also highlight the potential of lansoprazole in inhibiting FASN activity and attenuating BCSC properties, offering a novel treatment strategy for breast cancer patients. Materials and methods Cell culture and drug treatment. The human breast cancer cell lines M3k(derived from MCF-7, showing a 3000-fold higher resistance to doxorubicin compared to MCF-7, and an endogenous FASN highly-expressed cell line), MCF-7, and MDA-MB-468 were obtained from American Type Culture Collection in this study. MCF-7 and MDA-MB-468 cells were cultured at 37˚C in a humidified atmosphere containing 5% CO 2 in Dulbecco's modified Eagle's medium (DMEM) supplemented with 10% fetal bovine serum (FBS). M3k cells were maintained under the same conditions in RPMI 1640 medium, also supplemented with 10% FBS. All cell lines were propagated for fewer than 8 passages after thawing. Lansoprazole, used as a treatment agent in this study, was purchased from Supelco (Catalog No. PHR1390). Data source and processing. The single cell sequencing data was obtained from six breast cancer patients in the GSE161529 datasets. A total of 1075 breast cancer patients with complete survival information were obtained from the TCGA database after excluding normal samples and samples from the same patient. The createDataPartition function in the Caret package was then used to randomly divide the breast cancer patients into a training cohort (N = 755) and an internal validation cohort (N = 320) in a ratio of 7:3. The GSE20685 dataset containing 327 breast cancer patients was used as an external cohort to verify the accuracy and robustness of the prognostic model. All data sets were normalized using the normalized function in the limma package before use to reduce differences due to data heterogeneity. Sc-RNA analysis. Quality control was conducted with Seurat, filtering out cells with fewer than 200 detected genes and over 15% mitochondrial content. Dimensionality reduction was performed using PCA, and clustering was visualized with UMAP. Cell types were annotated through SingleR and manual inspection based on known markers. Differential gene expressions between FASN-high and FASN-low epithelial cells were assessed using the FindMarkers function, with enrichment analysis via GSEA revealing key proliferative pathways. CytoTRACE and Monocle2 were used for pseudotime and differentiation state analysis, respectively, while inferCNV identified chromosomal aberrations in FASN-high epithelial cells. Construction of algorithm combination model. First, 8182 differential genes were identified in epithelial cells with high and low expression of FASN based on single cell sequencing analysis. After extracting these differential genes from the TCGA cohort, 99 genes significantly associated with breast cancer prognosis were further identified using univariate Cox regression analysis (p < 0.01). These genes were then utilized for subsequent machine learning to construct prediction models. The TCGA cohort was randomly divided into the training set and the internal test set using the createDataPartition function in the caret package with a ratio of 7:3, while GSE20685 served as an external validation set. A total of 101 combinations of the following nine machine learning algorithms were employed to construct prognostic models such as Random Survival Forest (RSF), Elastic Net (Enet), Stepwise Cox Proportional Hazards Regression (StepCox), Cox Model with Gradient Boosting (CoxBoost), Partial Least Squares Cox Regression (plsRcox), Supervised Principal Component Analysis (SuperPC), Gradient Boosting Machine (GBM), Survival Support Vector Machine (Survival-SVM), and Least Absolute Shrinkage and Selection Operator (Lasso). For survival analysis, Kaplan-Meier survival curves were generated using the survminer package. Time-dependent ROC curves were generated using the timeROC package to assess the predictive accuracy of the prognostic models at 1, 3, and 5 years and the results were visualized using bar plots generated with the ggplot2 package. Comparison with published models of breast cancer prognosis. To validate the practicality and superiority of our proposed breast cancer prognosis model, we reviewed articles related to breast cancer prognosis published in PubMed over the past five years to evaluate the accuracy of prognostic models (Supplementary Table 1). The models examined in this study were developed using common single machine learning algorithms, such as multivariate Cox regression. To minimize potential discrepancies due to varying datasets, we ensured that our subsequent analyses were conducted within the same dimensional parameters and remained as consistent as possible with the original studies. Gene expression levels for each model were extracted from the TCGA breast cancer gene expression matrix. We then performed multivariate Cox regression to determine the regression coefficients for each gene. The risk score for each sample was subsequently calculated. The predictive power and clinical utility of each model were evaluated using the Consistency Index (C-index), decision curve analysis (DCA), the Area Under the ROC Curve (AUC), and survival analysis. These assessments were conducted using the timeROC and survival packages in R software. Generation of FASN-Modified Cell Lines. To construct FASN overexpression and knockout cell lines, we utilized gene engineering techniques. For FASN overexpression, human FASN cDNA was cloned into the pcDNA3 vector. MCF-7 breast cancer cells were transfected with FASN-pcDNA3 or the empty pcDNA3 vector using Lipofectamine 3000 (Thermo Fisher Scientific, Inc.). After 48 hours, transfected cells were selected with 800 µg/mL G418 for two weeks to establish stable cell lines. Successfully transfected cells were then expanded for further experiments. For FASN knockdown, a shRNA sequence targeting FASN mRNA (AACCCTGAGATCC CAGCGCTG) was designed and cloned into a suitable plasmid. M3k cells with high FASN-expression were transfected with either the FASN-targeting shRNA plasmid or a scrambled shRNA control plasmid using Lipofectamine 3000. After 24 hours, transfected cells were selected with 800 µg/mL G418 for two weeks to establish stable cell lines. Clones exhibiting efficient FASN knockdown were identified and expanded for subsequent use. These methods resulted in the generation of MCF-7 cell lines with FASN overexpression and their respective controls, as well as M3k cell lines with FASN knockdown and their controls. Colony formation assay. For the colony formation assay, we utilized both M3k and MCF-7 cell lines with distinct modifications to evaluate the impact of FASN expression. M3k cells included the M3k/Scr (scrambled shRNA control) and M3k/ShFASN (FASN knockdown) groups. MCF-7 cells included the MCF-7/Vec (vector control) and MCF-7/FASN (FASN overexpression) groups. Three hundred viable cells from each group were seeded into 6-well plates (Corning) and incubated at 37°C in a 5% CO 2 atmosphere for 24 hours, repeated three times. Following the incubation period, the cells were cultured in complete medium for 10 days. Colonies were then fixed using a solution of acetic acid and methanol in a 1:7 ratio for 5 minutes. After fixation, the colonies were stained with 0.5% crystal violet at room temperature for 20 minutes, and subsequently washed three times with PBS to remove excess dye. Colonies containing more than 50 cells were counted using an inverted phase contrast microscope (Nikon Corporation). Representative images of the stained colonies were captured using a Canon scanner (CanoScan 5600F) for visualization and documentation. Sphere formation assay. In this study, we evaluated sphere formation using M3k and MCF-7 cell lines with specific genetic modifications. The M3k cell lines consisted of M3k/Scr (scrambled shRNA control) and M3k/ShFASN (FASN knockdown) groups, while the MCF-7 cell lines included MCF-7/Vec (vector control) and MCF-7/FASN (FASN overexpression) groups. To evaluate sphere formation, we plated 5,000 cells per well into 6-well ultra-low attachment plates (Corning, Inc.). The cells were cultured at 37°C in a 5% CO 2 atmosphere using a cancer stem cell (CSC) enrichment medium. This medium was composed of DMEM/F12 serum-free medium, supplemented with 2% B27, 20 ng/mL epidermal growth factor (EGF), and 20 ng/mL recombinant human basic fibroblast growth factor (RH-bFGF) (all from Gibco, Thermo Fisher Scientific, Inc.). After 7 days of incubation, the formed tumor spheres were analyzed. Spheres with a diameter of at least 50 µm were imaged and counted using an inverted phase-contrast microscope (Nikon Corporation) at 100x magnification. Western blot analysis. Cells were lysed in ice-cold RIPA lysis buffer (50 mM Tris-HCl, pH 7.4, 150 mM NaCl, 1 mM EDTA, 1 mM dithiothreitol, 1% Triton-X-100, and 0.1% sodium deoxycholate) supplemented with phosphatase inhibitor (1 mM Na 3 VO 4 ) and protease inhibitor (1 mM phenylmethylsulfonyl fluoride). The cell lysates were then centrifuged at 12,000 x g for 10 minutes at 4℃ to remove debris. Protein concentrations were measured using the Pierce Rapid Gold bicinchoninic acid (BCA) kit (Thermo Fisher Scientific, Inc.). Proteins (20–50µg per lane) were separated by SDS-PAGE on 6% or 12% gels and transferred onto Sequi-Blot PVDF membranes (Bio-Rad Laboratories, Inc.). Membranes were blocked with 5% non-fat milk in TBST (Tris-buffered saline with 0.1% Tween-20) for 2 hours at room temperature. Primary antibodies were incubated with the membranes overnight at 4℃. The specific antibodies used were FASN (affinity, product no.DF6106), APC (Proteintech, product no.19782-1-AP), Axin-1 (Bioss, product no.bs-21732R), GSK-3β (Abcam, product no. ab93926), TCF-4 (Proteintech, product no.13838-1-AP), and TCF-7 (Proteintech, product no.14464-1-AP). β-catenin and GAPDH were used as loading control. After primary antibody incubation, membranes were washed three times with TBST and then incubated with the appropriate secondary antibodies (anti-mouse, product no. A2429, or anti-rabbit, product no. A3937, from Sigma-Aldrich, Merck KGaA) at a 1:1,000 dilution for 2 hours at room temperature. Protein bands were visualized using enhanced chemiluminescence (Thermo Fisher Scientific, Inc.) and detected with an X-ray film system (Ece Scientific Co., Inc.). Band intensities were quantified using ImageJ software (version 1.52; National Institutes of Health). Enzyme-linked immunosorbent assay. Microplates were coated with purified anti-WNT3a antibodies and incubated overnight at 4°C. The following day, plates were washed with PBS containing 0.05% Tween-20 (PBS-T) to remove unbound antibodies. Subsequently, 100 µL of standards or samples were added to each well and incubated at 37°C for 2 hours. After incubation, the liquid was discarded, and the plates were tapped dry on absorbent paper. Next, 100 µL of biotinylated anti-WNT3a antibody was added to each well, followed by a 1-hour incubation at 37°C. After washing with PBS-T, 100 µL of HRP-conjugated streptavidin was added to each well and incubated for 1 hour at 37°C. The plates were then washed five times with PBS-T. To develop the color, 90 µL of TMB (3,3',5,5'-tetramethylbenzidine) substrate solution was added to each well and incubated in the dark at 37°C for 15–30 minutes. The reaction was terminated by adding 50 µL of 2N sulfuric acid to each well, turning the color from blue to yellow. The optical density (OD) of each well was measured at 450 nm using a microplate reader. The OD values were directly proportional to the concentration of WNT3a in the samples, and a standard curve was generated to determine the WNT3a concentrations in the test samples. Statistical analysis. Statistical analyses of the data were conducted using R software (version 4.3.1). All data are expressed as Mean ± SD (n = 3) except special indication. Difference was statistically significant at P < 0.05. Results 3.1 Identification and Characterization of FASN in Cell Subtypes of Breast Cancer In our previous studies, our research team has been focusing on the important biological role of FASN in breast cancer and has confirmed that it is an important potential therapeutic target in breast cancer [ 30 ]. However, there has been no literature report on the study of FASN at the single-cell level, so in this study, we used single-cell sequencing technology to explore the role of FASN in breast cancer. In the initial analysis, dimensionality reduction cluster analysis was performed on the single cell sequencing data from six breast cancer patients in the GSE161529 dataset, and a total of 16 cell cluster subsets were screened (Fig. 1 A). After further identifying the top10 marker genes for each cell cluster subsets, we annotated the above cell cluster subsets by a combination of SingleR and manual annotation (Fig. 1 B-C). The clusters were annotated according to the characteristic biomarkers expressed by the different clusters, which led to the identification of eight cell types, namely epithelial cells, endothelial cells, fibroblasts, T cells, and B cells, Tissue stem cells, macrophages, monocytes(Fig. 1 D). The analysis revealed that FASN was expressed across most cell subsets, with particularly high expression in the epithelial cells of tumor tissues (Fig. 1 E). Furthermore, there was evidence of co-expression between FASN and EPCAM, a classical tumor marker (Fig. 1 F). 3.2 FASN-Driven Stemness and Malignancy in Epithelial Cells Exploring the biological significance of FASN expression at the single-cell level, we focused on epithelial cells with high and low FASN expression which was divided using the medium value of expression. The results of pathway enrichment analysis revealed that FASN-high epithelial cells were significantly enriched in pathways critical for proliferation, metabolism, and stress responses, including MYC targets, oxidative phosphorylation, and the unfolded protein response (Fig. 2 A). These pathways are essential for the rapid growth and survival of tumor cells, highlighting the role of FASN in supporting the metabolic and proliferative demands of these cells. We further elucidate the genomic alterations linked with FASN expression by inferCNV analysis. Significant copy number variations (CNVs) were observed in FASN-high epithelial cells, particularly in genomic regions associated with tumor progression. Notably, an amplification on chromosome 8, which may contribute to enhanced fatty acid synthesis, was detected, potentially driving tumor cell proliferation. Additionally, a deletion on chromosome 17 was identified, likely involving the loss of critical tumor suppressor genes, further contributing to the aggressive nature of the tumor (Fig. 2 B). These genomic alterations emphasize the pivotal role of FASN overexpression in promoting tumor development. CytoTRACE analysis revealed that FASN-high epithelial cells were predominantly situated in regions associated with a less differentiated state, as indicated by higher predicted order values. This finding suggests that these cells may possess stem-like properties or a more proliferative nature, traits often linked to aggressive tumor behavior and poor prognosis (Fig. 2 C-D). Pseudo time analysis was conducted to investigate the trajectory of FASN expression in epithelial cell development across five distinct cellular states. The analysis showed that cells in State 2, which exhibited the highest levels of FASN and EPCAM expression, were positioned early in the developmental trajectory. In contrast, cells in States 4 and 5, with lower FASN expression, appeared later in the trajectory, indicating a progression from a highly proliferative state to more differentiated states (Fig. 2 E-F). These findings suggested that FASN expression peaks during the early stages of epithelial cell development and decreases as the cells differentiate, further confirming that FASN-high epithelial cells exhibit a higher degree of stemness, reinforcing their critical role in the early proliferative phases of tumor progression. 3.3 Identification of FASN conferring breast cancer stem cell-like properties Building on our previous findings that identified a significant role for FASN in promoting stemness and malignancy in epithelial cells, we sought to validate these observations through functional assays in breast cancer models. Specifically, we investigated the impact of FASN modulation on stemness-related phenotypes using M3k and MCF-7 cell lines, which are representative of different breast cancer subtypes. To assess the biological functions associated with FASN expression, we generated M3k/ShFASN cell lines with FASN knockdown and MCF-7/FASN cell lines with FASN overexpression. The results demonstrated that M3k/ShFASN cells exhibited significantly reduced cell growth capacity compared to the control M3k/Scr cells (Fig. 3 A). Conversely, MCF-7/FASN cells displayed substantially increased cell growth ability compared to the control MCF-7/Vec cells (Fig. 3 B). Moreover, our results revealed that M3k/ShFASN cells exhibited a significantly reduced clonogenic capacity compared to the control M3k/Scr cells. In contrast, MCF-7/FASN cells showed a substantial increase in clonogenic potential relative to the vector control MCF-7/Vec cells (Fig. 3 C). Further, sphere formation assays demonstrated that M3k/ShFASN cells formed significantly fewer and smaller spheres compared to M3k/Scr cells, indicating a loss of self-renewal capacity. Conversely, MCF-7/FASN cells displayed enhanced sphere-forming ability, producing larger and more numerous spheres than MCF-7/Vec cells (Fig. 3 D). Additionally, we observed that the expression levels of stemness-associated markers including C-myc, Oct-4A, Nanog, and Sox-2 were markedly reduced in M3k/ShFASN cells compared to M3k/Scr cells. In contrast, these markers were significantly upregulated in MCF-7/FASN cells relative to MCF-7/Vec cells (Fig. 3 E-F). Collectively, these data provide strong evidence that FASN plays a critical role in sustaining the stemness characteristics of breast cancer cells. 3.4 Interpretation of FASN mediated Wnt/β-Catenin Signaling Pathway activation enhancing stemness of breast Cancer Cells The activation of the Wnt/β-catenin signaling pathway is known to play a critical role in maintaining the properties of breast cancer stem cells. To explore the potential influence of FASN on this pathway, we conducted a series of experiments. ELISA assays demonstrated that breast cancer cells with higher FASN expression significantly increased Wnt3a secretion (Fig. 4 A). Furthermore, in FASN knockout cells, the expression of components of the β-catenin destruction complex including APC, AXIN-1, and GSK3β was elevated, while the levels of β-catenin, its downstream co-transcription factor TCF-3, and the regulatory protein TCF-7 were significantly reduced. Conversely, in cells with elevated FASN expression, APC, AXIN-1, and GSK3β were markedly downregulated, whereas β-catenin, TCF-4, and TCF-7 levels were substantially increased (Fig. 4 B-E). Collectively, these findings suggest that FASN may enhance the stem-like characteristics of breast cancer cells by activating the Wnt/β-catenin signaling pathway. 3.5 Validation of FASN-Wnt/β-catenin axis activated in breast cancer stem cells Following the observation that FASN may influence the Wnt/β-catenin signaling pathway and support the breast cancer stem-like properties, we further isolated and purified breast cancer stem cells (BCSCs) using the CD44+/CD24 − marker to precisely assess the role of FASN in these stem cell populations. Our analysis demonstrated that FASN activity and expression were significantly elevated in the CD44+/CD24 − subpopulations of MCF-7 and MDA-MB-468 breast cancer cell lines compared to their respective unselected parent cells (p < 0.01) (Fig. 5 A). Moreover, Wnt3a protein levels were significantly increased in these subpopulations (Fig. 5 B). Western blot analysis revealed a marked activation of the Wnt/β-catenin signaling pathway in these subpopulations, as indicated by substantial upregulation of β-catenin, TCF-4, and TCF-7, along with a significant downregulation of the negative regulators APC, Axin-1, and GSK-3β (Fig. 5 C). These findings strongly suggest that elevated FASN expression in breast cancer stem cells enhances the activation of the Wnt/β-catenin signaling pathway, which in turn plays a pivotal role in sustaining the stem-like properties of these cells. 3.6 Inhibition of FASN by lansoprazole killing BCSCs Given the critical role of FASN in maintaining the stem-like properties of breast cancer stem cells (BCSCs) and its involvement in the activation of the Wnt/β-catenin signaling pathway, we explored potential therapeutic agents targeting FASN. Our previous studies have demonstrated that proton pump inhibitors (PPIs), primarily used to reduce gastric acid secretion, can selectively target the thioesterase (TE) domain of FASN, thereby inhibiting its expression and enzymatic activity [ 30 ]. Building on this knowledge, we selected lansoprazole, which was found to have the potential to inhibit FASN in previous studies, as a promising therapeutic drug[ 30 ].To assess the effects of Lansoprazole on FASN in BCSCs, we treated isolated and purified BCSCs (MCF-7 (CD44 + /CD24 − ) and MDA-MB-468 (CD44 + /CD24 − )) with Lansoprazole. Western blot analysis confirmed that Lansoprazole significantly downregulated FASN protein expression in BCSCs and substantially reduced FASN enzymatic activity, indicating effective inhibition of FASN function (Fig. 6 A-B). Subsequent functional assays revealed that Lansoprazole treatment led to a significant decrease in the proliferation capacity of BCSCs (Fig. 6 C). Clonogenic assays further demonstrated that Lansoprazole markedly inhibited the colony-forming ability of these cells (Fig. 6 D). Additionally, Lansoprazole significantly impaired the spheroid formation capacity of BCSCs, indicating a pronounced inhibitory effect on their self-renewal properties (Fig. 6 E). Collectively, these findings suggest that Lansoprazole effectively suppresses both the expression and activity of FASN in breast cancer stem cells, resulting in a substantial reduction in their proliferative and self-renewal capabilities. This highlights Lansoprazole as a potential therapeutic agent targeting FASN to inhibit the stem-like characteristics of BCSCs. 3.7 Mechanisms of Lansoprazole inhibiting FASN mediated the death of BCSCs Having established that Lansoprazole effectively inhibits FASN expression and activity in BCSCs, we sought to explore the underlying mechanisms by which Lansoprazole exerts its effects on these cells. Given that the Wnt/β-catenin signaling pathway is frequently activated in BCSCs, we investigated how Lansoprazole- mediated inhibition of FASN influences this pathway. Our results demonstrated that treatment with Lansoprazole significantly reduced the nuclear translocation of β-catenin in BCSCs (Fig. 7 A-B). This was accompanied by a marked upregulation of APC, Axin-1, and GSK-3β, as well as a significant downregulation of TCF-4, TCF-7, and Wnt3a secretion (Fig. 7 C-D). These findings indicate that the anti-BCSC effects of Lansoprazole may be mediated through its inhibition of the Wnt/β-catenin signaling pathway via targeting FASN. 3.8 Validation of Lansoprazole inhibiting BCSCs growth in vivo To further validate the inhibitory effects of Lansoprazole on BCSCs and elucidate its underlying mechanisms in vivo, we established subcutaneous xenografts using BCSCs derived from MCF-7 (CD44+/CD24-) and MDA-MB-468 (CD44+/CD24-) cell lines in nude mice. Once the tumors reached approximately 50 mm³ in volume, the mice were administered 30 mg/kg Lansoprazole or an equivalent volume of DMSO as a control, every other day via oral gavage. After 21 days of treatment, the mice were sacrificed, and the tumors were excised for analysis. The results demonstrated a significant reduction in tumor growth in the Lansoprazole-treated group compared to the control group (Fig. 8 A). Both tumor weight and volume were markedly decreased in Lansoprazole-treated MCF-7 and MDA-MB-468 xenografts, indicating that Lansoprazole effectively suppressed the tumorigenic potential of BCSCs in vivo (Fig. 8 B-C). Histopathological examination using hematoxylin and eosin (H&E) staining revealed extensive necrosis and a less compact cellular structure in tumors from the Lansoprazole-treated group, suggesting inhibited tumor proliferation. Immunohistochemical analysis further supported these findings, showing significant downregulation of Ki-67, Wnt3a, and β-catenin expression in the tumor tissues following Lansoprazole treatment (Fig. 8 D). These results suggest that the anti-tumor effects of Lansoprazole are mediated by inhibiting cell proliferation and suppressing the Wnt/β-catenin signaling pathway. 3.9 Construction and validation of FASN Expression based prognostic model for Breast Cancer patients Following the recognition of FASN as a pivotal factor driving both stem-like properties and malignancy in breast cancer, we extended our investigation to bridge mechanistic insights with clinical relevance. By integrating single cell sequencing data and bulk RNA sequencing data, we differentiated epithelial cell subpopulations based on FASN expression levels and identified key genes for constructing robust prognostic models. During model construction, 101 combinations of machine learning algorithms were tested, and model performance was evaluated across multiple cohorts. The combination of Least Absolute Shrinkage and Selection Operator (Lasso) and Random Survival Forest (RSF) algorithms, named FASN-related signature, yielded the best performance, achieving an average C-index of 0.777 across the training and validation cohorts (Fig. 9 A). Specifically, LASSO regression identified 21 prognostic genes (Fig. 9 B-C), which were then further refined using the RSF algorithm to optimize model accuracy. Additionally, this model demonstrated strong predictive accuracy, with the average area under the time-dependent ROC curves (AUC) across three cohorts being 0.82 at 1 year, 0.81 at 3 years, and 0.79 at 5 years (Fig. 9 D-F). Survival analysis, which stratified patients into high-risk and low-risk groups based on the median risk score, revealed that overall survival rates were significantly lower in the high-risk groups across all cohorts (Fig. 9 G-I). These findings indicate that the model exhibits strong risk stratification capabilities across different datasets, effectively distinguishing between high-risk and low-risk patients, and holds significant potential for clinical application. Notably, the prognostic accuracy of this FASN-based model exceeded that of the integrated clinical information nomogram, which typically performs better than individual models (Supplementary Fig. 2). This anomaly underscores the superior predictive value of the FASN-related model in our study. Moreover, the prognostic performance of the FASN-related signature was validated and demonstrated superiority over multiple established breast cancer prognosis models across various metrics and datasets, further confirming its robustness and clinical relevance (Supplementary Fig. 3). These findings suggest that the FASN-related signature is a reliable and powerful tool for predicting breast cancer prognosis, offering significant advantages in clinical applications. Discussion The role of fatty acid synthase (FASN) in tumor metabolism has attracted a lot of attention because it plays a key role in supporting the anabolic processes required for tumor growth and survival [ 19 ]. Unlike normal cells, which rely primarily on foreign fatty acids, many cancer cells rely heavily on new fat production, and FASN is a key enzyme that drives this process [ 31 ]. Elevated FASN expression is associated with poor prognosis for multiple cancer types, reflecting its important role in promoting tumorigenic properties such as enhanced proliferation, survival, and metastasis [ 32 ]. Although the function of FASN in a variety of solid tumors has been extensively studied [ 33 ], research on the link between FASN and the properties of breast cancer stem cells (BCSCs) remains limited, and the specific mechanism of action is unclear. In our study, we found that the activity and expression of FASN is specifically elevated in the breast cancer stem cell subpopulation (BCSCs) for the first time. This finding is significant because BCSCs are a small subset of cells in tumors that are highly heterogeneous and have been shown to be strongly associated with tumor initiation, metastasis, drug resistance, and relapse. Thus, the high expression of FASN in these subpopulations suggests that it may not only be a key enzyme supporting metabolic needs but may also be a central factor in maintaining the stem-like properties of these cells. The Wnt/β-catenin signaling pathway plays a key role in normal tissue development and maintenance of tissue homeostasis, but its abnormal activation in tumors is closely related to the occurrence and progression of many cancers [ 34 , 35 ]. The core mechanism of this pathway involves the binding of the Wnt protein to the Frizzled receptor on the cell membrane to activate the downstream signaling cascade, thereby inhibiting the degradation of β-catenin [ 36 ]. Undegraded β-catenin is able to migrate into the nucleus, bind to transcription factors in the T cell factor/lymphoenhancer (TCF/LEF) family, and initiate the expression of a series of genes associated with cell proliferation, differentiation, and self-renewal [ 37 ]. This process plays a crucial role in maintaining the self-renewal and differentiation of normal stem cells [ 38 ]. However, when the Wnt/ beta-catenin signaling pathway is abnormally activated in cancer, it not only promotes unlimited proliferation of tumor cells, but also helps cancer stem cells (CSCs) maintain their dry characteristics, leading to tumor progression, recurrence, and resistance to treatment [ 39 , 40 ]. In breast cancer, overactivation of the Wnt/β-catenin signaling pathway is thought to be one of the important mechanisms for maintaining the characteristics of BCSCs [ 16 ]. Through the activation of this pathway, BCSCs acquire self-renewal capabilities similar to normal stem cells, but also have strong proliferation potential and resistance to traditional therapies, enabling these cells to occupy a dominant position in the tumor microenvironment [ 41 – 43 ]. Our study found that high FASN expression is associated with enhanced Wnt/β-catenin signaling, which is manifested by increased Wnt3a secretion and enhanced nuclear translocation of β-catenin. These findings suggest that FASN may play a crucial role in linking metabolic reprogramming with the activation of key signaling pathways that drive cancer stem cell (CSC) properties. The elevated expression of FASN in BCSCs not only supports their metabolic demands but also enhances their ability to sustain self-renewal and resist conventional therapies through the activation of the Wnt/β-catenin signaling pathway. In addition, our study identified lansoprazole as a potential therapeutic agent that effectively inhibits FASN activity. Lansoprazole was shown to suppress BCSC proliferation, reduce their self-renewal capacity, and diminish their tumorigenicity by downregulating Wnt/β-catenin signaling. The connection between FASN and Wnt/β-catenin signaling opens new avenues for therapeutic intervention. Targeting FASN, whether through pharmacological inhibitors like lansoprazole or genetic approaches, could disrupt this pro-tumorigenic signaling axis, reducing the stemness of BCSCs and sensitizing them to conventional therapies. Given the central role of FASN in lipid metabolism, its inhibition may also compromise the energy supply and biosynthetic demands of BCSCs, hindering their ability to maintain their aggressive phenotype. Our study further identified lansoprazole as a potential therapeutic agent that effectively inhibits FASN activity, suppresses BCSC proliferation, reduces their self-renewal capacity, and diminishes their tumorigenicity by downregulating Wnt/β-catenin signaling. These strategies could be particularly effective in combination with existing therapies that target the Wnt/β-catenin pathway or other signaling networks involved in BCSC-like properties. Building on these mechanistic insights, we extended our research into the clinical domain by developing a prognostic model based on FASN expression. By employing a combination of 101 machine learning algorithms, we constructed a robust model that demonstrated high prognostic accuracy across multiple cohorts. This model not only underscores the translational relevance of our findings from basic research to clinical applications but also highlights the potential of FASN as a critical biomarker for breast cancer prognosis. The integration of advanced machine learning techniques allowed us to capture the complex interplay between FASN expression and patient outcomes, offering a powerful tool for risk stratification and personalized treatment planning. The significant prognostic value of this model further emphasizes the importance of targeting FASN in therapeutic strategies, particularly in the context of BCSCs, which are known to drive recurrence and metastasis. This comprehensive approach, bridging fundamental research with clinical implementation, underscores the potential impact of our findings on improving breast cancer treatment and patient outcomes. Despite the significant findings of our study, several limitations should be acknowledged. First, although our research demonstrated the crucial role of FASN in BCSCs and identified lansoprazole as a potential therapeutic agent, the in vitro and in vivo models used in this study may not fully recapitulate the complexity of human breast cancer. The tumor microenvironment, immune system interactions, and patient-specific genetic variations were not fully accounted for in our models, which may affect the translatability of our findings to clinical settings. Additionally, while the prognostic model we developed based on FASN expression showed high accuracy across multiple cohorts, further validation in larger, independent clinical datasets is necessary to confirm its generalizability and clinical utility. Finally, the exact molecular mechanisms by which FASN interacts with the Wnt/β-catenin signaling pathway in BCSCs remain to be fully elucidated. Future studies should focus on exploring these mechanisms in greater detail and evaluating the efficacy of FASN-targeted therapies in combination with other treatment modalities in more clinically relevant models. In conclusion, this study provides novel insights into the critical role of FASN in breast cancer stem cell biology and its potential as a therapeutic target. We demonstrated for the first time that FASN not only exhibits elevated expression and activity in BCSCs but also plays a pivotal role in sustaining their stem-like properties through the activation of the Wnt/β-catenin signaling pathway. Moreover, we identified lansoprazole as an effective inhibitor of FASN, capable of reducing BCSC proliferation, self-renewal, and tumorigenicity by downregulating this key signaling axis. These findings bridge the gap between fundamental research and clinical application, as we further developed and validated a robust prognostic model based on FASN expression using advanced machine learning techniques. This model highlights the prognostic value of FASN in breast cancer and offers a practical tool for personalized treatment strategies. Collectively, our research not only advances the understanding of the role of FASN in breast cancer stem cells but also paves the way for new therapeutic approaches targeting BCSCs, with the potential to significantly improve patient outcomes. Declarations Funding The present study was supported by the National Natural Science Foundation of China (grant number 82473965 to TLZ), the Natural Science Foundation of Hunan Province (grant number 2023JJ50156 to TLZ, 2025JJ90146 to HHH), the Clinical Research 4310 Program of the First Affiliated Hospital of the University of South China (grant number 20224310NHYCG04 to TLZ), Science and technology innovation Program of Hengyang City (grant number 202250045223 to TLZ), the project of Hunan health high-level youth backbone talents (grant number 20240304125 to Z.T.L) and the Scientific Research Project of the Hunan Provincial Department of Education (grant number 24B0413 to HHH). Competing interests The authors state that they do not have any competing interests. Ethics approval and consent to participate This study complied with the regulations of the Animal Ethics Committee of Hunan Evidence Based Biotechnology Co., Ltd. And was approved (No.ABXZ20230321). All experimental protocols were conducted in accordance with relevant guidelines and regulations, including those outlined by the Declaration of Helsinki. Availability of data and materials All raw data used in this study are publicly available from the Gene Expression Omnibus (GEO) under the accession numbers GSE20685 and GSE161529 (https://www.ncbi.nlm.nih.gov/geo/). Additional datasets and materials can be accessed through the UCSC Xena browser (https://xenabrowser.net/). All code and data may be obtained from the corresponding author for reasonable reasons. Consent for publication Not applicable Authors’ contributions TLZ, HHH and MXZ conceived and designed the study. 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07:53:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7324806/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7324806/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89703773,"identity":"9c80b188-f477-4638-b8af-0766b80dfc93","added_by":"auto","created_at":"2025-08-22 22:01:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4609230,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle cell transcriptome atlas of breast cancer.\u003c/strong\u003e (A) Dimension reduction cluster distribution map based on tSNE. (B) The top 10 marker genes of each cell subpopulation. (C) Marker genes for each specific cell type. (D) tSNE dimension reduction profile for specific cell types. (E) The Expression of FASN in different cell types. (F) Density profile of FASN and EPCAM.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7324806/v1/a7eb405414d2756aae277103.png"},{"id":89703619,"identity":"630a8ae2-cb63-4408-995a-8113f62fa9bd","added_by":"auto","created_at":"2025-08-22 21:53:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3897959,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of FASN Expression in Epithelial Cells and Associated Pathways.\u003c/strong\u003e(A) Pathway enrichment analysis of different cell types such as FASN-high and FASN-low epithelial cells. (B) InferCNV analysis revealing copy number variations in FASN-high epithelial cells. (C-D) CytoTRACE analysis showing that FASN-high epithelial cells are in less differentiated regions. (E-F) Pseudotime analysis illustrating the developmental trajectory of epithelial cells.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7324806/v1/1d6ecc5c37fbdab7fc46ca3f.png"},{"id":89703623,"identity":"235b9de3-0841-4be3-b0b1-bb3ca68adf8f","added_by":"auto","created_at":"2025-08-22 21:53:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1897439,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Effect of FASN on the characteristics of breast cancer cells. \u003c/strong\u003e(A-D) Overexpression of FASN in MCF-7 cells significantly increased both colony formation ability and sphere formation, including the size and number of spheres (Right). Conversely, the knockdown of FASN in M3k cells reduced colony formation, sphere size, and the number of spheres (Left). (G-J) Western blot analysis showed that stemness-associated markers (C-myc, Oct-4A, Nanog, Sox-2) were downregulated in M3k/ShFASN cells compared to M3k/Scr, while FASN overexpression in MCF-7 cells upregulated these markers. (Data are presented as mean ± standard deviation, n = 3, *P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7324806/v1/3211ade84ee4da13b1d59539.png"},{"id":89703637,"identity":"db6f9aa6-9479-4e17-a0d5-171036f2fa8e","added_by":"auto","created_at":"2025-08-22 21:53:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1454795,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe effect of FASN on the Wnt/β-catenin signaling pathway. \u003c/strong\u003e(A) Wnt3a secretion was significantly reduced after FASN knockdown in M3k cells, while Wnt3a over-secretion was observed in MCF-7 cells overexpressing FASN. (B) Western blot analysis revealed changes in the expression of key proteins in the β-catenin signaling pathway after FASN knockdown in M3k cells. (C) Quantification shows the differences in the expression of key proteins of β-catenin pathway between M3k/Scr and M3k/ShFASN cells using Image J. (D) Western blot analysis shows the altered expression of key proteins of β-catenin pathway after FASN overexpression in MCF-7 cells. (E) Quantification shows the differences in the expression of key proteins of β-catenin pathway between MCF-7/Vec and MCF-7/FASN using Image J. (Data are presented as mean ± standard deviation, n = 3, *P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7324806/v1/53be9cea5c481f5adda40f4b.png"},{"id":89703633,"identity":"9f0f5403-6166-414e-8463-499273bf85de","added_by":"auto","created_at":"2025-08-22 21:53:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1503585,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe expression level and activity of FASN and activation of Wnt/β-catenin signaling pathway in BCSCs.\u003c/strong\u003e (A) Differences in FASN activity between BCSCs and parent cells. (B) The difference of Wnt3a content in the supernatant culture medium of CD44+CD24--BCSCs and maternal cells. (C) The expression changes of FASN and key proteins of Wnt/β-catenin signaling pathway in BCSCs (MCF-7(CD44+CD24-) and MDA-MB-468(CD44+CD24-) compared with parent cells MCF-7 and MDA-MB-468. (Data expressed as mean ± standard deviation, n=3, *P\u0026lt;0.05, **P\u0026lt;0.01, ***P\u0026lt;0.001)\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7324806/v1/2b98728656d81dcc61b816e7.png"},{"id":89703770,"identity":"f882da01-95e7-4f84-a325-9b7f1318ea91","added_by":"auto","created_at":"2025-08-22 22:01:54","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2098149,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Effects of proton pump inhibitor Lansoprazole on FASN expression, activity and characteristics of BCSCs.\u003c/strong\u003e (A) Lansoprazole significantly inhibited FASN expression in BCSCs. (B) Lansoprazole significantly inhibited FASN activity in BCSCs. (C) Lansoprazole significantly inhibited the proliferation of BCSCs. (D) Lansoprazole significantly inhibited the colony-forming ability of BCSCs. (E) Lansoprazole significantly inhibited the spheroid formation capacity of BCSCs. (Data expressed as mean ± standard deviation, n=3, *P\u0026lt;0.05, **P\u0026lt;0.01, ***P\u0026lt;0.001)\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7324806/v1/c25a8db1914cfeff51c8cef3.png"},{"id":89703617,"identity":"5c39a663-9bf4-4cef-aef9-2e349aa5ea49","added_by":"auto","created_at":"2025-08-22 21:53:53","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":3220375,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Effect of lansoprazole on Wnt/β-catenin signaling pathway in BCSCs. \u003c/strong\u003e(A-B) Lansoprazole significantly inhibited β-catenin nuclear translocation in BCSCs. (C) Lansoprazole significantly regulates key signaling proteins in Wnt/β-catenin signaling pathway in BCSCs. (D) Lansoprazole significantly inhibited Wnt3a secretion in BCSCs. (Data expressed as mean ± standard deviation, n=3, *P\u0026lt;0.05, **P\u0026lt;0.01, ***P\u0026lt;0.001)\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7324806/v1/587b0278b4710f3dab5031b7.png"},{"id":89703616,"identity":"32addf0b-29ce-4978-b6c9-b633c48cbee0","added_by":"auto","created_at":"2025-08-22 21:53:53","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":6905331,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Effect of proton pump inhibitor lansoprazole on tumor formation of BCSCs in animal models.\u003c/strong\u003e (A) Examples of animal models of Lansoprazole inhibiting BCSCs tumor formation. (B) Lansoprazole inhibited tumor formation weight of BCSCs. (C) Lansoprazole inhibited tumor size of BCSCs. (D) Lansoprazole inhibited the expression of Ki-67, Wnt3a and β-catenin in tumorigenic BCSCs. (Data expressed as mean ± standard deviation, n=6, **P\u0026lt;0.01, ***P\u0026lt;0.001)\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7324806/v1/4fc75d0424ca2ed44064344e.png"},{"id":89703640,"identity":"4164e2c2-206f-44f9-8193-cb6bbd739d96","added_by":"auto","created_at":"2025-08-22 21:53:55","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":4450084,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction of breast cancer prognostic model.\u003c/strong\u003e (A) The 101 machine learning combination algorithms were used to construct breast cancer prognosis models. (B-C) Least absolute shrinkage and selection operator regression algorithm screened prognostic related feature genes. (D-F) Receiver Operating Characteristic curve and areas under curves for 1, 3, and 5 years. Survival analysis of patients in high and low risk groups in TCGA training cohort (G), TCGA validation cohort (H), and GSE20685 cohort (I).\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-7324806/v1/ed35c07279ab2fbe90f0a5d3.png"},{"id":89704002,"identity":"9d242a56-c264-4af4-91a7-52b65419abbf","added_by":"auto","created_at":"2025-08-22 22:10:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":32800946,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7324806/v1/b6c862d8-0101-4123-87b6-c5f0b05c3884.pdf"},{"id":89703618,"identity":"b5a71101-9b86-4b8d-802a-d9359eaf780c","added_by":"auto","created_at":"2025-08-22 21:53:53","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":9694960,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableandFigure.docx","url":"https://assets-eu.researchsquare.com/files/rs-7324806/v1/bbf3b24d468d9293033a6ba3.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrating Single-cell and Machine Learning with Experimental Validation Reveals FASN Conferring Breast Cancer Stem cell-like Properties: Therapeutic Insights and Prognostic Implications","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBreast cancer is a malignant tumor that predominantly arises from breast tissue in women. According to the authoritative data released by the International Agency for Research on Cancer (IARC) of the World Health Organization (WHO), in 2020, there were 2.3\u0026nbsp;million new cases of breast cancer globally, accounting for 11.7% of all cancer diagnoses worldwide. Additionally, more than 685,000 women succumbed to the disease, with both the incidence and mortality rates continuing to rise [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Current treatment modalities for breast cancer include surgery, radiotherapy, chemotherapy, targeted biological therapies, and immunotherapy [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Despite the significant advancements in early detection and therapeutic strategies driven by progress in biotechnology and clinical medicine, which have led to improvements in the quality of life and survival rates for patients with early-stage breast cancer, substantial challenges persist [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Recurrence, metastasis, and the development of resistance to treatment remain the primary factors contributing to poor clinical outcomes, with the five-year survival rate for advanced breast cancer remaining as low as 20% [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The limited understanding of the molecular mechanisms underlying these processes has hindered the development of effective therapeutic interventions [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Therefore, elucidating the molecular mechanisms that drive breast cancer recurrence, metastasis, and therapeutic resistance, and identifying novel therapeutic targets, are critical challenges that must be addressed to improve clinical outcomes in breast cancer treatment.\u003c/p\u003e\u003cp\u003eRecent studies have identified a small subpopulation within breast tumors, known as BCSCs, which are characterized by their self-renewal capacity, multipotent differentiation potential, high tumorigenicity, and marked resistance to conventional therapies, and are closely associated with breast cancer recurrence, metastasis, treatment resistance, and poor clinical outcomes [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The signaling pathways such as Notch, Wnt, and Hedgehog, which regulate cell self-renewal and differentiation, are often hyperactivated in BCSCs [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This overactivation enhances the stemness properties of BCSCs, making them highly tumorigenic and more prone to forming recurrent lesions [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Moreover, BCSCs are known to express high levels of various transport proteins, such as multidrug resistance protein 1 (MDR1), MRP1, and BCRP1, as well as DNA repair-related proteins like Ung, Uhrf1, and Xrcc5. These proteins contribute to the resistance of BCSCs to chemotherapy and radiotherapy, rendering conventional treatments less effective against this subpopulation [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In summary, breast cancer stem cells play an important role in the process of breast cancer recurrence, metastasis and treatment resistance, and are an important reason for breast cancer recurrence and metastasis. Therefore, targeted tumor stem cell therapy is expected to break through the bottleneck of current clinical breast cancer treatment.\u003c/p\u003e\u003cp\u003eFatty acid synthase (FASN) is a 270 kDa homodimeric enzyme composed of seven functional domains, primarily responsible for catalyzing the final steps in the endogenous biosynthesis of fatty acids, which is crucial for generating the lipids required to support membrane formation, energy storage, and signal transduction in rapidly proliferating cells [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In normal tissues, the expression of FASN is typically low, as most cells obtain fatty acids from dietary sources. However, in cancer cells, especially those with high proliferative rates, FASN is often overregulated to meet the increased metabolic demands for lipid biosynthesis [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Research has identified FASN as a key oncogenic driver and a tumor antigen in aggressive breast cancers, with its overexpression closely linked to various pathological processes, including enhanced cell proliferation, evasion of apoptosis, angiogenesis, and metabolic reprogramming [\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. More importantly, FASN overexpression is strongly correlated with recurrence, metastasis, and poor clinical outcomes, highlighting its role as a potential biomarker for breast cancer prognosis [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Additionally, FASN contributes to cancer cell resistance to radiotherapy and chemotherapy by regulating the non-homologous end joining (NHEJ) pathway, which is a critical mechanism for repairing DNA double-strand breaks (DSBs) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The NHEJ pathway is a major contributor to genomic stability in cancer cells, allowing them to survive DNA damage inflicted by therapeutic interventions [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. By promoting efficient DNA repair, FASN enables cancer cells to withstand DNA-damaging treatments and continue proliferating, thereby contributing to treatment resistance. This resistance to DNA damage and the ability to repair DSBs is a hallmark characteristic of cancer stem cells (CSCs), which are known for their enhanced survival, self-renewal, and ability to repopulate tumors after treatment [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Despite the well-established role of FASN in tumor metabolism and treatment resistance, there has been little specific research investigating the relationship between FASN and breast cancer stem cells (BCSCs). Given the unique characteristics of BCSCs, such as their high tumorigenic potential, ability to drive recurrence and metastasis, and resistance to conventional therapies, understanding how FASN contributes to these processes in the context of BCSCs could reveal new therapeutic targets.\u003c/p\u003e\u003cp\u003eThe overall logical structure and research content of this article are shown in Supplementary Fig.\u0026nbsp;1. In this study, we conducted an in-depth investigation of FASN from multiple dimensions, including single-cell RNA sequencing analysis, mechanistic studies, and clinical modeling. The innovative integration of single-cell analysis with machine learning algorithms provided a comprehensive and high-resolution understanding of the role of FASN across different breast cancer sub-populations. For the first time, we revealed that FASN not only exhibits abnormal increases in expression and activity within BCSCs but also plays a pivotal role in maintaining cancer stem cell-like properties by activating the Wnt/β-catenin pathway. This discovery highlights the broader function of FASN beyond its metabolic roles, positioning it as a key oncogenic driver in BCSCs. Additionally, we identified lansoprazole as a novel drug that effectively targets FASN, and through a series of in vitro and in vivo experiments, we validated its efficacy in inhibiting FASN activity. Lansoprazole significantly suppressed the proliferation and self-renewal capacity of BCSCs and reduced their tumorigenicity by downregulating the Wnt/β-catenin signaling pathway. Furthermore, our study utilized machine learning algorithms to develop and validate a clinical prognostic model based on FASN expression, confirming its independent predictive value in breast cancer prognosis. In conclusion, these findings not only provide new theoretical support for targeting FASN as a therapeutic strategy against BCSCs but also highlight the potential of lansoprazole in inhibiting FASN activity and attenuating BCSC properties, offering a novel treatment strategy for breast cancer patients.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cb\u003eCell culture and drug treatment.\u003c/b\u003e The human breast cancer cell lines M3k(derived from MCF-7, showing a 3000-fold higher resistance to doxorubicin compared to MCF-7, and an endogenous FASN highly-expressed cell line), MCF-7, and MDA-MB-468 were obtained from American Type Culture Collection in this study. MCF-7 and MDA-MB-468 cells were cultured at 37˚C in a humidified atmosphere containing 5% CO\u003csub\u003e2\u003c/sub\u003e in Dulbecco's modified Eagle's medium (DMEM) supplemented with 10% fetal bovine serum (FBS). M3k cells were maintained under the same conditions in RPMI 1640 medium, also supplemented with 10% FBS. All cell lines were propagated for fewer than 8 passages after thawing. Lansoprazole, used as a treatment agent in this study, was purchased from Supelco (Catalog No. PHR1390).\u003c/p\u003e\u003cp\u003e\u003cb\u003eData source and processing.\u003c/b\u003e The single cell sequencing data was obtained from six breast cancer patients in the GSE161529 datasets. A total of 1075 breast cancer patients with complete survival information were obtained from the TCGA database after excluding normal samples and samples from the same patient. The createDataPartition function in the Caret package was then used to randomly divide the breast cancer patients into a training cohort (N\u0026thinsp;=\u0026thinsp;755) and an internal validation cohort (N\u0026thinsp;=\u0026thinsp;320) in a ratio of 7:3. The GSE20685 dataset containing 327 breast cancer patients was used as an external cohort to verify the accuracy and robustness of the prognostic model. All data sets were normalized using the normalized function in the limma package before use to reduce differences due to data heterogeneity.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSc-RNA analysis.\u003c/b\u003e Quality control was conducted with Seurat, filtering out cells with fewer than 200 detected genes and over 15% mitochondrial content. Dimensionality reduction was performed using PCA, and clustering was visualized with UMAP. Cell types were annotated through SingleR and manual inspection based on known markers. Differential gene expressions between FASN-high and FASN-low epithelial cells were assessed using the FindMarkers function, with enrichment analysis via GSEA revealing key proliferative pathways. CytoTRACE and Monocle2 were used for pseudotime and differentiation state analysis, respectively, while inferCNV identified chromosomal aberrations in FASN-high epithelial cells.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConstruction of algorithm combination model.\u003c/b\u003e First, 8182 differential genes were identified in epithelial cells with high and low expression of FASN based on single cell sequencing analysis. After extracting these differential genes from the TCGA cohort, 99 genes significantly associated with breast cancer prognosis were further identified using univariate Cox regression analysis (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). These genes were then utilized for subsequent machine learning to construct prediction models. The TCGA cohort was randomly divided into the training set and the internal test set using the createDataPartition function in the caret package with a ratio of 7:3, while GSE20685 served as an external validation set. A total of 101 combinations of the following nine machine learning algorithms were employed to construct prognostic models such as Random Survival Forest (RSF), Elastic Net (Enet), Stepwise Cox Proportional Hazards Regression (StepCox), Cox Model with Gradient Boosting (CoxBoost), Partial Least Squares Cox Regression (plsRcox), Supervised Principal Component Analysis (SuperPC), Gradient Boosting Machine (GBM), Survival Support Vector Machine (Survival-SVM), and Least Absolute Shrinkage and Selection Operator (Lasso). For survival analysis, Kaplan-Meier survival curves were generated using the survminer package. Time-dependent ROC curves were generated using the timeROC package to assess the predictive accuracy of the prognostic models at 1, 3, and 5 years and the results were visualized using bar plots generated with the ggplot2 package.\u003c/p\u003e\u003cp\u003e\u003cb\u003eComparison with published models of breast cancer prognosis.\u003c/b\u003e To validate the practicality and superiority of our proposed breast cancer prognosis model, we reviewed articles related to breast cancer prognosis published in PubMed over the past five years to evaluate the accuracy of prognostic models (Supplementary Table\u0026nbsp;1). The models examined in this study were developed using common single machine learning algorithms, such as multivariate Cox regression. To minimize potential discrepancies due to varying datasets, we ensured that our subsequent analyses were conducted within the same dimensional parameters and remained as consistent as possible with the original studies. Gene expression levels for each model were extracted from the TCGA breast cancer gene expression matrix. We then performed multivariate Cox regression to determine the regression coefficients for each gene. The risk score for each sample was subsequently calculated. The predictive power and clinical utility of each model were evaluated using the Consistency Index (C-index), decision curve analysis (DCA), the Area Under the ROC Curve (AUC), and survival analysis. These assessments were conducted using the timeROC and survival packages in R software.\u003c/p\u003e\u003cp\u003e\u003cb\u003eGeneration of FASN-Modified Cell Lines.\u003c/b\u003e To construct FASN overexpression and knockout cell lines, we utilized gene engineering techniques. For FASN overexpression, human FASN cDNA was cloned into the pcDNA3 vector. MCF-7 breast cancer cells were transfected with FASN-pcDNA3 or the empty pcDNA3 vector using Lipofectamine 3000 (Thermo Fisher Scientific, Inc.). After 48 hours, transfected cells were selected with 800 \u0026micro;g/mL G418 for two weeks to establish stable cell lines. Successfully transfected cells were then expanded for further experiments. For FASN knockdown, a shRNA sequence targeting FASN mRNA (AACCCTGAGATCC CAGCGCTG) was designed and cloned into a suitable plasmid. M3k cells with high FASN-expression were transfected with either the FASN-targeting shRNA plasmid or a scrambled shRNA control plasmid using Lipofectamine 3000. After 24 hours, transfected cells were selected with 800 \u0026micro;g/mL G418 for two weeks to establish stable cell lines. Clones exhibiting efficient FASN knockdown were identified and expanded for subsequent use. These methods resulted in the generation of MCF-7 cell lines with FASN overexpression and their respective controls, as well as M3k cell lines with FASN knockdown and their controls.\u003c/p\u003e\u003cp\u003e\u003cb\u003eColony formation assay.\u003c/b\u003e For the colony formation assay, we utilized both M3k and MCF-7 cell lines with distinct modifications to evaluate the impact of FASN expression. M3k cells included the M3k/Scr (scrambled shRNA control) and M3k/ShFASN (FASN knockdown) groups. MCF-7 cells included the MCF-7/Vec (vector control) and MCF-7/FASN (FASN overexpression) groups. Three hundred viable cells from each group were seeded into 6-well plates (Corning) and incubated at 37\u0026deg;C in a 5% CO\u003csub\u003e2\u003c/sub\u003e atmosphere for 24 hours, repeated three times. Following the incubation period, the cells were cultured in complete medium for 10 days. Colonies were then fixed using a solution of acetic acid and methanol in a 1:7 ratio for 5 minutes. After fixation, the colonies were stained with 0.5% crystal violet at room temperature for 20 minutes, and subsequently washed three times with PBS to remove excess dye. Colonies containing more than 50 cells were counted using an inverted phase contrast microscope (Nikon Corporation). Representative images of the stained colonies were captured using a Canon scanner (CanoScan 5600F) for visualization and documentation.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSphere formation assay.\u003c/b\u003e In this study, we evaluated sphere formation using M3k and MCF-7 cell lines with specific genetic modifications. The M3k cell lines consisted of M3k/Scr (scrambled shRNA control) and M3k/ShFASN (FASN knockdown) groups, while the MCF-7 cell lines included MCF-7/Vec (vector control) and MCF-7/FASN (FASN overexpression) groups. To evaluate sphere formation, we plated 5,000 cells per well into 6-well ultra-low attachment plates (Corning, Inc.). The cells were cultured at 37\u0026deg;C in a 5% CO\u003csub\u003e2\u003c/sub\u003e atmosphere using a cancer stem cell (CSC) enrichment medium. This medium was composed of DMEM/F12 serum-free medium, supplemented with 2% B27, 20 ng/mL epidermal growth factor (EGF), and 20 ng/mL recombinant human basic fibroblast growth factor (RH-bFGF) (all from Gibco, Thermo Fisher Scientific, Inc.). After 7 days of incubation, the formed tumor spheres were analyzed. Spheres with a diameter of at least 50 \u0026micro;m were imaged and counted using an inverted phase-contrast microscope (Nikon Corporation) at 100x magnification.\u003c/p\u003e\u003cp\u003e\u003cb\u003eWestern blot analysis.\u003c/b\u003e Cells were lysed in ice-cold RIPA lysis buffer (50 mM Tris-HCl, pH 7.4, 150 mM NaCl, 1 mM EDTA, 1 mM dithiothreitol, 1% Triton-X-100, and 0.1% sodium deoxycholate) supplemented with phosphatase inhibitor (1 mM Na\u003csub\u003e3\u003c/sub\u003eVO\u003csub\u003e4\u003c/sub\u003e) and protease inhibitor (1 mM phenylmethylsulfonyl fluoride). The cell lysates were then centrifuged at 12,000 x g for 10 minutes at 4℃ to remove debris. Protein concentrations were measured using the Pierce Rapid Gold bicinchoninic acid (BCA) kit (Thermo Fisher Scientific, Inc.). Proteins (20\u0026ndash;50\u0026micro;g per lane) were separated by SDS-PAGE on 6% or 12% gels and transferred onto Sequi-Blot PVDF membranes (Bio-Rad Laboratories, Inc.). Membranes were blocked with 5% non-fat milk in TBST (Tris-buffered saline with 0.1% Tween-20) for 2 hours at room temperature. Primary antibodies were incubated with the membranes overnight at 4℃. The specific antibodies used were FASN (affinity, product no.DF6106), APC (Proteintech, product no.19782-1-AP), Axin-1 (Bioss, product no.bs-21732R), GSK-3β (Abcam, product no. ab93926), TCF-4 (Proteintech, product no.13838-1-AP), and TCF-7 (Proteintech, product no.14464-1-AP). β-catenin and GAPDH were used as loading control. After primary antibody incubation, membranes were washed three times with TBST and then incubated with the appropriate secondary antibodies (anti-mouse, product no. A2429, or anti-rabbit, product no. A3937, from Sigma-Aldrich, Merck KGaA) at a 1:1,000 dilution for 2 hours at room temperature. Protein bands were visualized using enhanced chemiluminescence (Thermo Fisher Scientific, Inc.) and detected with an X-ray film system (Ece Scientific Co., Inc.). Band intensities were quantified using ImageJ software (version 1.52; National Institutes of Health).\u003c/p\u003e\u003cp\u003e\u003cb\u003eEnzyme-linked immunosorbent assay.\u003c/b\u003e Microplates were coated with purified anti-WNT3a antibodies and incubated overnight at 4\u0026deg;C. The following day, plates were washed with PBS containing 0.05% Tween-20 (PBS-T) to remove unbound antibodies. Subsequently, 100 \u0026micro;L of standards or samples were added to each well and incubated at 37\u0026deg;C for 2 hours. After incubation, the liquid was discarded, and the plates were tapped dry on absorbent paper. Next, 100 \u0026micro;L of biotinylated anti-WNT3a antibody was added to each well, followed by a 1-hour incubation at 37\u0026deg;C. After washing with PBS-T, 100 \u0026micro;L of HRP-conjugated streptavidin was added to each well and incubated for 1 hour at 37\u0026deg;C. The plates were then washed five times with PBS-T. To develop the color, 90 \u0026micro;L of TMB (3,3',5,5'-tetramethylbenzidine) substrate solution was added to each well and incubated in the dark at 37\u0026deg;C for 15\u0026ndash;30 minutes. The reaction was terminated by adding 50 \u0026micro;L of 2N sulfuric acid to each well, turning the color from blue to yellow. The optical density (OD) of each well was measured at 450 nm using a microplate reader. The OD values were directly proportional to the concentration of WNT3a in the samples, and a standard curve was generated to determine the WNT3a concentrations in the test samples.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStatistical analysis.\u003c/b\u003e Statistical analyses of the data were conducted using R software (version 4.3.1). All data are expressed as Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (n\u0026thinsp;=\u0026thinsp;3) except special indication. Difference was statistically significant at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Identification and Characterization of FASN in Cell Subtypes of Breast Cancer\u003c/h2\u003e\u003cp\u003eIn our previous studies, our research team has been focusing on the important biological role of FASN in breast cancer and has confirmed that it is an important potential therapeutic target in breast cancer [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. However, there has been no literature report on the study of FASN at the single-cell level, so in this study, we used single-cell sequencing technology to explore the role of FASN in breast cancer. In the initial analysis, dimensionality reduction cluster analysis was performed on the single cell sequencing data from six breast cancer patients in the GSE161529 dataset, and a total of 16 cell cluster subsets were screened (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). After further identifying the top10 marker genes for each cell cluster subsets, we annotated the above cell cluster subsets by a combination of SingleR and manual annotation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB-C). The clusters were annotated according to the characteristic biomarkers expressed by the different clusters, which led to the identification of eight cell types, namely epithelial cells, endothelial cells, fibroblasts, T cells, and B cells, Tissue stem cells, macrophages, monocytes(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). The analysis revealed that FASN was expressed across most cell subsets, with particularly high expression in the epithelial cells of tumor tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Furthermore, there was evidence of co-expression between FASN and EPCAM, a classical tumor marker (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e3.2 FASN-Driven Stemness and Malignancy in Epithelial Cells\u003c/h2\u003e\u003cp\u003eExploring the biological significance of FASN expression at the single-cell level, we focused on epithelial cells with high and low FASN expression which was divided using the medium value of expression. The results of pathway enrichment analysis revealed that FASN-high epithelial cells were significantly enriched in pathways critical for proliferation, metabolism, and stress responses, including MYC targets, oxidative phosphorylation, and the unfolded protein response (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). These pathways are essential for the rapid growth and survival of tumor cells, highlighting the role of FASN in supporting the metabolic and proliferative demands of these cells. We further elucidate the genomic alterations linked with FASN expression by inferCNV analysis. Significant copy number variations (CNVs) were observed in FASN-high epithelial cells, particularly in genomic regions associated with tumor progression. Notably, an amplification on chromosome 8, which may contribute to enhanced fatty acid synthesis, was detected, potentially driving tumor cell proliferation. Additionally, a deletion on chromosome 17 was identified, likely involving the loss of critical tumor suppressor genes, further contributing to the aggressive nature of the tumor (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). These genomic alterations emphasize the pivotal role of FASN overexpression in promoting tumor development. CytoTRACE analysis revealed that FASN-high epithelial cells were predominantly situated in regions associated with a less differentiated state, as indicated by higher predicted order values. This finding suggests that these cells may possess stem-like properties or a more proliferative nature, traits often linked to aggressive tumor behavior and poor prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC-D). Pseudo time analysis was conducted to investigate the trajectory of FASN expression in epithelial cell development across five distinct cellular states. The analysis showed that cells in State 2, which exhibited the highest levels of FASN and EPCAM expression, were positioned early in the developmental trajectory. In contrast, cells in States 4 and 5, with lower FASN expression, appeared later in the trajectory, indicating a progression from a highly proliferative state to more differentiated states (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE-F). These findings suggested that FASN expression peaks during the early stages of epithelial cell development and decreases as the cells differentiate, further confirming that FASN-high epithelial cells exhibit a higher degree of stemness, reinforcing their critical role in the early proliferative phases of tumor progression.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Identification of FASN conferring breast cancer stem cell-like properties\u003c/h2\u003e\u003cp\u003eBuilding on our previous findings that identified a significant role for FASN in promoting stemness and malignancy in epithelial cells, we sought to validate these observations through functional assays in breast cancer models. Specifically, we investigated the impact of FASN modulation on stemness-related phenotypes using M3k and MCF-7 cell lines, which are representative of different breast cancer subtypes. To assess the biological functions associated with FASN expression, we generated M3k/ShFASN cell lines with FASN knockdown and MCF-7/FASN cell lines with FASN overexpression. The results demonstrated that M3k/ShFASN cells exhibited significantly reduced cell growth capacity compared to the control M3k/Scr cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Conversely, MCF-7/FASN cells displayed substantially increased cell growth ability compared to the control MCF-7/Vec cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Moreover, our results revealed that M3k/ShFASN cells exhibited a significantly reduced clonogenic capacity compared to the control M3k/Scr cells. In contrast, MCF-7/FASN cells showed a substantial increase in clonogenic potential relative to the vector control MCF-7/Vec cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Further, sphere formation assays demonstrated that M3k/ShFASN cells formed significantly fewer and smaller spheres compared to M3k/Scr cells, indicating a loss of self-renewal capacity. Conversely, MCF-7/FASN cells displayed enhanced sphere-forming ability, producing larger and more numerous spheres than MCF-7/Vec cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Additionally, we observed that the expression levels of stemness-associated markers including C-myc, Oct-4A, Nanog, and Sox-2 were markedly reduced in M3k/ShFASN cells compared to M3k/Scr cells. In contrast, these markers were significantly upregulated in MCF-7/FASN cells relative to MCF-7/Vec cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE-F). Collectively, these data provide strong evidence that FASN plays a critical role in sustaining the stemness characteristics of breast cancer cells.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Interpretation of FASN mediated Wnt/β-Catenin Signaling Pathway activation enhancing stemness of breast Cancer Cells\u003c/h2\u003e\u003cp\u003eThe activation of the Wnt/β-catenin signaling pathway is known to play a critical role in maintaining the properties of breast cancer stem cells. To explore the potential influence of FASN on this pathway, we conducted a series of experiments. ELISA assays demonstrated that breast cancer cells with higher FASN expression significantly increased Wnt3a secretion (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Furthermore, in FASN knockout cells, the expression of components of the β-catenin destruction complex including APC, AXIN-1, and GSK3β was elevated, while the levels of β-catenin, its downstream co-transcription factor TCF-3, and the regulatory protein TCF-7 were significantly reduced. Conversely, in cells with elevated FASN expression, APC, AXIN-1, and GSK3β were markedly downregulated, whereas β-catenin, TCF-4, and TCF-7 levels were substantially increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-E). Collectively, these findings suggest that FASN may enhance the stem-like characteristics of breast cancer cells by activating the Wnt/β-catenin signaling pathway.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Validation of FASN-Wnt/β-catenin axis activated in breast cancer stem cells\u003c/h2\u003e\u003cp\u003eFollowing the observation that FASN may influence the Wnt/β-catenin signaling pathway and support the breast cancer stem-like properties, we further isolated and purified breast cancer stem cells (BCSCs) using the CD44+/CD24\u0026thinsp;\u0026minus;\u0026thinsp;marker to precisely assess the role of FASN in these stem cell populations. Our analysis demonstrated that FASN activity and expression were significantly elevated in the CD44+/CD24\u0026thinsp;\u0026minus;\u0026thinsp;subpopulations of MCF-7 and MDA-MB-468 breast cancer cell lines compared to their respective unselected parent cells (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Moreover, Wnt3a protein levels were significantly increased in these subpopulations (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Western blot analysis revealed a marked activation of the Wnt/β-catenin signaling pathway in these subpopulations, as indicated by substantial upregulation of β-catenin, TCF-4, and TCF-7, along with a significant downregulation of the negative regulators APC, Axin-1, and GSK-3β (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). These findings strongly suggest that elevated FASN expression in breast cancer stem cells enhances the activation of the Wnt/β-catenin signaling pathway, which in turn plays a pivotal role in sustaining the stem-like properties of these cells.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Inhibition of FASN by lansoprazole killing BCSCs\u003c/h2\u003e\u003cp\u003eGiven the critical role of FASN in maintaining the stem-like properties of breast cancer stem cells (BCSCs) and its involvement in the activation of the Wnt/β-catenin signaling pathway, we explored potential therapeutic agents targeting FASN. Our previous studies have demonstrated that proton pump inhibitors (PPIs), primarily used to reduce gastric acid secretion, can selectively target the thioesterase (TE) domain of FASN, thereby inhibiting its expression and enzymatic activity [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Building on this knowledge, we selected lansoprazole, which was found to have the potential to inhibit FASN in previous studies, as a promising therapeutic drug[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].To assess the effects of Lansoprazole on FASN in BCSCs, we treated isolated and purified BCSCs (MCF-7 (CD44\u003csup\u003e+\u003c/sup\u003e/CD24\u003csup\u003e\u0026minus;\u003c/sup\u003e) and MDA-MB-468 (CD44\u003csup\u003e+\u003c/sup\u003e/CD24\u003csup\u003e\u0026minus;\u003c/sup\u003e)) with Lansoprazole. Western blot analysis confirmed that Lansoprazole significantly downregulated FASN protein expression in BCSCs and substantially reduced FASN enzymatic activity, indicating effective inhibition of FASN function (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-B). Subsequent functional assays revealed that Lansoprazole treatment led to a significant decrease in the proliferation capacity of BCSCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Clonogenic assays further demonstrated that Lansoprazole markedly inhibited the colony-forming ability of these cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). Additionally, Lansoprazole significantly impaired the spheroid formation capacity of BCSCs, indicating a pronounced inhibitory effect on their self-renewal properties (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). Collectively, these findings suggest that Lansoprazole effectively suppresses both the expression and activity of FASN in breast cancer stem cells, resulting in a substantial reduction in their proliferative and self-renewal capabilities. This highlights Lansoprazole as a potential therapeutic agent targeting FASN to inhibit the stem-like characteristics of BCSCs.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.7 Mechanisms of Lansoprazole inhibiting FASN mediated the death of BCSCs\u003c/h2\u003e\u003cp\u003eHaving established that Lansoprazole effectively inhibits FASN expression and activity in BCSCs, we sought to explore the underlying mechanisms by which Lansoprazole exerts its effects on these cells. Given that the Wnt/β-catenin signaling pathway is frequently activated in BCSCs, we investigated how Lansoprazole- mediated inhibition of FASN influences this pathway. Our results demonstrated that treatment with Lansoprazole significantly reduced the nuclear translocation of β-catenin in BCSCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA-B). This was accompanied by a marked upregulation of APC, Axin-1, and GSK-3β, as well as a significant downregulation of TCF-4, TCF-7, and Wnt3a secretion (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC-D). These findings indicate that the anti-BCSC effects of Lansoprazole may be mediated through its inhibition of the Wnt/β-catenin signaling pathway via targeting FASN.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.8 Validation of Lansoprazole inhibiting BCSCs growth in vivo\u003c/h2\u003e\u003cp\u003eTo further validate the inhibitory effects of Lansoprazole on BCSCs and elucidate its underlying mechanisms in vivo, we established subcutaneous xenografts using BCSCs derived from MCF-7 (CD44+/CD24-) and MDA-MB-468 (CD44+/CD24-) cell lines in nude mice. Once the tumors reached approximately 50 mm\u0026sup3; in volume, the mice were administered 30 mg/kg Lansoprazole or an equivalent volume of DMSO as a control, every other day via oral gavage. After 21 days of treatment, the mice were sacrificed, and the tumors were excised for analysis. The results demonstrated a significant reduction in tumor growth in the Lansoprazole-treated group compared to the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). Both tumor weight and volume were markedly decreased in Lansoprazole-treated MCF-7 and MDA-MB-468 xenografts, indicating that Lansoprazole effectively suppressed the tumorigenic potential of BCSCs in vivo (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB-C). Histopathological examination using hematoxylin and eosin (H\u0026amp;E) staining revealed extensive necrosis and a less compact cellular structure in tumors from the Lansoprazole-treated group, suggesting inhibited tumor proliferation. Immunohistochemical analysis further supported these findings, showing significant downregulation of Ki-67, Wnt3a, and β-catenin expression in the tumor tissues following Lansoprazole treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD). These results suggest that the anti-tumor effects of Lansoprazole are mediated by inhibiting cell proliferation and suppressing the Wnt/β-catenin signaling pathway.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.9 Construction and validation of FASN Expression based prognostic model for Breast Cancer patients\u003c/h2\u003e\u003cp\u003eFollowing the recognition of FASN as a pivotal factor driving both stem-like properties and malignancy in breast cancer, we extended our investigation to bridge mechanistic insights with clinical relevance. By integrating single cell sequencing data and bulk RNA sequencing data, we differentiated epithelial cell subpopulations based on FASN expression levels and identified key genes for constructing robust prognostic models. During model construction, 101 combinations of machine learning algorithms were tested, and model performance was evaluated across multiple cohorts. The combination of Least Absolute Shrinkage and Selection Operator (Lasso) and Random Survival Forest (RSF) algorithms, named FASN-related signature, yielded the best performance, achieving an average C-index of 0.777 across the training and validation cohorts (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). Specifically, LASSO regression identified 21 prognostic genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB-C), which were then further refined using the RSF algorithm to optimize model accuracy. Additionally, this model demonstrated strong predictive accuracy, with the average area under the time-dependent ROC curves (AUC) across three cohorts being 0.82 at 1 year, 0.81 at 3 years, and 0.79 at 5 years (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eD-F). Survival analysis, which stratified patients into high-risk and low-risk groups based on the median risk score, revealed that overall survival rates were significantly lower in the high-risk groups across all cohorts (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eG-I). These findings indicate that the model exhibits strong risk stratification capabilities across different datasets, effectively distinguishing between high-risk and low-risk patients, and holds significant potential for clinical application. Notably, the prognostic accuracy of this FASN-based model exceeded that of the integrated clinical information nomogram, which typically performs better than individual models (Supplementary Fig.\u0026nbsp;2). This anomaly underscores the superior predictive value of the FASN-related model in our study. Moreover, the prognostic performance of the FASN-related signature was validated and demonstrated superiority over multiple established breast cancer prognosis models across various metrics and datasets, further confirming its robustness and clinical relevance (Supplementary Fig.\u0026nbsp;3). These findings suggest that the FASN-related signature is a reliable and powerful tool for predicting breast cancer prognosis, offering significant advantages in clinical applications.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe role of fatty acid synthase (FASN) in tumor metabolism has attracted a lot of attention because it plays a key role in supporting the anabolic processes required for tumor growth and survival [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Unlike normal cells, which rely primarily on foreign fatty acids, many cancer cells rely heavily on new fat production, and FASN is a key enzyme that drives this process [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Elevated FASN expression is associated with poor prognosis for multiple cancer types, reflecting its important role in promoting tumorigenic properties such as enhanced proliferation, survival, and metastasis [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Although the function of FASN in a variety of solid tumors has been extensively studied [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], research on the link between FASN and the properties of breast cancer stem cells (BCSCs) remains limited, and the specific mechanism of action is unclear. In our study, we found that the activity and expression of FASN is specifically elevated in the breast cancer stem cell subpopulation (BCSCs) for the first time. This finding is significant because BCSCs are a small subset of cells in tumors that are highly heterogeneous and have been shown to be strongly associated with tumor initiation, metastasis, drug resistance, and relapse. Thus, the high expression of FASN in these subpopulations suggests that it may not only be a key enzyme supporting metabolic needs but may also be a central factor in maintaining the stem-like properties of these cells.\u003c/p\u003e\u003cp\u003eThe Wnt/β-catenin signaling pathway plays a key role in normal tissue development and maintenance of tissue homeostasis, but its abnormal activation in tumors is closely related to the occurrence and progression of many cancers [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The core mechanism of this pathway involves the binding of the Wnt protein to the Frizzled receptor on the cell membrane to activate the downstream signaling cascade, thereby inhibiting the degradation of β-catenin [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Undegraded β-catenin is able to migrate into the nucleus, bind to transcription factors in the T cell factor/lymphoenhancer (TCF/LEF) family, and initiate the expression of a series of genes associated with cell proliferation, differentiation, and self-renewal [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. This process plays a crucial role in maintaining the self-renewal and differentiation of normal stem cells [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. However, when the Wnt/ beta-catenin signaling pathway is abnormally activated in cancer, it not only promotes unlimited proliferation of tumor cells, but also helps cancer stem cells (CSCs) maintain their dry characteristics, leading to tumor progression, recurrence, and resistance to treatment [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. In breast cancer, overactivation of the Wnt/β-catenin signaling pathway is thought to be one of the important mechanisms for maintaining the characteristics of BCSCs [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Through the activation of this pathway, BCSCs acquire self-renewal capabilities similar to normal stem cells, but also have strong proliferation potential and resistance to traditional therapies, enabling these cells to occupy a dominant position in the tumor microenvironment [\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Our study found that high FASN expression is associated with enhanced Wnt/β-catenin signaling, which is manifested by increased Wnt3a secretion and enhanced nuclear translocation of β-catenin. These findings suggest that FASN may play a crucial role in linking metabolic reprogramming with the activation of key signaling pathways that drive cancer stem cell (CSC) properties. The elevated expression of FASN in BCSCs not only supports their metabolic demands but also enhances their ability to sustain self-renewal and resist conventional therapies through the activation of the Wnt/β-catenin signaling pathway. In addition, our study identified lansoprazole as a potential therapeutic agent that effectively inhibits FASN activity. Lansoprazole was shown to suppress BCSC proliferation, reduce their self-renewal capacity, and diminish their tumorigenicity by downregulating Wnt/β-catenin signaling.\u003c/p\u003e\u003cp\u003eThe connection between FASN and Wnt/β-catenin signaling opens new avenues for therapeutic intervention. Targeting FASN, whether through pharmacological inhibitors like lansoprazole or genetic approaches, could disrupt this pro-tumorigenic signaling axis, reducing the stemness of BCSCs and sensitizing them to conventional therapies. Given the central role of FASN in lipid metabolism, its inhibition may also compromise the energy supply and biosynthetic demands of BCSCs, hindering their ability to maintain their aggressive phenotype. Our study further identified lansoprazole as a potential therapeutic agent that effectively inhibits FASN activity, suppresses BCSC proliferation, reduces their self-renewal capacity, and diminishes their tumorigenicity by downregulating Wnt/β-catenin signaling. These strategies could be particularly effective in combination with existing therapies that target the Wnt/β-catenin pathway or other signaling networks involved in BCSC-like properties. Building on these mechanistic insights, we extended our research into the clinical domain by developing a prognostic model based on FASN expression. By employing a combination of 101 machine learning algorithms, we constructed a robust model that demonstrated high prognostic accuracy across multiple cohorts. This model not only underscores the translational relevance of our findings from basic research to clinical applications but also highlights the potential of FASN as a critical biomarker for breast cancer prognosis. The integration of advanced machine learning techniques allowed us to capture the complex interplay between FASN expression and patient outcomes, offering a powerful tool for risk stratification and personalized treatment planning. The significant prognostic value of this model further emphasizes the importance of targeting FASN in therapeutic strategies, particularly in the context of BCSCs, which are known to drive recurrence and metastasis. This comprehensive approach, bridging fundamental research with clinical implementation, underscores the potential impact of our findings on improving breast cancer treatment and patient outcomes.\u003c/p\u003e\u003cp\u003eDespite the significant findings of our study, several limitations should be acknowledged. First, although our research demonstrated the crucial role of FASN in BCSCs and identified lansoprazole as a potential therapeutic agent, the in vitro and in vivo models used in this study may not fully recapitulate the complexity of human breast cancer. The tumor microenvironment, immune system interactions, and patient-specific genetic variations were not fully accounted for in our models, which may affect the translatability of our findings to clinical settings. Additionally, while the prognostic model we developed based on FASN expression showed high accuracy across multiple cohorts, further validation in larger, independent clinical datasets is necessary to confirm its generalizability and clinical utility. Finally, the exact molecular mechanisms by which FASN interacts with the Wnt/β-catenin signaling pathway in BCSCs remain to be fully elucidated. Future studies should focus on exploring these mechanisms in greater detail and evaluating the efficacy of FASN-targeted therapies in combination with other treatment modalities in more clinically relevant models.\u003c/p\u003e\u003cp\u003eIn conclusion, this study provides novel insights into the critical role of FASN in breast cancer stem cell biology and its potential as a therapeutic target. We demonstrated for the first time that FASN not only exhibits elevated expression and activity in BCSCs but also plays a pivotal role in sustaining their stem-like properties through the activation of the Wnt/β-catenin signaling pathway. Moreover, we identified lansoprazole as an effective inhibitor of FASN, capable of reducing BCSC proliferation, self-renewal, and tumorigenicity by downregulating this key signaling axis. These findings bridge the gap between fundamental research and clinical application, as we further developed and validated a robust prognostic model based on FASN expression using advanced machine learning techniques. This model highlights the prognostic value of FASN in breast cancer and offers a practical tool for personalized treatment strategies. Collectively, our research not only advances the understanding of the role of FASN in breast cancer stem cells but also paves the way for new therapeutic approaches targeting BCSCs, with the potential to significantly improve patient outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study was supported by the National Natural Science Foundation of China (grant number 82473965 to TLZ), the Natural Science Foundation of Hunan Province (grant number 2023JJ50156 to TLZ, 2025JJ90146 to HHH), the Clinical Research 4310 Program of the First Affiliated Hospital of the University of South China (grant number 20224310NHYCG04 to TLZ), Science and technology innovation Program of Hengyang City (grant number 202250045223 to TLZ), the project of Hunan health high-level youth backbone talents (grant number 20240304125 to Z.T.L) and the Scientific Research Project of the Hunan Provincial Department of Education (grant number 24B0413 to HHH).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors state that they do not have any competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study complied with the regulations of the Animal Ethics Committee of Hunan Evidence Based Biotechnology Co., Ltd. And was approved (No.ABXZ20230321). All experimental protocols were conducted in accordance with relevant guidelines and regulations, including those outlined by the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll raw data used in this study are publicly available from the Gene Expression Omnibus (GEO) under the accession numbers GSE20685 and GSE161529 (https://www.ncbi.nlm.nih.gov/geo/). Additional datasets and materials can be accessed through the UCSC Xena browser (https://xenabrowser.net/). All code and data may be obtained from the corresponding author for reasonable reasons.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTLZ, HHH and MXZ conceived and designed the study. JDZ, HHH, CX drafted the manuscript and conducted data analysis. TLZ, ZL, MXZ, BH and HXZ strictly revised the manuscript. JDZ, WDZ, TY, SYW and HXW contributed to writing figure legends. All authors read and approved of the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel RL, Miller KD, Wagle NS, Jemal A: \u003cstrong\u003eCancer statistics, 2023\u003c/strong\u003e. \u003cem\u003eCA Cancer J Clin \u003c/em\u003e2023, \u003cstrong\u003e73\u003c/strong\u003e(1):17-48.\u003c/li\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F: \u003cstrong\u003eGlobal Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries\u003c/strong\u003e. \u003cem\u003eCA Cancer J Clin \u003c/em\u003e2021, \u003cstrong\u003e71\u003c/strong\u003e(3):209-249.\u003c/li\u003e\n\u003cli\u003eMcDonald ES, Clark AS, Tchou J, Zhang P, Freedman GM: \u003cstrong\u003eClinical Diagnosis and Management of Breast 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\u003cstrong\u003e179\u003c/strong\u003e:106222.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-systems-biology-and-applications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjsba","sideBox":"Learn more about [npj Systems Biology and Applications](http://www.nature.com/npjsba/)","snPcode":"41540","submissionUrl":"https://submission.springernature.com/new-submission/41540/3","title":"npj Systems Biology and Applications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Fatty Acid Synthase, breast cancer stem cells, Wnt/β-catenin, lansoprazole, Machine Learning","lastPublishedDoi":"10.21203/rs.3.rs-7324806/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7324806/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBreast 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.\u003c/p\u003e","manuscriptTitle":"Integrating Single-cell and Machine Learning with Experimental Validation Reveals FASN Conferring Breast Cancer Stem cell-like Properties: Therapeutic Insights and Prognostic Implications","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-22 21:53:33","doi":"10.21203/rs.3.rs-7324806/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-09T03:30:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-08T21:20:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-07T22:11:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-06T00:13:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"199851835108564713889015028892567064902","date":"2025-11-24T12:30:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"6479180524095409984222688944333107201","date":"2025-11-23T23:25:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"59976639632332673366662873810770856651","date":"2025-11-21T13:43:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"18648256776023166730597054545826750394","date":"2025-11-04T03:22:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-03T08:52:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"213662598798614150218793533407494549049","date":"2025-08-21T12:22:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"262768895165266441950382524874324958197","date":"2025-08-18T22:35:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"334741643798378916964463004262563501599","date":"2025-08-16T16:22:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-15T14:57:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-11T12:21:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-11T09:37:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Systems Biology and Applications","date":"2025-08-08T07:42:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-systems-biology-and-applications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjsba","sideBox":"Learn more about [npj Systems Biology and Applications](http://www.nature.com/npjsba/)","snPcode":"41540","submissionUrl":"https://submission.springernature.com/new-submission/41540/3","title":"npj Systems Biology and Applications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4bcc19d3-4dc7-4a36-8613-166fbaf0edb7","owner":[],"postedDate":"August 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":53510668,"name":"Biological sciences/Cancer"},{"id":53510669,"name":"Biological sciences/Cell biology"},{"id":53510670,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":53510671,"name":"Biological sciences/Drug discovery"}],"tags":[],"updatedAt":"2025-12-09T03:38:41+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-22 21:53:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7324806","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7324806","identity":"rs-7324806","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}