Stigmasterol and barasertib target cuproptosis-related prognostic model for the synergistic treatment of breast cancer.

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Yongqin wang wang, Yuxiao Ma, Junyi Tan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3341565/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Breast cancer (BRCA) has a high incidence and a poor prognosis. Cuproptosis is a crucial regulator of carcinogenesis and tumor progression. However, it has not been reported how cuproptosis in BRCA patients is treated using Chinese and Western medicines. Methods This study investigated how cuproptosis is used to diagnose and treat BRCA. A cuproptosis prognostic model was constructed using a bioinformatics approach. We used LASSO to establish a prognostic model associated with cuproptosis, and demonstrated the reliability of the model with survival analysis. Results CIBERSORT analysis showed that the prognostic model was associated with immune infiltration. An interesting finding from the CellMiner database analysis revealed a high correlation between the risk score and Barasertib. According to network pharmacology and molecular docking analysis, stigmasterol, an active ingredient of Curcuma longa L., may target the gene ADAM9 in the prognostic model. The combination of drugs confirmed that stigmasterol and barasertib had a significant synergistic effect on BRCA cells. Conclusion Our study provides a potential strategy for treating cuproptosis in combination with Chinese and Western medicines for BRCA. Breast cancer cuproptosis synergistic therapeutic malignant development Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Breast cancer (BRCA) seriously affects women and men [ 1 ]. It occurs when cells in the breast tissue grow uncontrollably and form a tumor [ 2 ]. Early detection is essential for effective treatment, but existing diagnostic techniques have limitations [ 3 ]. Although mammograms are the most popular screening method, they can potentially give inaccurate results, including false-negative and false-positive [ 4 ]. The only reliable method of diagnosing BRCA is through a biopsy, which is invasive [ 5 ]. The choice of treatment for BRCA depends on the patient’s overall health; the specific type and stage of the disease; and options such as radiation therapy, chemotherapy, and targeted therapy. Although the quality of treatments has improved over time, there are still limitations [ 6 ]. For example, chemotherapy may have side effects, and radiation therapy may potentially harm healthy tissue [ 7 ]. While BRCA survival rates have increased over years, the disease is still serious and calls for continued research and the development of novel treatments. Cuproptosis is a recently discovered type of cell death that includes dysregulation of copper metabolism in cells [ 8 ]. Although copper is a key trace element needed for many cellular processes, excessive accumulation or depletion of it can disturb cellular homeostasis and cause cuproptotic cell death [ 9 ]. Cuproptosis has also been demonstrated to influence tumor growth and therapeutic response [ 10 ]. When tumor cells proliferate quickly, they often have altered copper metabolism and a higher demand for copper [ 11 ]. Cuproptosis can be triggered in tumor cells by adjusting copper levels or using specific therapeutic agents that target copper metabolism [ 12 ]. This cell death pathway provides a potential route for developing novel anticancer treatments [ 13 ]. In addition to directly removing tumor cells, cuproptosis can influence the tumor microenvironment by inducing an immune response and modifying the activity of adjacent cells [ 14 ]. Thus, understanding cuproptosis and its effects on tumors has the potential to provide useful insights for developing more potent cancer treatments. Cuproptosis and BRCA have been associated in recent studies. The prognostic significance of cuproptosis-related genes (CRGs) in BRCA was investigated in a study, and a risk model for CRGs was constructed [ 15 ]. The role of CRGs in BRCA was investigated in another study using machine learning approaches to identify characteristic genes [ 16 ]. These studies show the potential of cuproptosis as a promising research area for understanding the disease, although it is still poorly known in BRCA. Recent attempts have been made to construct CRG signatures to predict tumor prognosis and develop novel and effective treatments [ 17 ]. Copper ion carriers and chelators have the potential to be effective therapeutic options for tumor treatment, according to several research [ 18 ]. The cuproptosis-based signature for some cancer types, such as glioma, remains less researched. Only a limited number of CRG signatures have been developed, despite many studies trying to identify CRG signatures for predicting tumor prognosis. Therefore, developing novel targets and predictive models for diagnosis and treatment is necessary. Since ancient times, traditional Chinese medicine has used turmeric, also known as Curcuma longa L., to treat diverse ailments [ 19 ]. Recent studies have shown that turmeric has a wide range of robust properties, including antioxidant, anti-inflammatory, antimutagenic, antimicrobial, and anticancer potential [ 20 ]. It has been found that the turmeric-derived active ingredient curcumin offers several advantages, including anti-inflammatory, anticancer, and antioxidant properties [ 21 ]. Traditional Chinese medicine has used turmeric to treat several ailments, including tumors [ 22 ]. A common unsaturated phytosterol found in many plants, including turmeric, stigmasterol is a member of the tetracyclic triterpene class [ 23 , 24 ]. Several health advantages and pharmacological properties of stigmasterol, such as anti-inflammatory, antioxidant, and anticancer activities, have been demonstrated [ 25 ]. Stigmasterol in tumors can inhibit tumor progression and metastasis, induce apoptosis, and improve the efficacy of chemotherapy and radiotherapy [ 26 ]. The effects of stigmasterol on BRCA and its potential as a therapeutic agent must be fully understood via further research. In acute myeloid leukemia cell lines, barasertib (AZD1152), a small Aurora B inhibitor, effectively inhibits cell proliferation, induces polyploidy, and enhances apoptosis [ 27 ]. It has been demonstrated that barasertib inhibits Aurora B kinase activity in lung cancer cells that is sensitive to H446 but resistant to H345 and H748 [ 28 ]. Different types of human cancers have been found to overexpress Aurora B, and this overexpression is associated with a poor prognosis for patients with cancer [ 29 ]. One promising cancer treatment strategy has been the use of inhibitors to target Aurora B, such as barasertib [ 27 ]. BRCA instances have shown that barasertib has the ability to reduce cell proliferation and induce apoptosis in vitro [ 30 ]. More research is required to fully comprehend the impact of barasertib on BRCA and its potential as a therapeutic agent. Similar to stigmasterol, the exact mechanism by which barasertib treats BRCA is unknown. We investigated the mechanism of action of the two drugs in BRCA due to their potential in the diagnosis and treatment of cancer. This study investigated how cuproptosis is used to diagnose and treat BRCA (Fig. 1 A). We constructed a cuproptosis prognostic model using a bioinformatics approach. Additionally, the survival analysis demonstrated that the prognostic model was strongly correlated with risk factors in BRCA survival. The multi-omics analysis revealed that this prognostic model was associated with immune leaching and DNA mutations. Based on the abovementioned results, the combination drug experiment also proved that stigmasterol and barasertib have a significant synergistic effect in BRCA cells. This study has established a solid research foundation for cuproptosis in developing new diagnostic and therapeutic methods for BRCA. Materials and Methods Data collection The Cancer Genome Atlas (TCGA) ( https://portal.gdc.cancer.gov/ ) and Gene Expression Omnibus (GEO) database GSE20685 ( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE20685 ) were used to obtain the mRNA-seq data of patients with BRCA. Clinical information from 1226 BRCA samples was used to create TCGA-BRCA, and 327 BRCA sample data were used to create GSE20685. TCGA- BRCA data were clustered using the cluster package based on 13 cuproptosis-related genes, and differential expression analysis of samples from different groups was performed using the limma package to identify the differentially expressed genes (DEGs). TCGA-BRCA data were clustered using the cluster package. The cut-off was set to log2|fold change| ≥1, and the adjusted p < 0.05 was used to identify the DEGs. Gene Set Enrichment Analysis (GSEA) GSEA was used to determine the target gene’s function using cluster P rofiler in R. Additionally, the high and low expression groups generated Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomics (KEGG) enrichment. The Molecular Signatures Database was used to obtain the KEGG gene set (C2.cp.kegg.v7.2.entrez.gmt) and the GO gene set (C5.go.v7.2.entrez.gmt). The most significantly enriched signaling pathways were found using the following criteria: false discovery rate q-values < 0.25, adjusted p -values 1. Prognostic model Overall survival and gene expression levels in GSE20685 and TCGA-BRCA were explored using a univariate Cox proportional hazards regression model and the R package “survival.” DEGs with independent prognostic values were further identified using the least absolute shrinkage and selection operator (LASSO). A set of independent prognostic genes and the coefficients for each gene were obtained based on the highest lambda value after 1000 cross-validations by LASSO. Patients whose data were included in GSE20685 were divided into two groups according to the median expression value of each gene to determine whether the selected genes were associated with prognosis in patients with BRCA. The prognostic value was calculated using the Kaplan-Meier curve, and p < 0.05 was considered statistically significant. The selected genes’ risk model and predictive accuracy were assessed using the receiver operating characteristic (ROC) curve and area under the curve (AUC). Human Protein Atlas (HPA) database The HPA database can map tissues, cells, and organs using proteomic, transcriptomic, and systems biology data. We collected immunohistochemical staining maps for the risk score genes from BRCA tissue and normal tissue. Tumor immune infiltration analysis Transcripts per kilobase million standardized gene expression matrices were created using the R package (CIBERSORT) with built-in data recording gene expression signature data for 22 immune cell types. Pharmacology of Chinese medicine network The active ingredients were screened in the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database (tcmsp-e.com) for those herbal medicines reported to treat BRCA. Oral bioavailability prediction (OB) ≥ 30%, Drug-likeness (DL) ≥ 0.18. The obtained active ingredients corresponded with risk score genes and therapeutic target genes. The degree of binding between active ingredients and genes was calculated using Autodock software. Sensitivity analysis for chemotherapy Sensitivity data for FDA-approved anticancer drugs were obtained from the CellMiner database ( https://discover.nci.nih.gov/cellminer/ ) to investigate the anticancer drugs that target the risk genes. Pearson’s correlation analysis between the z-score values of FDA-approved drugs and risk gene expression was used to screen the results for therapeutic agents inducing sensitivity in BRCA. The z-score was positively correlated with drug sensitivity. The results with Pearson’s correlation coefficient | >0.3 and p < 0.05 were used. Cell culture The American Type Culture Collection (ATCC) provided MCF-7 cells cultured in Dulbecco's Modified Eagle Medium (DMEM) (ATCC, USA). The media were supplemented with 10% fetal bovine serum (FBS, Thermo, USA), penicillin (100 U/mL), and streptomycin (100 mg/mL) (Thermo). The media were then incubated at 37ºC with 5% carbon dioxide (CO 2 ) in a humidified incubator. Fresh medium was added 2–3 times a week, 0.25% Trypsin-EDTA (Gibco, USA) was added at room temperature for 1–2 min, and the cells were passaged at 1:2 and 1:3 dilutions. Cell Counting Kit-8 (CCK8) assay In 96-well plates, a total of 5 × 10 3 cells were seeded. After 24 h of cell culture, 10 µL of CCK-8 solution (Biosharp, USA, Cat. BS350B) was added to the plates. Cells were cultured at 37℃ for 1 h with 5% CO 2 . The proliferative activity was then assessed by measuring the absorbance value at 450 nm using an enzyme marker. The experiments were repeated three times. Colony formation assay In DMEM medium supplemented with 10% FBS. One hundred cells were seeded in 6-well plates and incubated at 37°C in a cell culture incubator. Evert 7 days, the culture medium was changed. Following 20 days of stimulation with afatinib and quercetin at different concentrations, the cells were fixed in 100% methanol for 30 min. Then, staining with 0.2% crystal violet at room temperature for 15 min. The colonies were observed and counted under a light microscope after the cells had been stained and had undergone three phosphate-buffered saline washes. Statistical analysis Statistical analyses were performed using R v4.2.1. The applicability of the model was assessed through ROC curve analysis. The difference in survival between the two risk groups was compared using KM curves. The prognostic value of risk scores was evaluated using univariate Cox analysis, deriving hazard ratios (HR) and 95% confidence intervals for each variable. GraphPad was used to analyze the assay results. p < 0.05 was considered statistically significant. Results Cuproptosis-associated subtypes and DEGs of BRCA Cuproptosis is important for BRCA. Based on the expression matrix of 13 genes associated with cuproptosis, 327 patients with BRCA samples from GEO were classified into k groups (k = 2–9) with different cuproptosis levels. When k = 2, we can classify objects more effectively (Figs. 2 A and S1 A, B). We then compared the overall survival rates of the two clusters using KM analysis and discovered that group 1 ( p = 0.0052) had significantly better survival than group 2 (Fig. 2 B). We then performed differential analysis on the two clusters using the limma package and identified 150 upregulated and 284 downregulated differential genes (Figs. 2 C, D and S1 C). We also performed a GSEA analysis on these differential genes and discovered that they were primarily enriched in the cell proliferation pathway (Figs. 2 E, F and S1 D, E). Construction and verification of the cuproptosis-associated prognostic signature Then, using one-way Cox analysis to screen 1080 BRCA oncogenes (HR > 1, p < 0.05), we discovered 25 candidate genes by overlapping them with genes that were upregulated in group cuproptosis (Fig. 3 A). On these genes, LASSO regression analysis was performed to eliminate the effect of false positives. Data from GSE20685 and TCGA-BRCA were used as the training and validation sets, respectively. Twenty genes were identified in the training set to calculate the risk coefficient: Risk score = 0.0980 × Expression ( ADAM9 ) − 0.1232 × Expression ( OCRL ) + 0.0982 × Expression ( STK3 ) − 0.0970 × Expression ( SLC6A1 ) + 0.0452 × Expression ( LMO1 ) + 0.1874 × Expression ( TMED9 ) + 0.0783 × Expression ( TUBA1C ) + 0.0136 × Expression ( BRIX1 ) + 0.0180 × Expression ( GRHL2 ) + 0.1080 × Expression ( LONRF3 ) + 0.1986 × Expression ( TMC7 ) + 0.0186 × Expression ( ADAMTS7 ) + 0.0588 × Expression ( ARMC1 ) + 0.1606 × Expression ( KCTD15 ) + 0.2387 × Expression ( ZFHX3 ) + 0.0045 × Expression ( IYD ) + 0.0628 × Expression ( CASP14 ) + 0.0232 × Expression ( PRSS27 ) + 0.3511 × Expression ( TMEM65 ) + 0.0838 × Expression ( QPRT ) (Figs. 3 B-D). The AUC value in the ROC curve was 0.803 over one year (Fig. 3 F), and the threshold values for the high-risk and low-risk groups were set to the median (Figures S2A, B). The difference in survival between the high-risk and low-risk groups in the training set was also more significant (Figs. 3 E and S2 C). These findings were validated in the validation set (Figure S3A-E). Multi-omics expression of risk score-related genes in BRCA We performed a multi-omics analysis to investigate the expression of risk score genes in BRCA tumors. First, mutations were created on the ZFHX3 , GRHL2 , ADAMTS7 , OCRL , TUBA1C and LONRF3 DNA sequences at the DNA level (Fig. 4 A). However, TMED9, TUBA1C, and CASP14 nucleic acid and protein levels were found to be significantly upregulated in BRCA tumors by Genotype-Tissue Expression database and HPA database analyses (Figs. 4 B, C). TMC7 , on the other hand, was only upregulated at the protein level. In contrast, ZFHX3 , IYD , and SLC6A1 were only upregulated at the nucleic acid level. The data above demonstrated that most risk-score genes had high DNA, RNA, or protein expression levels. Immune cell infiltration in patients with BRCA in the high-risk and low-risk groups The percentage of various immune cell types (Figs. 5 A-D and S4 A-D) and the correlation of various immune cells (Figs. 5 E and S4 E) were examined in the high-risk and low-risk groups and in the BRCA tumor versus paraneoplastic groups, respectively to observe the correlation between our prognostic model and the immune microenvironment in BRCA tumors. Significant differences in the level of naïve B cells, macrophages M2 cells, CD4 memory resting cells, and monocytes cells were found between the BRCA tumor group and the paracancer group when compared (Figure S4F). Additionally, the high-risk group had considerably more immunological infiltration of macrophages than the low-risk group (Fig. 5 F). According to the results mentioned above, there is a significant change in the immune microenvironment during BRCA tumorigenesis, and this change has some correlation with the risk score predicted by the prognostic model used in this study. Exploration of drug sensitivity based on the findings of the prognostic model The prognostic model has been extensively used and validated as a diagnostic tool, but whether it can be used therapeutically to produce antitumor effects is unknown. As a result, we need to investigate anticancer drugs that target this model. As the risk score increased, 228 drugs were identified to be significantly associated with the signature gene ( p 0.3). These drugs included barasertib, AZD-9496, XAV-939, etc. (Figs. 6 A and S5 ). It suggests that these drugs may target genes on risk score and that their sensitivity is related to the risk score. Molecular docking of compounds and targets We investigated whether the component of curcuma, a chemical formulation with anti-BRCA properties, targets oncogenes to achieve a combination of Chinese and Western medicines for treating BRCA tumors. The 358 target genes corresponding to the three active ingredients of turmeric were discovered using the TCMSP database (Figs. 7 A, B). Stigmasterol targets the oncogene ADAM9 (Fig. 8 B). Molecular docking with stigmasterol vs. ADAM9 score = − 8.6 kcal/mol was used to mimic the binding capacity between these active ingredients and the discovered targets and the active compounds (Figs. 7 C, D and S6 ). According to molecular docking results, the capacity of these active ingredients and target proteins to bind varies. We focused on the drug stigmasterol for further research since it has the highest binding energy to the ADAM9 molecule and binds the most target genes. Turmeric combined with barasertib has a synergistic antitumor effect on BRCA We selected the top 3 drugs with cor values based on Cellminer results, and we determined the IC50 using the CCK8 assay in MCF-7 cells, respectively (Figs. 8 A and S7 ). We selected barasertib drugs for subsequent combination assays based on the IC50 and cor values. We combined stigmasterol and barasertib to treat MCF-7 cells to investigate the inhibitory effect of combined Chinese and Western treatments on BRCA. Stigmasterol and barasertib worked better together than alone to lower MCF-7 cell viability (Figs. 8 B, C). The results of the CCK8 assay and the clone formation assay were the same as above (Figs. 8 D-E). Discussion BRCA is a common malignant tumor distinguished by its aggressive nature and poor prognosis. The medical community is still looking toward novel and effective methods for diagnosis and treatment. Cuproptosis, a type of regulated cell death, can potentially induce tumor cells, especially those in BRCA, to die. Cuproptosis is a method for diagnosing and treating BRCA, although its precise targets and techniques have not yet been fully elucidated. This study uses LASSO to create a predictive model for CRGs in BRCA to address this knowledge gap. A subsequent CIBERSORT analysis was conducted to assess the prognostic potential of the developed model, and the results showed a correlation between this model and immune infiltration in BRCA. This study suggests that the immune system plays a significant role in initiating cuproptosis and its effects on the tumor microenvironment. The synergistic effect of stigmasterol and barasertib on BRCA cells was another exciting study discovery. These findings suggest a novel combinational treatment strategy for BRCA. Cuproptosis is a recently discovered programmed cell death. The association between cuproptosis and tumors has been investigated, and the associations between CRGs and various tumor characteristics across common cancer types have been investigated. Cuproptosis has been associated with c-Myc-mediated BRCA and is dysregulated in BRCA tissues [ 31 ]. A prognostic cuproptosis-related signature in BRCA has also been identified through studies, and it not only predicts patient outcomes but also sheds light on the immune microenvironment. According to these findings, cuproptosis may represent a novel target for diagnosis and treatment [ 32 , 33 ]. We conducted difference analysis and Cox unifactor analysis for the two patient groups, trained the obtained genes through LASSO, and obtained the calculation formula for risk score, which was used as a prognosis prediction model for patients with cuproptosis. The cluster analysis was based on the expression levels of 13 CRGs in BRCA. ADAM9 is a member of the ADAM family of proteins, which is involved in several physiological and pathological processes, including the progression of cancer. According to studies, ADAM9 is overexpressed and associated with higher tumor development grade, positive lymph node status, and distant metastasis in triple-negative BRCA (TNBRCA) compared to non-TNBRCA. The motility, invasion, and proliferation of cancer cells are all promoted by ADAM9 . Recent research has demonstrated that the gene signatures associated with cuproptosis in lower-grade gliomas contain ADAM9 . Here, we performed a KM survival analysis and calculated the AUC value in the training and validation groups, respectively. This proved that the risk score of our prediction model was in high agreement with the actual prognosis of patients. Additionally, we used several methods to verify the practicability and reliability of the model in the training and validation groups. The tumor immune microenvironment (TME), which comprises complex interactions between cellular and non-cellular elements, plays a crucial role in the advancement and progression of cancer [ 34 ]. Cancer cells, immune cells, stromal tissue, and extracellular matrix are all parts of the TME [ 35 ]. The immune system has a significant impact on the TME, and the condition of the immune system within the TME is essential for the prevention, development, and progression of tumors [ 36 ]. Significant correlations have been found between the immunological constituents of the TME and tumor development, recurrence, and metastasis [ 37 ]. Tumor progression and therapeutic response in BRCA are contacted by the TME [ 38 ]. The balance between pro- and anti-tumor immune responses determines whether the TME can promote and inhibit tumor growth. [ 39 ]. Understanding the TME and its role in tumor development and progression is crucial for immune-targeted cancer therapies to be as effective as possible. This study revealed significant variations in the infiltration of B cells, macrophages, NK cells, and T cells between cancer and normal groups and between the high-risk and low-risk groups. These investigations suggest that our prognostic model determines the clinical prognosis and, to a certain extent, the immune infiltration of tumor cells. Targeted therapy, a type of cancer treatment, uses drugs to specifically target cancer growth and survival by focusing on certain genes, proteins, or the tissue environment involved [ 40 ]. Cancer cells can be targeted using various targeted drugs, including monoclonal antibodies, hormone therapy, and targeted therapy [ 41 ]. Trastuzumab, pertuzumab, trastuzumab deruxtecan, and other drugs are specific examples of targeted therapies used to treat BRCA. [ 42 ]. Targeted therapy does have some limitations, including the development of drug resistance, side effects, and expensive treatment. Additionally, only some types of BRCA, such as HER2-positive and hormone receptor-positive BRCA, respond to targeted therapy [ 43 ]. It is important to create novel targeted therapies to overcome these limitations and improve the effectiveness of BRCA treatment. A phytosterol exhibits anticancer properties against several cancer cell lines through multiple mechanisms. These mechanisms include inhibiting tumor cell proliferation, reducing tumor angiogenesis, and inducing cancer cell apoptosis. In human gastric cancer cells, stigmasterol effectively inhibits cell migration, induces cell cycle arrest, triggers mitochondrial-mediated apoptosis, and impedes the JAK/STAT signaling pathway, resulting in strong antitumor effects [ 44 ]. The potential clinical utility of phytosterols in cancer treatment is hampered by the lack of standardization and their limited pharmacological action. Conversely, leukemia, colorectal cancer, and BRCA all exhibit a dose-dependent reduction of cell proliferation in vivo when treated with the Aurora kinase B inhibitor barasertib. Barasertib has become well-known as a therapeutic drug after extensive research in numerous tumor types. However, drug toxicity affected approximately 25% of patients with solid tumor, with neutropenia being the most prevalent dose-limiting toxicity [ 45 ]. Additional research is required to determine the ideal dosage, effectiveness, and safety of barasertib in cancer therapy. Analysis of the sensitivity of many regularly used chemotherapeutic drugs revealed a positive correlation between risk score-related genes and sensitivity. Notably, the risk score showed a strong association with barasertib, suggesting that it may be an effective drug for treating the cuproptosis prognosis model. Additionally, a prognostic model using molecular docking and network pharmacology revealed that stigmasterol, a compound present in turmeric, targets the ADAM9 protein. Therefore, we selected stigmasterol, which had the strongest binding ability, and barasertib, the most sensitive drug, for combined use in vitro . The results revealed that the two drugs synergically inhibited BRCA cells. Our study identified a novel and reliable strategy for treating BRCA and a solid theoretical foundation for combining traditional Chinese medicine and Western medicine. Declarations Acknowledgements Not applicable. Authors ’ contributions All authors contributed substantially to this manuscript. Yongqin wang and Yuxiao Ma performed the majority of experiments and data analysis. Yongqin wang contributed to carrying out the experiments and interpreted the results. Yuxiao Ma contributed to wrote the manuscript. Junyi Tan revised the manuscript and designed and conducted the project. All authors read and approved the final manuscript. Funding Not applicable. D ata Availability The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. Conflict of interest The authors have no relevant financial or non-financial interests to disclose. Ethics approval and consent to participate Not applicable. Competing interests The authors declare that they have no competing interests. Consent for publication All the authors agreed on the publication of this manuscript. 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Cells 9(7):1725 Fu T et al (2021) Spatial architecture of the immune microenvironment orchestrates tumor immunity and therapeutic response. J Hematol Oncol 14(1):98 Gruosso T et al (2019) Spatially distinct tumor immune microenvironments stratify triple-negative breast cancers. J Clin Investig 129(4):1785–1800 Saeed M, Gao J, Shi Y, Lammers T, Yu H (2019) Engineering nanoparticles to reprogram the tumor immune microenvironment for improved cancer immunotherapy. Theranostics 9(26):7981 Lee YT, Tan YJ, Oon CE (2018) Molecular targeted therapy: Treating cancer with specificity. Eur J Pharmacol 834:188–196 Mele L et al (2020) The role of autophagy in resistance to targeted therapies. Cancer Treat Rev 88:102043 Adams E, Wildiers H, Neven P, Punie K (2021) Sacituzumab govitecan and trastuzumab deruxtecan: two new antibody–drug conjugates in the breast cancer treatment landscape. ESMO open 6(4):100204 Ayoub NM, Al-Shami KM, Yaghan RJ (2019) Immunotherapy for HER2-positive breast cancer: recent advances and combination therapeutic approaches. Breast Cancer: Targets and Therapy, : p. 53–69 Li K et al (2018) Stigmasterol exhibits potent antitumor effects in human gastric cancer cells mediated via inhibition of cell migration, cell cycle arrest, mitochondrial mediated apoptosis and inhibition of JAK/STAT signalling pathway. J BUON 23(5):1420–1425 Johnson ML et al (2023) Safety, tolerability, and pharmacokinetics of Aurora kinase B inhibitor AZD2811: a phase 1 dose-finding study in patients with advanced solid tumours. Br J Cancer 128(10):1906–1915 Additional Declarations No competing interests reported. Supplementary Files S1.png Figure S1 PCA and GO results. (A) Consensus clustering cumulative distribution function (CDF) for k=2 to 10 in GSE20685 dataset. (B) Relative change in area under CDF curve for k=2 to 10 in GSE20685 dataset. (C) Principal component analysis (PCA) of patients in two cuproptosis-associated clusters. (D) The representative results of the Gene Ontology analysis between two cluster groups. (E) The representative results of the GSEA analysis between two cluster groups. S2.png Figure S2 Validation set rsults. (A) The risk scores of BRCA in GSE20685 database. (B) The PCA analysis of patients with high- and low-risk score groups. (C) The distribution of survival status and risk scores in BRCA patients. S3.png Figure S3 Validation of a risk-prediction model in test dataset. (A) Risk score in the TCGA-BECA test set, patient survival, and expression of 20 DEGs in the test set. (B) The risk scores of BRCA in TCGA-BRCA database. (C) Kaplan-Meier curves of patients in two cuproptosis-associated clusters for overall survival. (D) The distribution of survival status and risk scores in BRCA patients. (E) ROC curve of the risk score model. S4.png Figure S4 The immune infiltration of 22 immune cell types in tumor and normal patients with BRCA. (A) The immune infiltration of 22 immune cell types in tumor patients with BRCA. (B) The immune infiltration of 22 immune cell types in normal patients with BRCA. (C) The estimated proportion of the immune cell types in tumor patients with BRCA. (D) The estimated proportion of the immune cell types in normal patients with BRCA. (E) Correlation matrix of all 22 immune cells proportions. Immune cells with higher, lower, and same correlation levels are shown in red, blue, and white, respectively. (F) Violin plot of immune cell infiltration between cancer and normal groups. S5.png Figure S5 Drug sensitivity analysis of prognostic factors. (A) the boxplot of the differences in drug sensitivity between the two clusters was shown. S6.png Figure S6 Molecular docking results. (A) The amplification image of stigmasterol and ADAM9. S7.png Figure S7 The effect of cell proliferation in BRCA cell lines by using barasertib and stigmasterol. (A) The CCK8 assay of MCF-7 cells treating with different concentrations barasertib. (B) The CCK8 assay of MCF-7 cells treating with different concentrations AZD-9496. (C) The CCK8 assay of MCF-7 cells treating with different concentrations XAV-939. Data shown represent mean SD from three independent experiments. **p<0.01; ***p<0.001; ****p<0.0001. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3341565","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":232299465,"identity":"3d133226-cfcf-4354-a42d-31a56a0c54db","order_by":0,"name":"Yongqin wang wang","email":"","orcid":"","institution":"Traditional Chinese Medicine Hospital of Shizhu","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yongqin","middleName":"wang","lastName":"wang","suffix":""},{"id":232299466,"identity":"0f333aac-e5bd-4318-a124-a5af04cbb5d9","order_by":1,"name":"Yuxiao Ma","email":"","orcid":"","institution":"Traditional Chinese Medicine Hospital of Shizhu","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yuxiao","middleName":"","lastName":"Ma","suffix":""},{"id":232299467,"identity":"0c4518e3-eca0-4aba-bd8c-a4799a8c18af","order_by":2,"name":"Junyi Tan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYBACNv7mgw8+8Ngw8zPzf3yQUFFDWAufxLFkwxkyaeyS7QzGBg/OHCOsRY4hx0yaw+YQv8F5BjPJhy3MRDiM4ViaNEPOAWnJZoa0isQGNgb+9u4E/FqYmw9bF5y5Y8zPzHDsRuIOGQaJM2c3ELIl8fbMnmfJks2MbTcSz7AxGEjkEtKSYyDN++9w/YbDzGwFiW3MRGkxkubhOcxscJiNjYE4LeBA5kljlmzmYZZIOHOMh6Bf5PthUcl/hvHjj4oaOf72XvxaMAAPacpHwSgYBaNgFGAFALyeR7rpaZkDAAAAAElFTkSuQmCC","orcid":"","institution":"Traditional Chinese Medicine Hospital of Shizhu","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Junyi","middleName":"","lastName":"Tan","suffix":""}],"badges":[],"createdAt":"2023-09-10 08:14:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3341565/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3341565/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":43149295,"identity":"63986b44-7ea9-49e0-946a-790739b8eff8","added_by":"auto","created_at":"2023-09-14 17:40:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":323943,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of the study. (A) Study pattern diagram.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3341565/v1/b67bfae43c525cf53cdf3493.png"},{"id":43149296,"identity":"cf80538c-2119-4796-af9e-bfb9424be756","added_by":"auto","created_at":"2023-09-14 17:40:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":806677,"visible":true,"origin":"","legend":"\u003cp\u003eDifferentially expressed gene analysis of BRCA patients based on cuproptosis-associated clusters. (A) The consensus matrix of all samples when k=2. (B) Kaplan-Meier curves of patients in 2 cuproptosis-associated clusters for overall survival. (C) Volcano plot and (D) heatmap of cuproptosis-associated DEGs between cluster 1 and cluster 2. (E-F) GSEA analysis of DEGs between two cluster groups.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3341565/v1/c71def2bc720fac6731f1d1e.png"},{"id":43150044,"identity":"fad67e66-dabb-4237-8032-30744061c974","added_by":"auto","created_at":"2023-09-14 17:48:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":433089,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction of a risk-prediction model of the training set and analyses of model performance. (A) Univariate Cox regression analysis of top 20 genes correlated with cuproptosis. (B) LASSO regression coefficient profile. (C) LASSO deviance profile. (D) Risk score in the GSE20685 test set, patient survival, and expression of 20 DEGs in the test set. (E) Kaplan-Meier curves of patients in two cuproptosis-associated clusters for overall survival. (F) ROC curve of the risk score model.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3341565/v1/07012e8bbea77c61b5c15e96.png"},{"id":43150851,"identity":"3aedeec5-e01b-409d-917f-6d5d7cdfe9e5","added_by":"auto","created_at":"2023-09-14 17:56:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1629824,"visible":true,"origin":"","legend":"\u003cp\u003eThe expression levels of the risk score genes in multi-omics data. (A) The mutation of the risk score genes in BRCA patients. (B) The RNA levels of the risk score genes in Genotype-Tissue Expression database. (C) The proten levels of the risk genes in HPA database.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3341565/v1/4585970e033790c49e34e828.png"},{"id":43150042,"identity":"bfcb6b3a-d2af-4e73-9830-50c656f76530","added_by":"auto","created_at":"2023-09-14 17:48:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":492816,"visible":true,"origin":"","legend":"\u003cp\u003eThe immune infiltration of 22 immune cell types in high and low risk patients with BRCA. (A) The immune infiltration of 22 immune cell types in high risk patients with BRCA. (B) The immune infiltration of 22 immune cell types in low risk patients with BRCA. (C) The estimated proportion of the immune cell types in high risk patients with BRCA. (D) The estimated proportion of the immune cell types in low risk patients with BRCA. (E) The correlation of various immune cells in patients with BRCA. (F) Violin plot of immune cell infiltration between high risk patients and low risk patients groups.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3341565/v1/0a2a584f40b84c0cb4dcdf24.png"},{"id":43150849,"identity":"aeb05e8a-67b7-45ee-af81-aac4f3bf749a","added_by":"auto","created_at":"2023-09-14 17:56:06","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":163276,"visible":true,"origin":"","legend":"\u003cp\u003eDrug sensitivity analysis of prognostic factors. (A) The correlation scatter plot between the risk score value and the drug IC50 value in CellMiner database was selected.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-3341565/v1/5830b8dc7a2afb83897e2620.png"},{"id":43149309,"identity":"ccd2dcaf-4627-4423-942f-fac15f6b5254","added_by":"auto","created_at":"2023-09-14 17:40:06","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":849119,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork pharmacology and molecular docking of active ingredients of Curcuma and cuproptosis-related signature genes. (A) The flow of network pharmacology and molecular docking. (B) The overlap genes between 358 targeted genes of active ingredients in Curcuma and 20 riskscore genes. (C-D) The 3D structure model of small molecule and protein.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-3341565/v1/06a8f1f25498461db06834f9.png"},{"id":43149303,"identity":"e10b4ad1-97e4-43a0-baae-9d2b205c7ad5","added_by":"auto","created_at":"2023-09-14 17:40:06","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":558160,"visible":true,"origin":"","legend":"\u003cp\u003eThe effect of cell proliferation in BRCA cell lines by using barasertib and stigmasterol. (A) The IC50 values of barasertib, AZD-9496 and XAV-939 for cell viability inhibition in BRCA cancer cell line MCF-7 were determined. (B) The HAS synergy and antagonism of barasertib and stigmasterol in MCF-7 cell. (C) The cell percentages of MCF-7 treated with barasertib and stigmasterol. (D) The CCK8 assay of MCF-7 cell treated with different drugs. (E) MCF-7 cells were cultured with stigmasterol and/or barasertib. Analyzing the induced colony formation and calculate the colony formation. Data shown represent mean SD from three independent experiments. **p\u0026lt;0.01; ***p\u0026lt;0.001; ****p\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-3341565/v1/ba3d2343131d1d796b4ecc26.png"},{"id":45279014,"identity":"813db822-45c8-4e7a-aba3-2a8ff2af4e04","added_by":"auto","created_at":"2023-10-26 18:07:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4517586,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3341565/v1/94b88205-cfa3-4215-a91f-ea56994253a2.pdf"},{"id":43151242,"identity":"f593b4e1-0839-4c3a-81cc-301d80b2c854","added_by":"auto","created_at":"2023-09-14 18:04:06","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":359201,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S1 PCA and GO results. (A) Consensus clustering cumulative distribution function (CDF) for k=2 to 10 in GSE20685 dataset. (B) Relative change in area under CDF curve for k=2 to 10 in GSE20685 dataset. (C) Principal component analysis (PCA) of patients in two cuproptosis-associated clusters. (D) The representative results of the Gene Ontology analysis between two cluster groups. (E) The representative results of the GSEA analysis between two cluster groups.\u003c/p\u003e","description":"","filename":"S1.png","url":"https://assets-eu.researchsquare.com/files/rs-3341565/v1/bf8d11ee51c546328f5eb490.png"},{"id":43149294,"identity":"41bbc0c3-7193-48ee-a535-ba9ab1b915e7","added_by":"auto","created_at":"2023-09-14 17:40:05","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":130145,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S2 Validation set rsults. (A) The risk scores of BRCA in GSE20685 database. (B) The PCA analysis of patients with high- and low-risk score groups. (C) The distribution of survival status and risk scores in BRCA patients.\u003c/p\u003e","description":"","filename":"S2.png","url":"https://assets-eu.researchsquare.com/files/rs-3341565/v1/1c2eb19138c7aa0bc4632f56.png"},{"id":43150039,"identity":"8db1f6fa-1860-476c-90e3-5619a271c8be","added_by":"auto","created_at":"2023-09-14 17:48:06","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":343036,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S3 Validation of a risk-prediction model in test dataset. (A) Risk score in the TCGA-BECA test set, patient survival, and expression of 20 DEGs in the test set. (B) The risk scores of BRCA in TCGA-BRCA database. (C) Kaplan-Meier curves of patients in two cuproptosis-associated clusters for overall survival. (D) The distribution of survival status and risk scores in BRCA patients. (E) ROC curve of the risk score model.\u003c/p\u003e","description":"","filename":"S3.png","url":"https://assets-eu.researchsquare.com/files/rs-3341565/v1/1c3e5b949bcbcaf752fca793.png"},{"id":43149301,"identity":"1caab254-43e8-4340-86da-5a3dcbdfaf29","added_by":"auto","created_at":"2023-09-14 17:40:06","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":683376,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S4 The immune infiltration of 22 immune cell types in tumor and normal patients with BRCA. (A) The immune infiltration of 22 immune cell types in tumor patients with BRCA. (B) The immune infiltration of 22 immune cell types in normal patients with BRCA. (C) The estimated proportion of the immune cell types in tumor patients with BRCA. (D) The estimated proportion of the immune cell types in normal patients with BRCA. (E) Correlation matrix of all 22 immune cells proportions. Immune cells with higher, lower, and same correlation levels are shown in red, blue, and white, respectively. (F) Violin plot of immune cell infiltration between cancer and normal groups.\u003c/p\u003e","description":"","filename":"S4.png","url":"https://assets-eu.researchsquare.com/files/rs-3341565/v1/827f99261df04a813b34d125.png"},{"id":43149306,"identity":"8de17366-e71d-4b2c-b742-59df22d32f6d","added_by":"auto","created_at":"2023-09-14 17:40:06","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":125514,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S5 Drug sensitivity analysis of prognostic factors. (A) the boxplot of the differences in drug sensitivity between the two clusters was shown.\u003c/p\u003e","description":"","filename":"S5.png","url":"https://assets-eu.researchsquare.com/files/rs-3341565/v1/7b1ce348e88ccb95503df8d3.png"},{"id":43149307,"identity":"9083ac46-c6f2-4037-9c12-a7558c098a1b","added_by":"auto","created_at":"2023-09-14 17:40:06","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":954445,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S6 Molecular docking results. (A) The amplification image of stigmasterol and ADAM9.\u003c/p\u003e","description":"","filename":"S6.png","url":"https://assets-eu.researchsquare.com/files/rs-3341565/v1/d7d5a7da1c065244fc6c946f.png"},{"id":43149308,"identity":"429709c7-0056-49bf-9e23-4ffacf48de4e","added_by":"auto","created_at":"2023-09-14 17:40:06","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":88482,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S7 The effect of cell proliferation in BRCA cell lines by using barasertib and stigmasterol. (A) The CCK8 assay of MCF-7 cells treating with different concentrations barasertib. (B) The CCK8 assay of MCF-7 cells treating with different concentrations AZD-9496. (C) The CCK8 assay of MCF-7 cells treating with different concentrations XAV-939. Data shown represent mean SD from three independent experiments. **p\u0026lt;0.01; ***p\u0026lt;0.001; ****p\u0026lt;0.0001.\u003c/p\u003e","description":"","filename":"S7.png","url":"https://assets-eu.researchsquare.com/files/rs-3341565/v1/6f138d8727b4deb15dab1762.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Stigmasterol and barasertib target cuproptosis-related prognostic model for the synergistic treatment of breast cancer.","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBreast cancer (BRCA) seriously affects women and men [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It occurs when cells in the breast tissue grow uncontrollably and form a tumor [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Early detection is essential for effective treatment, but existing diagnostic techniques have limitations [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Although mammograms are the most popular screening method, they can potentially give inaccurate results, including false-negative and false-positive [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The only reliable method of diagnosing BRCA is through a biopsy, which is invasive [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The choice of treatment for BRCA depends on the patient\u0026rsquo;s overall health; the specific type and stage of the disease; and options such as radiation therapy, chemotherapy, and targeted therapy. Although the quality of treatments has improved over time, there are still limitations [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. For example, chemotherapy may have side effects, and radiation therapy may potentially harm healthy tissue [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. While BRCA survival rates have increased over years, the disease is still serious and calls for continued research and the development of novel treatments.\u003c/p\u003e \u003cp\u003eCuproptosis is a recently discovered type of cell death that includes dysregulation of copper metabolism in cells [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Although copper is a key trace element needed for many cellular processes, excessive accumulation or depletion of it can disturb cellular homeostasis and cause cuproptotic cell death [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Cuproptosis has also been demonstrated to influence tumor growth and therapeutic response [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. When tumor cells proliferate quickly, they often have altered copper metabolism and a higher demand for copper [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Cuproptosis can be triggered in tumor cells by adjusting copper levels or using specific therapeutic agents that target copper metabolism [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This cell death pathway provides a potential route for developing novel anticancer treatments [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In addition to directly removing tumor cells, cuproptosis can influence the tumor microenvironment by inducing an immune response and modifying the activity of adjacent cells [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Thus, understanding cuproptosis and its effects on tumors has the potential to provide useful insights for developing more potent cancer treatments. Cuproptosis and BRCA have been associated in recent studies. The prognostic significance of cuproptosis-related genes (CRGs) in BRCA was investigated in a study, and a risk model for CRGs was constructed [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The role of CRGs in BRCA was investigated in another study using machine learning approaches to identify characteristic genes [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These studies show the potential of cuproptosis as a promising research area for understanding the disease, although it is still poorly known in BRCA. Recent attempts have been made to construct CRG signatures to predict tumor prognosis and develop novel and effective treatments [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Copper ion carriers and chelators have the potential to be effective therapeutic options for tumor treatment, according to several research [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The cuproptosis-based signature for some cancer types, such as glioma, remains less researched. Only a limited number of CRG signatures have been developed, despite many studies trying to identify CRG signatures for predicting tumor prognosis. Therefore, developing novel targets and predictive models for diagnosis and treatment is necessary.\u003c/p\u003e \u003cp\u003eSince ancient times, traditional Chinese medicine has used turmeric, also known as \u003cem\u003eCurcuma longa\u003c/em\u003e L., to treat diverse ailments [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Recent studies have shown that turmeric has a wide range of robust properties, including antioxidant, anti-inflammatory, antimutagenic, antimicrobial, and anticancer potential [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. It has been found that the turmeric-derived active ingredient curcumin offers several advantages, including anti-inflammatory, anticancer, and antioxidant properties [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Traditional Chinese medicine has used turmeric to treat several ailments, including tumors [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. A common unsaturated phytosterol found in many plants, including turmeric, stigmasterol is a member of the tetracyclic triterpene class [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Several health advantages and pharmacological properties of stigmasterol, such as anti-inflammatory, antioxidant, and anticancer activities, have been demonstrated [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Stigmasterol in tumors can inhibit tumor progression and metastasis, induce apoptosis, and improve the efficacy of chemotherapy and radiotherapy [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The effects of stigmasterol on BRCA and its potential as a therapeutic agent must be fully understood via further research. In acute myeloid leukemia cell lines, barasertib (AZD1152), a small Aurora B inhibitor, effectively inhibits cell proliferation, induces polyploidy, and enhances apoptosis [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. It has been demonstrated that barasertib inhibits Aurora B kinase activity in lung cancer cells that is sensitive to H446 but resistant to H345 and H748 [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Different types of human cancers have been found to overexpress Aurora B, and this overexpression is associated with a poor prognosis for patients with cancer [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. One promising cancer treatment strategy has been the use of inhibitors to target Aurora B, such as barasertib [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. BRCA instances have shown that barasertib has the ability to reduce cell proliferation and induce apoptosis \u003cem\u003ein vitro\u003c/em\u003e [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. More research is required to fully comprehend the impact of barasertib on BRCA and its potential as a therapeutic agent. Similar to stigmasterol, the exact mechanism by which barasertib treats BRCA is unknown. We investigated the mechanism of action of the two drugs in BRCA due to their potential in the diagnosis and treatment of cancer.\u003c/p\u003e \u003cp\u003eThis study investigated how cuproptosis is used to diagnose and treat BRCA (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). We constructed a cuproptosis prognostic model using a bioinformatics approach. Additionally, the survival analysis demonstrated that the prognostic model was strongly correlated with risk factors in BRCA survival. The multi-omics analysis revealed that this prognostic model was associated with immune leaching and DNA mutations. Based on the abovementioned results, the combination drug experiment also proved that stigmasterol and barasertib have a significant synergistic effect in BRCA cells. This study has established a solid research foundation for cuproptosis in developing new diagnostic and therapeutic methods for BRCA.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eThe Cancer Genome Atlas (TCGA) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and Gene Expression Omnibus (GEO) database GSE20685 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE20685\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE20685\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) were used to obtain the mRNA-seq data of patients with BRCA. Clinical information from 1226 BRCA samples was used to create TCGA-BRCA, and 327 BRCA sample data were used to create GSE20685. TCGA- BRCA data were clustered using the cluster package based on 13 cuproptosis-related genes, and differential expression analysis of samples from different groups was performed using the limma package to identify the differentially expressed genes (DEGs). TCGA-BRCA data were clustered using the cluster package. The cut-off was set to log2|fold change| \u0026ge;1, and the adjusted \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was used to identify the DEGs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eGene Set Enrichment Analysis (GSEA)\u003c/h2\u003e \u003cp\u003eGSEA was used to determine the target gene\u0026rsquo;s function using cluster\u003cem\u003eP\u003c/em\u003erofiler in R. Additionally, the high and low expression groups generated Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomics (KEGG) enrichment. The Molecular Signatures Database was used to obtain the KEGG gene set (C2.cp.kegg.v7.2.entrez.gmt) and the GO gene set (C5.go.v7.2.entrez.gmt). The most significantly enriched signaling pathways were found using the following criteria: false discovery rate q-values\u0026thinsp;\u0026lt;\u0026thinsp;0.25, adjusted \u003cem\u003ep\u003c/em\u003e-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and absolute normalized enrichment scores\u0026thinsp;\u0026gt;\u0026thinsp;1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePrognostic model\u003c/h2\u003e \u003cp\u003eOverall survival and gene expression levels in GSE20685 and TCGA-BRCA were explored using a univariate Cox proportional hazards regression model and the R package \u0026ldquo;survival.\u0026rdquo; DEGs with independent prognostic values were further identified using the least absolute shrinkage and selection operator (LASSO). A set of independent prognostic genes and the coefficients for each gene were obtained based on the highest lambda value after 1000 cross-validations by LASSO. Patients whose data were included in GSE20685 were divided into two groups according to the median expression value of each gene to determine whether the selected genes were associated with prognosis in patients with BRCA. The prognostic value was calculated using the Kaplan-Meier curve, and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. The selected genes\u0026rsquo; risk model and predictive accuracy were assessed using the receiver operating characteristic (ROC) curve and area under the curve (AUC).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eHuman Protein Atlas (HPA) database\u003c/h2\u003e \u003cp\u003eThe HPA database can map tissues, cells, and organs using proteomic, transcriptomic, and systems biology data. We collected immunohistochemical staining maps for the risk score genes from BRCA tissue and normal tissue.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eTumor immune infiltration analysis\u003c/h2\u003e \u003cp\u003eTranscripts per kilobase million standardized gene expression matrices were created using the R package (CIBERSORT) with built-in data recording gene expression signature data for 22 immune cell types.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePharmacology of Chinese medicine network\u003c/h2\u003e \u003cp\u003eThe active ingredients were screened in the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database (tcmsp-e.com) for those herbal medicines reported to treat BRCA. Oral bioavailability prediction (OB)\u0026thinsp;\u0026ge;\u0026thinsp;30%, Drug-likeness (DL)\u0026thinsp;\u0026ge;\u0026thinsp;0.18. The obtained active ingredients corresponded with risk score genes and therapeutic target genes. The degree of binding between active ingredients and genes was calculated using Autodock software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analysis for chemotherapy\u003c/h2\u003e \u003cp\u003eSensitivity data for FDA-approved anticancer drugs were obtained from the CellMiner database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://discover.nci.nih.gov/cellminer/\u003c/span\u003e\u003cspan address=\"https://discover.nci.nih.gov/cellminer/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to investigate the anticancer drugs that target the risk genes. Pearson\u0026rsquo;s correlation analysis between the z-score values of FDA-approved drugs and risk gene expression was used to screen the results for therapeutic agents inducing sensitivity in BRCA. The z-score was positively correlated with drug sensitivity. The results with Pearson\u0026rsquo;s correlation coefficient | \u0026gt;0.3 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were used.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCell culture\u003c/h2\u003e \u003cp\u003eThe American Type Culture Collection (ATCC) provided MCF-7 cells cultured in Dulbecco's Modified Eagle Medium (DMEM) (ATCC, USA). The media were supplemented with 10% fetal bovine serum (FBS, Thermo, USA), penicillin (100 U/mL), and streptomycin (100 mg/mL) (Thermo). The media were then incubated at 37\u0026ordm;C with 5% carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e) in a humidified incubator. Fresh medium was added 2\u0026ndash;3 times a week, 0.25% Trypsin-EDTA (Gibco, USA) was added at room temperature for 1\u0026ndash;2 min, and the cells were passaged at 1:2 and 1:3 dilutions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCell Counting Kit-8 (CCK8) assay\u003c/h2\u003e \u003cp\u003eIn 96-well plates, a total of 5 \u0026times; 10\u003csup\u003e3\u003c/sup\u003e cells were seeded. After 24 h of cell culture, 10 \u0026micro;L of CCK-8 solution (Biosharp, USA, Cat. BS350B) was added to the plates. Cells were cultured at 37℃ for 1 h with 5% CO\u003csub\u003e2\u003c/sub\u003e. The proliferative activity was then assessed by measuring the absorbance value at 450 nm using an enzyme marker. The experiments were repeated three times.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eColony formation assay\u003c/h2\u003e \u003cp\u003eIn DMEM medium supplemented with 10% FBS. One hundred cells were seeded in 6-well plates and incubated at 37\u0026deg;C in a cell culture incubator. Evert 7 days, the culture medium was changed. Following 20 days of stimulation with afatinib and quercetin at different concentrations, the cells were fixed in 100% methanol for 30 min. Then, staining with 0.2% crystal violet at room temperature for 15 min. The colonies were observed and counted under a light microscope after the cells had been stained and had undergone three phosphate-buffered saline washes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using R v4.2.1. The applicability of the model was assessed through ROC curve analysis. The difference in survival between the two risk groups was compared using KM curves. The prognostic value of risk scores was evaluated using univariate Cox analysis, deriving hazard ratios (HR) and 95% confidence intervals for each variable. GraphPad was used to analyze the assay results. \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCuproptosis-associated subtypes and DEGs of BRCA\u003c/h2\u003e \u003cp\u003eCuproptosis is important for BRCA. Based on the expression matrix of 13 genes associated with cuproptosis, 327 patients with BRCA samples from GEO were classified into k groups (k\u0026thinsp;=\u0026thinsp;2\u0026ndash;9) with different cuproptosis levels. When k\u0026thinsp;=\u0026thinsp;2, we can classify objects more effectively (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA, B). We then compared the overall survival rates of the two clusters using KM analysis and discovered that group 1 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0052) had significantly better survival than group 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). We then performed differential analysis on the two clusters using the limma package and identified 150 upregulated and 284 downregulated differential genes (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, D and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC). We also performed a GSEA analysis on these differential genes and discovered that they were primarily enriched in the cell proliferation pathway (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE, F and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003eS1\u003c/span\u003eD, E).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eConstruction and verification of the cuproptosis-associated prognostic signature\u003c/h2\u003e \u003cp\u003eThen, using one-way Cox analysis to screen 1080 BRCA oncogenes (HR\u0026thinsp;\u0026gt;\u0026thinsp;1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), we discovered 25 candidate genes by overlapping them with genes that were upregulated in group cuproptosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). On these genes, LASSO regression analysis was performed to eliminate the effect of false positives. Data from GSE20685 and TCGA-BRCA were used as the training and validation sets, respectively. Twenty genes were identified in the training set to calculate the risk coefficient: Risk score\u0026thinsp;=\u0026thinsp;0.0980 \u0026times; Expression (\u003cem\u003eADAM9\u003c/em\u003e)\u0026thinsp;\u0026minus;\u0026thinsp;0.1232 \u0026times; Expression (\u003cem\u003eOCRL\u003c/em\u003e)\u0026thinsp;+\u0026thinsp;0.0982 \u0026times; Expression (\u003cem\u003eSTK3\u003c/em\u003e)\u0026thinsp;\u0026minus;\u0026thinsp;0.0970 \u0026times; Expression (\u003cem\u003eSLC6A1\u003c/em\u003e)\u0026thinsp;+\u0026thinsp;0.0452 \u0026times; Expression (\u003cem\u003eLMO1\u003c/em\u003e)\u0026thinsp;+\u0026thinsp;0.1874 \u0026times; Expression (\u003cem\u003eTMED9\u003c/em\u003e)\u0026thinsp;+\u0026thinsp;0.0783 \u0026times; Expression (\u003cem\u003eTUBA1C\u003c/em\u003e)\u0026thinsp;+\u0026thinsp;0.0136 \u0026times; Expression (\u003cem\u003eBRIX1\u003c/em\u003e)\u0026thinsp;+\u0026thinsp;0.0180 \u0026times; Expression (\u003cem\u003eGRHL2\u003c/em\u003e)\u0026thinsp;+\u0026thinsp;0.1080 \u0026times; Expression (\u003cem\u003eLONRF3\u003c/em\u003e)\u0026thinsp;+\u0026thinsp;0.1986 \u0026times; Expression (\u003cem\u003eTMC7\u003c/em\u003e)\u0026thinsp;+\u0026thinsp;0.0186 \u0026times; Expression (\u003cem\u003eADAMTS7\u003c/em\u003e)\u0026thinsp;+\u0026thinsp;0.0588 \u0026times; Expression (\u003cem\u003eARMC1\u003c/em\u003e)\u0026thinsp;+\u0026thinsp;0.1606 \u0026times; Expression (\u003cem\u003eKCTD15\u003c/em\u003e)\u0026thinsp;+\u0026thinsp;0.2387 \u0026times; Expression (\u003cem\u003eZFHX3\u003c/em\u003e)\u0026thinsp;+\u0026thinsp;0.0045 \u0026times; Expression (\u003cem\u003eIYD\u003c/em\u003e)\u0026thinsp;+\u0026thinsp;0.0628 \u0026times; Expression (\u003cem\u003eCASP14\u003c/em\u003e)\u0026thinsp;+\u0026thinsp;0.0232 \u0026times; Expression (\u003cem\u003ePRSS27\u003c/em\u003e)\u0026thinsp;+\u0026thinsp;0.3511 \u0026times; Expression (\u003cem\u003eTMEM65\u003c/em\u003e)\u0026thinsp;+\u0026thinsp;0.0838 \u0026times; Expression (\u003cem\u003eQPRT\u003c/em\u003e) (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eB-D). The AUC value in the ROC curve was 0.803 over one year (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eF), and the threshold values for the high-risk and low-risk groups were set to the median (Figures S2A, B). The difference in survival between the high-risk and low-risk groups in the training set was also more significant (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003eE and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003eS2\u003c/span\u003eC). These findings were validated in the validation set (Figure S3A-E).\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eMulti-omics expression of risk score-related genes in BRCA\u003c/h2\u003e \u003cp\u003eWe performed a multi-omics analysis to investigate the expression of risk score genes in BRCA tumors. First, mutations were created on the \u003cem\u003eZFHX3\u003c/em\u003e, \u003cem\u003eGRHL2\u003c/em\u003e, \u003cem\u003eADAMTS7\u003c/em\u003e, \u003cem\u003eOCRL\u003c/em\u003e, \u003cem\u003eTUBA1C\u003c/em\u003e and \u003cem\u003eLONRF3\u003c/em\u003e DNA sequences at the DNA level (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). However, TMED9, TUBA1C, and CASP14 nucleic acid and protein levels were found to be significantly upregulated in BRCA tumors by Genotype-Tissue Expression database and HPA database analyses (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, C). \u003cem\u003eTMC7\u003c/em\u003e, on the other hand, was only upregulated at the protein level. In contrast, \u003cem\u003eZFHX3\u003c/em\u003e, \u003cem\u003eIYD\u003c/em\u003e, and \u003cem\u003eSLC6A1\u003c/em\u003e were only upregulated at the nucleic acid level. The data above demonstrated that most risk-score genes had high DNA, RNA, or protein expression levels.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eImmune cell infiltration in patients with BRCA in the high-risk and low-risk groups\u003c/h2\u003e \u003cp\u003eThe percentage of various immune cell types (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-D and \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003eS4\u003c/span\u003eA-D) and the correlation of various immune cells (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eE and \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003eS4\u003c/span\u003eE) were examined in the high-risk and low-risk groups and in the BRCA tumor versus paraneoplastic groups, respectively to observe the correlation between our prognostic model and the immune microenvironment in BRCA tumors. Significant differences in the level of na\u0026iuml;ve B cells, macrophages M2 cells, CD4 memory resting cells, and monocytes cells were found between the BRCA tumor group and the paracancer group when compared (Figure S4F). Additionally, the high-risk group had considerably more immunological infiltration of macrophages than the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eF). According to the results mentioned above, there is a significant change in the immune microenvironment during BRCA tumorigenesis, and this change has some correlation with the risk score predicted by the prognostic model used in this study.\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eExploration of drug sensitivity based on the findings of the prognostic model\u003c/h2\u003e \u003cp\u003eThe prognostic model has been extensively used and validated as a diagnostic tool, but whether it can be used therapeutically to produce antitumor effects is unknown. As a result, we need to investigate anticancer drugs that target this model. As the risk score increased, 228 drugs were identified to be significantly associated with the signature gene (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, |cor| \u0026gt;0.3). These drugs included barasertib, AZD-9496, XAV-939, etc. (Figs.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e6\u003c/span\u003eA and \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). It suggests that these drugs may target genes on risk score and that their sensitivity is related to the risk score.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eMolecular docking of compounds and targets\u003c/h2\u003e \u003cp\u003eWe investigated whether the component of curcuma, a chemical formulation with anti-BRCA properties, targets oncogenes to achieve a combination of Chinese and Western medicines for treating BRCA tumors. The 358 target genes corresponding to the three active ingredients of turmeric were discovered using the TCMSP database (Figs.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e7\u003c/span\u003eA, B). Stigmasterol targets the oncogene \u003cem\u003eADAM9\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). Molecular docking with stigmasterol vs. \u003cem\u003eADAM9\u003c/em\u003e score\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;8.6 kcal/mol was used to mimic the binding capacity between these active ingredients and the discovered targets and the active compounds (Figs.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e7\u003c/span\u003eC, D and \u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003eS6\u003c/span\u003e). According to molecular docking results, the capacity of these active ingredients and target proteins to bind varies. We focused on the drug stigmasterol for further research since it has the highest binding energy to the \u003cem\u003eADAM9\u003c/em\u003e molecule and binds the most target genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eTurmeric combined with barasertib has a synergistic antitumor effect on BRCA\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eWe selected the top 3 drugs with cor values based on Cellminer results, and we determined the IC50 using the CCK8 assay in MCF-7 cells, respectively (Figs.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e8\u003c/span\u003eA and \u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003eS7\u003c/span\u003e). We selected barasertib drugs for subsequent combination assays based on the IC50 and cor values. We combined stigmasterol and barasertib to treat MCF-7 cells to investigate the inhibitory effect of combined Chinese and Western treatments on BRCA. Stigmasterol and barasertib worked better together than alone to lower MCF-7 cell viability (Figs.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e8\u003c/span\u003eB, C). The results of the CCK8 assay and the clone formation assay were the same as above (Figs.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e8\u003c/span\u003eD-E).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eBRCA is a common malignant tumor distinguished by its aggressive nature and poor prognosis. The medical community is still looking toward novel and effective methods for diagnosis and treatment. Cuproptosis, a type of regulated cell death, can potentially induce tumor cells, especially those in BRCA, to die. Cuproptosis is a method for diagnosing and treating BRCA, although its precise targets and techniques have not yet been fully elucidated. This study uses LASSO to create a predictive model for CRGs in BRCA to address this knowledge gap. A subsequent CIBERSORT analysis was conducted to assess the prognostic potential of the developed model, and the results showed a correlation between this model and immune infiltration in BRCA. This study suggests that the immune system plays a significant role in initiating cuproptosis and its effects on the tumor microenvironment. The synergistic effect of stigmasterol and barasertib on BRCA cells was another exciting study discovery. These findings suggest a novel combinational treatment strategy for BRCA.\u003c/p\u003e \u003cp\u003eCuproptosis is a recently discovered programmed cell death. The association between cuproptosis and tumors has been investigated, and the associations between CRGs and various tumor characteristics across common cancer types have been investigated. Cuproptosis has been associated with c-Myc-mediated BRCA and is dysregulated in BRCA tissues [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. A prognostic cuproptosis-related signature in BRCA has also been identified through studies, and it not only predicts patient outcomes but also sheds light on the immune microenvironment. According to these findings, cuproptosis may represent a novel target for diagnosis and treatment [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. We conducted difference analysis and Cox unifactor analysis for the two patient groups, trained the obtained genes through LASSO, and obtained the calculation formula for risk score, which was used as a prognosis prediction model for patients with cuproptosis. The cluster analysis was based on the expression levels of 13 CRGs in BRCA. ADAM9 is a member of the ADAM family of proteins, which is involved in several physiological and pathological processes, including the progression of cancer. According to studies, \u003cem\u003eADAM9\u003c/em\u003e is overexpressed and associated with higher tumor development grade, positive lymph node status, and distant metastasis in triple-negative BRCA (TNBRCA) compared to non-TNBRCA. The motility, invasion, and proliferation of cancer cells are all promoted by \u003cem\u003eADAM9\u003c/em\u003e. Recent research has demonstrated that the gene signatures associated with cuproptosis in lower-grade gliomas contain \u003cem\u003eADAM9\u003c/em\u003e. Here, we performed a KM survival analysis and calculated the AUC value in the training and validation groups, respectively. This proved that the risk score of our prediction model was in high agreement with the actual prognosis of patients. Additionally, we used several methods to verify the practicability and reliability of the model in the training and validation groups.\u003c/p\u003e \u003cp\u003eThe tumor immune microenvironment (TME), which comprises complex interactions between cellular and non-cellular elements, plays a crucial role in the advancement and progression of cancer [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Cancer cells, immune cells, stromal tissue, and extracellular matrix are all parts of the TME [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The immune system has a significant impact on the TME, and the condition of the immune system within the TME is essential for the prevention, development, and progression of tumors [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Significant correlations have been found between the immunological constituents of the TME and tumor development, recurrence, and metastasis [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Tumor progression and therapeutic response in BRCA are contacted by the TME [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The balance between pro- and anti-tumor immune responses determines whether the TME can promote and inhibit tumor growth. [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Understanding the TME and its role in tumor development and progression is crucial for immune-targeted cancer therapies to be as effective as possible. This study revealed significant variations in the infiltration of B cells, macrophages, NK cells, and T cells between cancer and normal groups and between the high-risk and low-risk groups. These investigations suggest that our prognostic model determines the clinical prognosis and, to a certain extent, the immune infiltration of tumor cells.\u003c/p\u003e \u003cp\u003eTargeted therapy, a type of cancer treatment, uses drugs to specifically target cancer growth and survival by focusing on certain genes, proteins, or the tissue environment involved [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Cancer cells can be targeted using various targeted drugs, including monoclonal antibodies, hormone therapy, and targeted therapy [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Trastuzumab, pertuzumab, trastuzumab deruxtecan, and other drugs are specific examples of targeted therapies used to treat BRCA. [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Targeted therapy does have some limitations, including the development of drug resistance, side effects, and expensive treatment. Additionally, only some types of BRCA, such as HER2-positive and hormone receptor-positive BRCA, respond to targeted therapy [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. It is important to create novel targeted therapies to overcome these limitations and improve the effectiveness of BRCA treatment. A phytosterol exhibits anticancer properties against several cancer cell lines through multiple mechanisms. These mechanisms include inhibiting tumor cell proliferation, reducing tumor angiogenesis, and inducing cancer cell apoptosis. In human gastric cancer cells, stigmasterol effectively inhibits cell migration, induces cell cycle arrest, triggers mitochondrial-mediated apoptosis, and impedes the JAK/STAT signaling pathway, resulting in strong antitumor effects [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The potential clinical utility of phytosterols in cancer treatment is hampered by the lack of standardization and their limited pharmacological action. Conversely, leukemia, colorectal cancer, and BRCA all exhibit a dose-dependent reduction of cell proliferation \u003cem\u003ein vivo\u003c/em\u003e when treated with the Aurora kinase B inhibitor barasertib. Barasertib has become well-known as a therapeutic drug after extensive research in numerous tumor types. However, drug toxicity affected approximately 25% of patients with solid tumor, with neutropenia being the most prevalent dose-limiting toxicity [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Additional research is required to determine the ideal dosage, effectiveness, and safety of barasertib in cancer therapy. Analysis of the sensitivity of many regularly used chemotherapeutic drugs revealed a positive correlation between risk score-related genes and sensitivity. Notably, the risk score showed a strong association with barasertib, suggesting that it may be an effective drug for treating the cuproptosis prognosis model. Additionally, a prognostic model using molecular docking and network pharmacology revealed that stigmasterol, a compound present in turmeric, targets the ADAM9 protein. Therefore, we selected stigmasterol, which had the strongest binding ability, and barasertib, the most sensitive drug, for combined use \u003cem\u003ein vitro\u003c/em\u003e. The results revealed that the two drugs synergically inhibited BRCA cells. Our study identified a novel and reliable strategy for treating BRCA and a solid theoretical foundation for combining traditional Chinese medicine and Western medicine.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u003c/strong\u003e\u003cstrong\u003e\u0026rsquo;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;contributions\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors contributed substantially to this manuscript.\u0026nbsp;Yongqin\u0026nbsp;wang\u0026nbsp;and\u0026nbsp;Yuxiao Ma\u0026nbsp;performed the majority of experiments and data analysis.\u0026nbsp;Yongqin\u0026nbsp;wang\u0026nbsp;contributed to carrying out the experiments and interpreted the results. Yuxiao Ma\u0026nbsp;contributed to wrote the manuscript. Junyi Tan revised the manuscript and designed and conducted the project. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD\u003c/strong\u003e\u003cstrong\u003eata Availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e The authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e All the authors agreed on the publication of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOpen Access\u003c/strong\u003e This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. 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Breast Cancer: Targets and Therapy, : p. 53\u0026ndash;69\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi K et al (2018) Stigmasterol exhibits potent antitumor effects in human gastric cancer cells mediated via inhibition of cell migration, cell cycle arrest, mitochondrial mediated apoptosis and inhibition of JAK/STAT signalling pathway. J BUON 23(5):1420\u0026ndash;1425\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohnson ML et al (2023) Safety, tolerability, and pharmacokinetics of Aurora kinase B inhibitor AZD2811: a phase 1 dose-finding study in patients with advanced solid tumours. Br J Cancer 128(10):1906\u0026ndash;1915\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Breast cancer, cuproptosis, synergistic therapeutic, malignant development","lastPublishedDoi":"10.21203/rs.3.rs-3341565/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3341565/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eBreast cancer (BRCA) has a high incidence and a poor prognosis. Cuproptosis is a crucial regulator of carcinogenesis and tumor progression. However, it has not been reported how cuproptosis in BRCA patients is treated using Chinese and Western medicines.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study investigated how cuproptosis is used to diagnose and treat BRCA. A cuproptosis prognostic model was constructed using a bioinformatics approach. We used LASSO to establish a prognostic model associated with cuproptosis, and demonstrated the reliability of the model with survival analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCIBERSORT analysis showed that the prognostic model was associated with immune infiltration. An interesting finding from the CellMiner database analysis revealed a high correlation between the risk score and Barasertib. According to network pharmacology and molecular docking analysis, stigmasterol, an active ingredient of \u003cem\u003eCurcuma longa\u003c/em\u003e L., may target the gene \u003cem\u003eADAM9\u003c/em\u003e in the prognostic model. The combination of drugs confirmed that stigmasterol and barasertib had a significant synergistic effect on BRCA cells.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur study provides a potential strategy for treating cuproptosis in combination with Chinese and Western medicines for BRCA.\u003c/p\u003e","manuscriptTitle":"Stigmasterol and barasertib target cuproptosis-related prognostic model for the synergistic treatment of breast cancer.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-09-14 17:40:01","doi":"10.21203/rs.3.rs-3341565/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f63e74ba-31b4-4abd-aa67-252a70f361c5","owner":[],"postedDate":"September 14th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-10-26T17:59:17+00:00","versionOfRecord":[],"versionCreatedAt":"2023-09-14 17:40:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3341565","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3341565","identity":"rs-3341565","version":["v1"]},"buildId":"GqpaHPwrfC8PjnIFayRh5","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}