Application of FASN target spot

Abstract

The invention relates to the field of biological medicines, in particular to application of fatty acid synthase (FASN) as a target spot in screening or preparing medicines for treating breast cancer, and particularly relates to targeted therapy for breast cancer stem cells (BCSCs). The invention discloses application of an FASN target spot in screening or preparing a medicine for treating breast cancer. Research finds that FASN is highly expressed in BCSCs, and stem cell characteristics of the BCSCs are maintained by activating a Wnt / beta-catenin pathway. By inhibiting the activity of FASN (such as using lansoprazole), the proliferation, self-renewal and tumor formation ability of BCSCs can be significantly reduced. The invention provides an FASN-based drug screening method and a drug composition, and provides a new strategy for breast cancer treatment.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of biological medicine, in particular to an application of a FASN target in screening or preparing a drug for treating breast cancer, especially a targeted therapy for breast cancer stem cells (BCSCs). BACKGROUND

[0002] Breast cancer is one of the most common malignant tumors in women worldwide, and its recurrence, metastasis and treatment resistance are closely related to breast cancer stem cells (BCSCs). BCSCs have characteristics such as self-renewal, multilineage differentiation and high drug resistance, and traditional therapy is difficult to eradicate. Our previous studies have found that BCSCs can induce CD8 + T cell exhaustion forms an inhibitory immune microenvironment, leading to poor clinical treatment prognosis in breast cancer patients, but the specific mechanism is still unknown. In addition, BCSCs express a variety of transport proteins (such as multidrug resistance protein 1 (MDR1), MRP1 and BCRP1) and proteins related to DNA repair (such as Ung, Uhrf1 and Xrcc5), which make BCSCs resistant to chemotherapy and radiotherapy, reducing the effect of conventional therapy on the subpopulation of BCSCs. Based on the important role of breast cancer stem cells in the process of breast cancer recurrence, metastasis and treatment resistance, breast cancer stem cells are an important reason for breast cancer recurrence and metastasis. Therefore, targeting breast cancer stem cells (BCSCs) therapy is expected to break through the current bottleneck of clinical breast cancer treatment. Fatty acid synthase (FASN) is a key enzyme in lipid metabolism and is highly expressed in breast cancer cells, but its role and mechanism in BCSCs are not yet clear. SUMMARY

[0003] In view of the problems in the prior art, the present application aims to provide an application of a FASN target, specifically:

[0004] The first aspect is the application of the FASN target in screening or preparing a drug for acting on breast cancer stem cells.

[0005] The FASN enhances the stem cell-like properties of breast cancer stem cells by activating the Wnt / β-catenin signaling pathway.

[0006] The FASN target is inhibited to act on breast cancer stem cells to produce a therapeutic effect on breast cancer.

[0007] The second aspect is the application of the FASN target in constructing a prognosis model for breast cancer patients, and the construction method of the prognosis model is:

[0008] (1) Collect breast cancer patient data from TCGA, divide into training set and test set, and select GSE20685 cohort as validation set; (2) use StepCox and GMB combination to construct FASN-Score prognostic model, and screen prognostic risk genes; (3) use FASN-Score prognostic model to calculate the risk score of each patient in each cohort, and use the median risk value as the cutoff point to divide the patients into high-risk group and low-risk group, and observe the survival difference between the high-risk group and the low-risk group by using the Kaplan-Meier survival curve of the TCGA training set, test set and GSE20685 validation set for verification; (4) verify the independent predictive value of the FASN-Score prognostic model by single factor Cox regression analysis and multi-factor Cox regression analysis.

[0009] The FASN-Score prognostic model is composed of 29 prognostic risk genes, and the 29 genes are: PDP1, AGPAT1, MECP2, ELOVL2, ETFA, SLC6A3, FABP7, SERPINA1, ALOX15, VDAC1, ABCA1, CASP7, PDSS2, IVL, PAPSS2, SDHA, KRT14, ACBD5, CD79A, LTF, PEX3, IGF2R, IL10, BCL2, TBXA2R, BRCA1, SLC25A13, NUS1 and ITGB3.

[0010] Beneficial effects: (1) The present application first discloses that FASN is highly expressed in BCSCs and maintains the characteristics of BCSCs by activating the Wnt / β-catenin signaling pathway, which provides a new target for targeting BCSCs, and it is explained that FASN can be used as a target for screening or preparing drugs for treating breast cancer, especially for BCSCs related treatment. (2) It is verified that the FASN inhibitor lansoprazole activates the Wnt / β-catenin signaling pathway by acting on the FASN target to weaken the characteristics of BCSCs, which provides a basis for the clinical transformation of lansoprazole as an "old drug with new use", and at the same time, it is proved again that FASN is a new target for targeting BCSCs. (3) The breast cancer patient prognostic model based on FASN constructed by the present application has a higher accuracy than the nomogram integrated with clinical information, which highlights the superior predictive performance of the FASN-related model of the present application, and shows that it is superior to multiple established breast cancer prognostic models, further confirming the robustness and clinical relevance of the FASN-related model of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0011] Figure 1Single-cell transcriptome atlas of breast cancer: (A) tSNE-based dimensionality reduction clustering distribution map; (B) top 10 marker genes of each cell subpopulation; (C) marker genes of each specific cell type; (D) tSNE dimensionality reduction curve of specific cell types; (E) expression of FASN in different cell types; (F) density distribution of FASN and EPCAM.

[0012] Figure 2 FASN expression and related pathway analysis in breast cancer epithelial cells: (A) pathway enrichment analysis of different cell types, such as FASN high expression and FASN low expression epithelial cells; (B) InferCNV analysis reveals the presence of copy number variations in FASN high expression epithelial cells; (C-D) CytoTRACE analysis shows that FASN high expression epithelial cells are located in areas with lower differentiation degree; (E-F) pseudo-time analysis shows the developmental trajectory of epithelial cells.

[0013] Figure 3 Effect of FASN on breast cancer cell characteristics: (A-D) Overexpression of FASN in MCF-7 cells significantly increased colony formation and spheroid formation capacity, including spheroid size and number (right), on the contrary, knockdown of FASN in M3k cells reduced colony formation, spheroid size and spheroid number (left); (E-F) Western blot analysis showed that the down-regulation of stemness-related markers (C-myc, Oct-4A, Nanog, Sox-2) in M3k / ShFASN cells compared with M3k / Scr, while overexpression of FASN in MCF-7 cells up-regulated these markers.

[0014] Figure 4 Effect of FASN on Wnt / β-catenin signaling pathway: (A) After knocking down FASN in M3k cells, Wnt3a secretion was significantly reduced, while overexpression of FASN in MCF-7 cells observed Wnt3a over-secretion; (B) Western blot analysis showed that after knocking down FASN in M3k cells, the expression of key proteins in the β-catenin signaling pathway changed; (C) Image J quantification showed the difference in expression of key proteins in the β-catenin pathway between M3k / Scr and M3k / ShFASN cells; (D) Western blot analysis showed that after overexpression of FASN in MCF-7 cells, the expression of key proteins in the β-catenin protein pathway changed; (E) Image J quantification showed the difference in expression of key proteins in the β-catenin pathway between MCF-7 / Vec and MCF-7 / FASN.

[0015] Figure 5For the expression level and activity of FASN in BCSCs and the activation of Wnt / β-catenin signaling pathway: (A) the difference of FASN activity in BCSCs and mother cells; (B) the difference of Wnt3a content in the supernatant culture medium of CD44+CD24- BCSCs and mother 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+CCD24-)) compared with mother cells MCF-7 and MDA-MB-468.

[0016] Figure 6 For the results of the effects of proton pump inhibitor lansoprazole on FASN expression, activity and characteristics in BCSCs: (A) the molecular docking diagram of lansoprazole and FASN; (B) lansoprazole significantly inhibited the expression of FASN in BCSCs; (C) lansoprazole significantly inhibited the activity of FASN in BCSCs; (D) lansoprazole significantly inhibited the proliferation of BCSCs; (E) lansoprazole significantly inhibited the colony formation ability of BCSCs; (F) lansoprazole significantly inhibited the sphere formation ability of BCSCs.

[0017] Figure 7 For the results of the effects of lansoprazole on Wnt / β-catenin signaling pathway in BCSCs: (A-B) lansoprazole significantly inhibited the nuclear translocation of β-catenin in BCSCs; (C) lansoprazole significantly regulated the key signaling proteins in Wnt / β-catenin signaling pathway in BCSCs; (D) lansoprazole significantly inhibited the secretion of Wnt3a in BCSCs.

[0018] Note: Figures 3-7 The data are expressed as mean ± standard deviation, n = 3, *P < 0.05, **P < 0.01, ***P < 0.001.

[0019] Figure 8 For the results of the effects of proton pump inhibitor lansoprazole on BCSCs tumor formation in animal models: (A) an example of lansoprazole inhibiting BCSCs tumor formation in animal models; (B) lansoprazole inhibiting the weight of BCSCs tumor formation; (C) lansoprazole inhibiting the size of BCSCs tumor; (D) lansoprazole inhibiting the expression of Ki-67, Wnt3a and β-catenin in tumorigenic BCSCs (the data are expressed as mean ± standard deviation, n = 6, **P < 0.01, ***P < 0.001).

[0020] Figure 9For the construction of FASN-Score based on machine learning: (A) C-index of 101 prediction models established using 9 machine learning algorithms; (B) 29 genes were identified to construct the prognostic model; (C) survival analysis plot of two risk group patients in TCGA test cohort, TCGA training cohort and GSE20685 cohort; (D) 1-year, 3-year and 5-year AUC histogram of TCGA test cohort, TCGA training cohort and GSE20685 cohort.

[0021] Figure 10 For the development and validation of nomogram: (A) forest plot of univariate Cox regression analysis; (B) forest plot of multivariate Cox regression analysis; (C) predictive nomogram combining clinical features and risk score; (D) calibration curve of nomogram; (E-G) 1, 3 and 5-year nomogram, FASN-Score and clinical factors receiver operating characteristic curve.

[0022] Figure 11 For the analysis of tumor microenvironment and immune landscape of immune infiltration: (A) GSEA enrichment analysis of high-risk group and low-risk group; (B) heat map showing immune infiltration of high and low risk groups; (C) comparison of ESTIMATE score, immune score, stromal score and tumor purity of different risk groups; (D) box plot showing the difference in abundance of 22 immune infiltration cells between high-risk group and low-risk group (*P<0.05; **P<0.01; ***P<0.001); (E) correlation analysis of FASN-Score and immune infiltration cells.

[0023] Figure 12 For the correlation analysis of immune checkpoints and TMB in two risk groups: (A) expression of 13 immune checkpoint molecules (*P<0.05; **P<0.01; ***P<0.001); (B) TMB of low-risk group; (C) TMB of high-risk group. DETAILED DESCRIPTION

[0024] I. Research Methods

[0025] 1. Cell culture and drug treatment: Human breast cancer cell lines MCF-7 and MDA-MB-468 used in this study were obtained from American Type Culture Collection (ATCC), and M3k was a 3000-fold doxorubicin-resistant cell line induced from MCF-7 parental cells. MCF-7 and MDA-MB-468 cells were cultured in DMEM medium supplemented with 10% fetal bovine serum (FBS) at 37 °C in a 5% CO2 environment. M3k cells were cultured in RPMI 1640 medium supplemented with 10% FBS under the same conditions. All cell lines were not passaged more than 8 generations after thawing. Lansoprazole (batch number PHR1390) used in this study was purchased from Supelco.

[0026] 2. Data source and processing: Single-cell sequencing data were obtained from 6 breast cancer patients in GSE161529 dataset. 1075 breast cancer patients with complete survival information were obtained from TCGA database, and after excluding normal samples and samples from the same patient, breast cancer patients were randomly divided into training cohort (N=755) and internal validation cohort (N=320) using the create Data Partition function in the caret package, with a ratio of 7:3. The GSE20685 dataset of 327 breast cancer patients was used as an external cohort to verify the accuracy and robustness of the prognosis model. All datasets were standardized using the normalized function in the limma package before use to reduce differences caused by data heterogeneity.

[0027] 3. Single-cell RNA analysis: Quality control was performed using Seurat, and cells with less than 200 gene detections and mitochondrial content exceeding 15% were filtered. Dimensionality reduction was performed using PCA, and clusters were visualized using UMAP. Cell types were annotated by SingleR and manual inspection based on known markers. The FindMarkers function was used to evaluate the differential gene expression between FASN-high and FASN-low epithelial cells, and enrichment analysis was performed by GSEA to reveal key proliferation pathways. CytoTRACE and Monocle2 were used for pseudo-time and differentiation state analysis, respectively, and inferCNV was used to identify chromosomal abnormalities in FASN-high epithelial cells.

[0028] 4. Development and validation of FASN-Score

[0029] TCGA cohort was randomly divided into training set and internal test set with a ratio of 7:3 using the createDataPartition function in the caret package, and the GSE20685 cohort was included as an external validation set. Using the above DEGs and 9 machine learning algorithms, a prediction model was constructed, forming 101 machine learning algorithm combinations, including 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). Kaplan-Meier survival curves were generated using the survminer package. ROC curves were generated using the timeROC package to evaluate the predictive accuracy of the prognostic model at 1 year, 3 years, and 5 years, and bar charts generated using the ggplot2 package for visualization.

[0030] 5. Construction of prognostic risk assessment nomogram combined with clinical variables

[0031] To individualize the survival rate of breast cancer patients, we developed a nomogram containing risk scores and clinical variables such as age and tumor stage. Initially, univariate and multivariate Cox regression analyses were performed to assess whether risk scores and clinical variables could be used as independent prognostic factors for breast cancer. Next, the rms package in R software was used to construct a nomogram and calibration curve, including the risk score, age, stage, and T, N, and M stages of the patient. Among them, age was divided into two groups with 65 years as the boundary, and gender was divided into male and female groups. TNM was grouped according to clinical staging, and M staging was divided into M0 and M1 groups, and T and N staging was divided into T0-T4 and N0-N4 groups, respectively. ROC curves were plotted using the timeROC and ggDCA packages in R software to evaluate the performance of the nomogram. Therefore, this has important value in clinical practice.

[0032] 6. Generation of FASN engineered cell lines: To construct FASN gene overexpression and gene knockout cell lines, we used genetic engineering techniques.

[0033] FASN gene overexpression: Human FASN cDNA was cloned into the pcDNA3 vector, and MCF-7 breast cancer cells were transfected with either FASN-pcDNA3 or an empty pcDNA3 vector using Lipofectamine 3000 (Thermo Fisher Scientific, Inc.). Forty-eight hours later, 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.

[0034] FASN gene knockout: A shRNA sequence targeting FASN mRNA (AACCCTGAGATCC CAGCGCTG) was designed and cloned into a suitable plasmid. The FASN-targeting shRNA plasmid or a scrambled shRNA control plasmid was transfected into M3k cells that highly express FASN using Lipofectamine 3000. Twenty-four hours later, transfected cells were selected with 800 μg / mL G418 for two weeks to establish a stable cell line. Clones that effectively knocked out FASN were identified and amplified for subsequent use.

[0035] The above methods were used to generate the FASN-overexpressing MCF-7 cell line and its control group, as well as the FASN gene knockout M3k cell line and its control group.

[0036] 7. Colony formation assay: Colony formation assays were performed using M3k and MCF-7 cell lines with specific genetic modifications.

[0037] The M3k cell line includes the M3k / Scr (scrambled shRNA control) and M3k / ShFASN (FASN knockout) groups; the MCF-7 cell line includes the MCF-7 / Vec (vector control) and MCF-7 / FASN (FASN overexpression) groups.

[0038] Three hundred viable cells from each group were seeded into 6-well Corning plates and incubated at 37°C and 5% CO2 for 24 hours, repeated three times. After the incubation period, the cells were cultured in complete medium for 10 days. Colonies were then fixed for 5 minutes with a 1:7 solution of acetic acid and methanol. After fixation, the cells were stained with 0.5% crystal violet at room temperature for 20 minutes, followed by washing three times with PBS to remove excess dye. Colonies with more than 50 cells were counted using an inverted phase-contrast microscope (Nikon Corporation). Representative images of the stained colonies were captured for visualization and recording using a Canon scanner (CanoScan 5600F).

[0039] 8. Spheroid formation assay: to evaluate the spheroid formation ability of M3k and MCF-7 cell lines with specific genetic modifications.

[0040] The M3k cell line includes the M3k / Scr (scrambled shRNA control) and M3k / ShFASN (FASN knockout) groups; the MCF-7 cell line includes the MCF-7 / Vec (vector control) and MCF-7 / FASN (FASN overexpression) groups.

[0041] To assess spheroid formation capacity, we cultured 5000 cells per well in 6-well ultra-low adhesion plates (Corning, Inc.). Cells were cultured at 37°C and 5% CO2 using cancer stem cell (CSC) enrichment medium. This medium consisted 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 culture, the formed tumor spheroids were analyzed. Tumor spheroids with a diameter of at least 50 μm were imaged and counted using an inverted phase-contrast microscope (Nikon Corporation) at 100x magnification.

[0042] 9. 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) with the addition of a phosphatase inhibitor (1 mM Na3VO4) and a protease inhibitor (1 mM benzyl sulfonyl fluoride). Cell lysates were centrifuged at 12,000 x g for 10 min at 4 °C to remove debris. Protein concentration was measured using the Pierce Rapid Gold Biuret Acetate (BCA) kit (ThermoFisher Scientific, Inc.). 20–50 μg of protein per lane was separated onto 6% or 12% SDS-PAGE gels and transferred to Sequi-Blot PVDF membranes (Bio-Rad Laboratories, Inc.). The membrane was blocked for 2 hours at room temperature in 5% skim milk in TBST (Tris-buffered saline containing 0.1% Tween-20). Primary antibodies were incubated with the membrane overnight at 4°C. Specific antibodies used included FASN (Affinity, DF6106), APC (Proteintech, 19782-1-AP), Axin-1 (Bioss, bs-21732R), GSK-3β (Abcam, ab93926), TCF-4 (Proteintech, 13838-1-AP), and TCF-7 (Proteintech, 14464-1-AP). β-catenin and GAPDH were used as internal controls. After incubation with primary antibody, the membrane was washed three times with TBST and then incubated for 2 hours at room temperature with appropriate secondary antibody (anti-mouse, product number A2429, or anti-rabbit, product number A3937, from Sigma-Aldrich, Merck KGaA). Protein bands were visualized using enhanced chemiluminescence (Thermo Fisher Scientific, Inc.) and detected using an X-ray film system (Ece Scientific Co., Inc.). Band intensity was quantified using ImageJ software (version 1.52; National Institutes of Health).

[0043] 10. Enzyme-linked immunosorbent assay (ELISA): Coat microplates with purified anti-WNT3a antibody and incubate overnight at 4°C. The next day, wash the wells with PBS containing 0.05% Tween-20 to remove unbound antibody. Then, add 100 μL of standard or sample to each well and incubate at 37°C for 2 hours. After incubation, discard the liquid and pat the wells dry on absorbent paper. Then, add 100 μL of biotinylated anti-WNT3a antibody to each well and incubate at 37°C for 1 hour. After washing with PBS-T, add 100 μL of horseradish peroxidase (HRP)-labeled streptavidin to each well and incubate at 37°C for 1 hour. Then wash the wells five times with PBS-T. For color development, add 90 μL of TMB (3,3',5,5'-tetramethylbenzidine) substrate solution to each well and incubate 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, changing the color from blue to yellow. The optical density (OD) of each well was measured at a wavelength of 450 nm using a microplate reader. The OD value was proportional to the concentration of WNT3a in the sample, and a standard curve was generated to determine the concentration of WNT3a in the test sample.

[0044] 11. Statistical Analysis: Data analysis was performed using R software (version 4.3.1). Unless otherwise specified, all data are expressed as Mean ± SD (n = 3). A p-value < 0.05 was considered statistically significant.

[0045] II. Results

[0046] 1. Identification and Characterization of FASN in Breast Cancer Cell Subtypes

[0047] In our previous research, our team has focused on the important biological role of FASN in breast cancer and has identified it as an important potential therapeutic target. However, there are currently no published studies on FASN at the single-cell level. Therefore, in this study, we used single-cell sequencing technology to explore the role of FASN in breast cancer. In the initial analysis, dimensionality reduction clustering analysis was performed on single-cell sequencing data from six breast cancer patients in the GSE161529 dataset, identifying 16 cell cluster subpopulations (…). Figure 1 A). After further identifying the top 10 marker genes for each cell subpopulation, the cell subpopulations were annotated using a combination of SingleR and manual annotation. Figure 1 BC). Cell populations were annotated based on characteristic biomarkers expressed by different cell populations, identifying eight cell types: epithelial cells, endothelial cells, fibroblasts, T cells, B cells, monocytes, macrophages, and tissue stem cells. Figure 1 D). Analysis showed that FASN was expressed in most cell subsets, especially in epithelial cells of tumor tissue.Figure 1 E). Furthermore, there is evidence of co-expression between FASN and the classic tumor marker EPCAM (E). Figure 1 F).

[0048] 3.2 Stem cell characteristics and malignancy of FASN-driven epithelial cells

[0049] To explore the biological significance of FASN expression at the single-cell level, we focused on epithelial cells with high and low FASN expression, divided by median expression values. Pathway enrichment analysis showed that epithelial cells with high FASN expression were significantly enriched in key pathways such as proliferation, metabolism, and stress response, including MYC targets, oxidative phosphorylation, and unfolded protein responses. Figure 2 A). These pathways are crucial for the rapid growth and survival of tumor cells, highlighting the role of FASN in supporting the metabolic and proliferative needs of these cells.

[0050] InferCNV analysis further elucidated the genomic alterations associated with FASN expression. Significant copy number variations (CNVs) were observed in FASN-highly expressed epithelial cells, particularly in genomic regions associated with tumor progression. Notably, an amplification was detected on chromosome 8, which may enhance fatty acid synthesis, thereby driving tumor cell proliferation. Furthermore, a deletion was identified on chromosome 17, potentially involving the loss of a key tumor suppressor gene, further exacerbating tumor invasiveness. Figure 2 B). These genomic alterations highlight the crucial role of FASN overexpression in promoting tumor development.

[0051] CytoTRACE analysis showed that FASN-highly expressed epithelial cells were primarily located in regions associated with undifferentiated state, indicated by high predictive order values. This finding suggests that these cells may possess stem cell-like properties or proliferative capacity, which are often associated with aggressive tumor behavior and poor prognosis. Figure 2 CD).

[0052] Pseudo-time analysis was used to study the trajectory of FASN expression during epithelial cell development, spanning five distinct cell states. The analysis revealed that, in the early stages of the developmental trajectory, cells with the highest FASN and EPCAM expression were located in state 2. In contrast, cells with lower FASN expression were located in states 4 and 5, indicating progression from a highly proliferative state to a more differentiated state. Figure 2 These findings indicate that FASN expression peaks in the early stages of epithelial cell development and decreases with cell differentiation, further confirming that epithelial cells with high FASN expression possess superior stem cell characteristics and reinforcing their crucial role in the early proliferative phase of tumor progression. 3.3 Identification of Stem Cell-like Characteristics Conferred by FASN in Breast Cancer

[0053] To assess the biological functions associated with FASN expression, we generated FASN knockout M3k / ShFASN cell lines and FASN overexpressing MCF-7 / FASN cell lines. The results showed that, compared with control M3k / Scr cells, the growth capacity of M3k / ShFASN cells was significantly reduced (…). Figure 3 A). Conversely, compared with the control group MCF-7 / Vec cells, the growth capacity of MCF-7 / FASN cells was significantly improved ( Figure 3 B).

[0054] Furthermore, our results showed that the clonogenic capacity of M3k / ShFASN cells was significantly reduced compared to control M3k / Scr cells. In contrast, the clonogenic potential of MCF-7 / FASN cells was significantly increased compared to the vector control MCF-7 / Vec cells. Figure 3 C).

[0055] Further spheroid formation experiments showed that M3k / ShFASN cells formed significantly fewer and smaller spheroids compared to M3k / Scr cells, indicating a loss of cell self-renewal capacity. Conversely, compared to MCF-7 / Vec cells, MCF-7 / FASN cells exhibited enhanced spheroid formation capacity, producing larger and more numerous spheroids. Figure 3 D). Furthermore, we observed that the expression levels of stem cell-related markers (including C-myc, Oct-4A, Nanog, and Sox-2) were significantly reduced in M3k / Scr cells compared to M3k / Scr cells. Conversely, these markers were significantly upregulated in MCF-7 / FASN cells compared to MCF-7 / Vec cells. Figure 3 These data strongly demonstrate that FASN plays a crucial role in maintaining the stem cell characteristics of breast cancer cells.

[0056] 3.4 FASN-mediated activation of the Wnt / β-Catenin signaling pathway enhances the stem cell properties of breast cancer cells

[0057] ELISA experiments showed that breast cancer cells with high FASN expression had significantly increased Wnt3a secretion. Figure 4 A). In FASN knockout cells, the expression of components of the β-catenin disruption complex, including APC, AXIN-1, and GSK3β, was upregulated, while the levels of β-catenin, its downstream co-transcription factor TCF-3, and the regulatory protein TCF-7 were significantly decreased. Conversely, in cells with increased FASN expression, APC, AXIN-1, and GSK3β were significantly downregulated, while the levels of β-catenin, TCF-4, and TCF-7 were significantly increased.Figure 4 BE).

[0058] These findings suggest that FASN may enhance the stem cell-like properties of breast cancer cells by activating the Wnt / β-catenin signaling pathway. 3.5 Validation of the Activated FASN-Wnt / β-catenin Axis in Breast Cancer Stem Cells

[0059] Having observed that FASN may influence the Wnt / β-catenin signaling pathway and support breast cancer stem cell-like characteristics, we further isolated and purified breast cancer stem cells (BCSCs) using CD44+ / CD24- labeling to precisely assess the role of FASN in these stem cell populations. Our analysis showed that FASN activity and expression were significantly increased in the CD44+ / CD24- subsets of the MCF-7 and MDA-MB-468 breast cancer cell lines compared to their respective unselected blast cells (p<0.01). Figure 5 A). Furthermore, the Wnt3a protein level was significantly increased in these subgroups ( Figure 5 B). Western blot analysis showed that the Wnt / β-catenin signaling pathway was significantly activated in these subgroups, as evidenced by significant upregulation of β-catenin, TCF-4, and TCF-7, and significant downregulation of negative regulators APC, Axin-1, and GSK-3β. Figure 5 C). These findings strongly suggest that increased FASN expression in breast cancer stem cells enhances the activation of the Wnt / β-catenin signaling pathway, which in turn plays a crucial role in maintaining the stem-like properties of these cells.

[0060] 3.6 Lansoprazole kills BCSCs by inhibiting FASN

[0061] Given the crucial role of FASN in maintaining the stem cell properties of breast cancer stem cells (BCSCs) and its involvement in activating the Wnt / β-catenin signaling pathway, we explored potential therapeutic agents targeting FASN.

[0062] We used computer-aided drug design (CADD) and high-throughput screening methods to identify potential FASN inhibitors, and ultimately selected lansoprazole as a promising candidate. Figure 6A). To evaluate the effect 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 the expression of FASN protein in BCSCs and greatly reduced FASN enzyme activity, indicating that it effectively inhibited FASN function. Figure 6 Subsequent functional experiments showed that lansoprazole treatment significantly reduced the proliferation capacity of BCSCs. Figure 6 D). Colony formation assays further demonstrated that lansoprazole significantly inhibited the colony-forming ability of these cells. Figure 6 E). Furthermore, lansoprazole significantly impaired the spheroid-forming ability of BCSCs, indicating that lansoprazole has a significant inhibitory effect on the self-renewal properties of BCSCs. Figure 6 F). These findings indicate that lansoprazole effectively inhibits the expression and activity of FASN in BCSCs, thereby significantly reducing their proliferation and self-renewal capacity. This highlights lansoprazole as a potential therapeutic agent targeting FASN, inhibiting the dry-like properties of BCSCs.

[0063] 3.7 Mechanism by which lansoprazole inhibits FASN-mediated BCSC death

[0064] The results showed that lansoprazole significantly reduced the nuclear translocation of β-catenin in BCSCs. Figure 7 This was accompanied by significant upregulation of APC, Axin-1, and GSK-3β, and significant downregulation of TCF-4, TCF-7, and Wnt3a secretion. Figure 7 These findings suggest that the anticancer effect of lansoprazole may be mediated by targeting FASN to inhibit the Wnt / β-catenin signaling pathway.

[0065] 3.8 Verification of lansoprazole's inhibition of BCSCs growth in vivo

[0066] To further verify the inhibitory effect of lansoprazole on BCSCs and elucidate its potential mechanism in vivo, we established subcutaneous xenograft tumors in nude mice using BCSCs from MCF-7 (CD44+ / CD24-) and MDA-MB-468 (CD44+ / CD24-) cell lines. When the tumor volume reached approximately 50 mm... 3 Mice were administered lansoprazole (treatment group) or an equal volume of DMSO (control group) orally every other day. After 21 days of treatment, the mice were sacrificed, and the tumors were removed for analysis. The results showed that tumor growth was significantly reduced in the lansoprazole-treated group compared to the control group.Figure 8 A). Lansoprazole treatment significantly reduced tumor weight and volume in both MCF-7 and MDA-MB-468 xenografts, indicating that lansoprazole effectively inhibited the tumorigenic potential of BCSCs in vivo. Figure 8 BC). Histopathological examination using hematoxylin and eosin (H&E) staining showed extensive necrosis and loose cellular structure in the lansoprazole-treated tumors, indicating suppressed tumor proliferation. Immunohistochemical analysis further supported these findings, showing a significant downregulation of Ki-67, Wnt3a, and β-catenin expression in tumor tissues after lansoprazole treatment. Figure 8 D). These results indicate that the antitumor effect of lansoprazole is mediated by inhibiting cell proliferation and suppressing the Wnt / β-catenin signaling pathway.

[0067] 3.9 Construction and validation of a FASN-based prognostic model for breast cancer patients

[0068] Having recognized FASN as a key driver of breast cancer stem cell characteristics and malignancy, we expanded our research to link mechanistic insights with clinical relevance. We selected nine machine learning methods, including LASSO, GBM, RSF, plsRcox, StepCox, SuperPC, Survival-SVM, CoxBoost, and Enet. We combined these machine learning methods to obtain 101 combinations ( Figure 9 A). TCGA was divided into training and testing sets, with the GSE20685 queue selected as the validation set. Results showed that the model built using a combination of StepCox and GMB was selected as the best model (FASN-Score) because it had the highest average C-index of 0.776. This model consists of 29 genes ( Figure 9 B), the 29 genes are: PDP1, AGPAT1, MECP2, ELOVL2, ETFA, SLC6A3, FABP7, SERPINA1, ALOX15, VDAC1, ABCA1, CASP7, PDSS2, IVL, PAPSS2, SDHA, KRT14, ACBD5, CD79A, LTF, PEX3, IGF2R, IL10, BCL2, TBXA2R, BRCA1, SLC25A13, NUS1 and ITGB3.

[0069] Using this model, we calculated the risk score for each patient in multiple cohorts and used the median risk value as a cutoff point to divide patients into high-risk and low-risk groups. Significant survival differences between the high-risk and low-risk groups were observed based on Kaplan-Meier survival curves from the TCGA training set, test set, and GSE20685 validation set (all p-values ​​< 0.01).Figure 9 C). tROC analysis shows that in the TCGA test set, the AUCs for 1 year, 3 years, and 5 years are 0.735, 0.710, and 0.764, respectively; in the TCGA training set, they are 0.947, 0.978, and 0.962, respectively; and in the GSE20685 validation queue, they are 0.701, 0.711, and 0.678, respectively. Figure 9 D). These results indicate that the model has good predictive performance, and that elevated FASN risk scores are associated with malignant phenotypes in the occurrence and progression of BC tumors, demonstrating great potential for clinical application.

[0070] 3.10. Creation and Verification of Nodal Charts

[0071] To further explore the relationship between clinical parameters and risk characteristics, we performed univariate and multivariate Cox regression analyses to validate the independent predictive value of the FASN-Score. Univariate Cox regression analysis showed that age, stage, TNM stage, and risk score were all associated with prognosis in breast cancer patients. Figure 10 A, p<0.001). These factors were then incorporated into a multivariate Cox regression analysis, which showed that age and risk score had predictive power in prognosis (A, p<0.001). Figure 10 B, p<0.001), indicating that the FASN-Score can serve as an independent prognostic factor in clinical practice. Next, we constructed a nomogram combining clinicopathological features and risk scores to predict overall survival (OS) in breast cancer patients. Figure 10 C). For example Figure 10 As shown in Figure C, when the total score is 402, the 1-year, 3-year, and 5-year survival rates are 0.996, 0.978, and 0.955, respectively, indicating a better prognosis for patients with lower total scores. Calibration curves for 1-year, 3-year, and 5-year OS predictions demonstrate the good performance of the predictive nomogram. Figure 10 D). ROC curve analysis shows that the AUC of the nomogram at 1 year, 3 years, and 5 years are 0.847, 0.848, and 0.839, respectively. Figure 10 These results indicate that nomograms have good predictive performance and are more suitable for clinical prediction of the prognosis of breast cancer patients.

[0072] 3.11. Immune characteristics of high-risk and low-risk groups

[0073] Since the low-risk group had better overall survival, we hypothesized that it might have stronger immune activity to further explore this correlation. First, we performed GSVA and GSEA analyses to investigate the relationship between a breast cancer prognostic model and immunity, and found significant differences in biological processes between the high-risk and low-risk groups. In the low-risk group, signaling pathways were significantly enriched in chemokine signaling, cytokine-cytokine receptor interactions, hematopoietic cell lines, primary immunodeficiency, and T-cell receptor signaling. In contrast, in the high-risk group, pathways were significantly enriched in cell cycle, glycosphingolipid-to-lactose and neolactose biosynthesis, homologous recombination, and steroid synthesis. Figure 11 A). We can observe that the enrichment in the low-risk group is primarily immune-related. Subsequently, we investigated the TME and immune infiltration characteristics associated with the immune landscape of the high-risk and low-risk groups. The results of the ESTIMATE algorithm showed that the ESTIMATE score, immune score, and matrix score of the low-risk group were significantly higher than those of the high-risk group, while the tumor purity was lower in the low-risk group. Figure 11 B, C). This indicates that BRCA patients have a better prognosis in the low-risk group. We then used the CIBERSORT algorithm to further analyze immune cell infiltration in the high-risk and low-risk groups. Box plots showed significant differences in the infiltration of naive B cells, plasma cells, CD8 T cells, γδ T cells, macrophage M0, macrophage M2, resting dendritic cells, and neutrophils between the high-risk and low-risk groups. Figure 11 D). Furthermore, the risk score was negatively correlated with the infiltration of innocent B cells, plasma cells, resting dendritic cells, CD8 T cells, γδ T cells, and regulatory T cells. Figure 11 E). These results indicate a close relationship between breast cancer prognostic models and immune cells, and the low-risk group had higher expression of stromal and immune cells in the TME, further validating our hypothesis.

[0074] 3.12. Relationship between high-risk and low-risk groups and immunotherapy response and evaluation

[0075] Based on these results, we decided to explore the relationship between high-risk and low-risk groups and immunotherapy response. First, we analyzed the relationship between high-risk and low-risk groups and immune checkpoint gene expression. Figure 12 A). Immune checkpoint gene expression was significantly higher in the low-risk group than in the high-risk group. These results suggest that patients in the low-risk group responded better to immunotherapy than those in the high-risk group. Subsequently, we investigated the gene mutation distribution in the high-risk and low-risk groups. The mutation waterfall plot shows ( Figure 12In groups B and C, PIK3CA, TP53, and TTN are the most common mutations (mutation rate >12%) in the low-risk group, while PIK3CAH, TP53, TTN, PIK3CAH, and MUC16 are the most common mutations (mutation rate >12%) in the high-risk group. We can observe that the high-risk group has a higher number of mutations. Notably, the most common mutation type is missense variant.

[0076] The construction and validation results of the FASN-based prognostic model for breast cancer patients further illustrate that FASN is an important target for predicting the prognosis of breast cancer patients.

[0077] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. Application of FASN targets in screening or preparing drugs that act on breast cancer stem cells.

2. The application according to claim 1, characterized in that: FASN enhances the stem cell-like properties of breast cancer stem cells by activating the Wnt / β-catenin signaling pathway.

3. The application according to claim 1, characterized in that: It exerts an anti-breast cancer effect by inhibiting the action of FASN targets on breast cancer stem cells.

4. Application of FASN target in constructing a prognostic model for breast cancer patients. The method for constructing the prognostic model is as follows: (1) Collect TCGA data of breast cancer patients, divide them into training set and test set, and select GSE20685 cohort as validation set; (2) Use StepCox and GMB combination to construct FASN-Score prognostic model and screen prognostic risk genes; (3) Use FASN-Score prognostic model to calculate the risk score of each patient in each cohort, and use the median risk value as the cutoff point to divide patients into high-risk group and low-risk group. Validate the survival difference between high-risk group and low-risk group by observing the Kaplan-Meier survival curves of TCGA training set, test set and GSE20685 validation set; (4) Validate the independent predictive value of FASN-Score prognostic model by univariate Cox regression analysis and multivariate Cox regression analysis.

5. The application according to claim 4, characterized in that: The FASN-Score prognostic model consists of 29 prognostic risk genes, namely: PDP1, AGPAT1, MECP2, ELOVL2, ETFA, SLC6A3, FABP7, SERPINA1, ALOX15, VDAC1, ABCA1, CASP7, PDSS2, IVL, PAPSS2, SDHA, KRT14, ACBD5, CD79A, LTF, PEX3, IGF2R, IL10, BCL2, TBXA2R, BRCA1, SLC25A13, NUS1, and ITGB3.

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