Abstract

   Breast cancer is the second leading cancer type in women, accounting
   for 11.6% of all cancers. Recently, its incidence has increased in
   younger individuals. Estrogen receptors (ERs) play important roles in
   the development and progression of breast cancer by controlling hormone
   signaling. Therefore, targeting ERs is one of the most promising
   therapeutic strategies for treating ER-positive breast cancer. High
   heterogeneity contributes to the tumorigenicity, metastatic ability,
   and therapeutic resistance of breast cancer. However, drug discovery
   studies at the single-cell level are lacking. In the present study, we
   used single-cell sequencing data analysis, together with a network
   pharmacology approach, to determine the targets and molecular
   mechanisms of formononetin in ER-positive breast cancer. Comparative
   single-cell sequencing analysis identified 3899, 3395, 333, 398, 398,
   and 17 differentially expressed genes in stromal cells, epithelial
   cells, fibroblasts, neutrophils, eosinophils, and macrophages,
   respectively, of ER-positive breast cancer compared with normal breast
   tissues. Further network pharmacology analysis highlighted the
   importance of formononetin targets in biological functions and
   signaling pathways related to immune and inflammatory responses,
   metastatic ability, metabolism, cell proliferation, and gland
   development in different ER-positive breast cancer cell types. For the
   first time, we used a systems biology approach to investigate the
   targets of formononetin and its anti-ER-positive breast cancer
   mechanisms at the single-cell level.

   Keywords: Network pharmacology, Formononetin, Estrogen receptor,
   ER-positive breast cancer, Single cell

   Subject terms: Medical research, Molecular medicine, Cancer

Introduction

   According to the 2022 Global Cancer Statistics, female breast cancer is
   the second leading disease in terms of incidence rate, accounting for
   11.6% of all cancers^[32]1. Breast cancer has become a major global
   public health challenge and is gradually affecting younger individuals.
   Its incidence among young people in Japan has increased annually^[33]2.
   Breast cancer can be pathologically classified into ductal and lobular
   carcinomas with cancerous lesions occurring in the ducts and lobules,
   respectively^[34]3. Breast cancer can be caused by multiple factors,
   such as heredity, environmental exposure, individual differences, and
   lifestyle^[35]4. Among these, hormone receptors, including estrogen
   receptor (ER), progesterone receptor (PR), and human epidermal growth
   factor receptor 2 (HER2), are routine indicators of breast cancer
   treatment and prognostic markers because they are expressed in the vast
   majority of breast cancers^[36]5. ER and PR positivity (ER +/PR +)
   accounts for two-thirds of all breast cancers^[37]6, and these tumors
   express either ERs or PRs^[38]7. Because ER expression is closely
   related to the proliferation of breast cancer cells, the use of hormone
   therapy to target ERs has become one of the major treatments for
   ER-positive breast cancers^[39]6. Clinically, breast cancer
   heterogeneity is associated with the expression of different hormone
   receptors, which provides different options for breast cancer
   treatment^[40]8. Conventional treatments for breast cancer are not
   limited to surgical excision, radiotherapy, chemotherapy, and endocrine
   therapy^[41]9. Although these methods have improved the effectiveness
   of treatment to a certain extent, they have several limitations. For
   example, endocrine therapy for breast cancer is associated with a risk
   of drug resistance and recurrence^[42]10. Radiotherapy may cause
   adverse effects in normal tissues and organs. Furthermore, radiotherapy
   for breast cancer increases the risk of heart disease and myocardial
   infarction^[43]11. Therefore, an in-depth understanding of the role of
   ER in breast cancer tumorigenicity is crucial for the development of
   effective treatment strategies. With the development of biomedical
   technology, numerous studies have revealed the complex mechanisms
   underlying breast cancer development, including gene mutations,
   epigenetic regulation, and signaling pathway abnormalities. Therefore,
   breast cancer treatment needs to be constantly innovated and explored
   to develop new therapeutic approaches that improve treatment efficacy
   and reduce side effects. Network pharmacology is a network-based
   research approach used to analyze systems biology that can reveal
   complex network relationships between drugs and organisms. Traditional
   single-target approaches tend to focus only on the effects of drugs on
   a single target and ignore the complex effects of drugs on an organism.
   Network pharmacology can be used to understand multi-pathway and
   multi-target mechanisms of action in organisms by constructing
   drug–target–disease interaction networks for studying many complex
   diseases^[44]12–[45]15. In addition, tumor development is no longer
   considered a single-factor-induced disease^[46]16. Drug resistance,
   which is a major challenge in breast cancer treatment, is associated
   with tumor heterogeneity. The development of single-cell RNA sequencing
   has recently played a great advantage in the analysis of tumor
   heterogeneity and has allowed scientists to analyze the genetic
   profiles of drug-resistant cells to determine the mechanism of cellular
   resistance acquisition^[47]17. Various studies have indicated that
   breast cancer is highly heterogeneous and has different molecular
   subtypes^[48]18,[49]19. The molecular drivers and heterogeneity of
   breast cancer can be precisely studied using single-cell sequencing
   technology. Formononetin, a natural phytoestrogen, can be extracted
   from many plants, such as soybeans and red clover^[50]20. It has a
   promising potential for clinical applications, especially in cancer
   treatment^[51]21. Its chemical structure is similar to that of
   estrogen, making it a potential drug for treating ER-positive breast
   cancer. Moreover, formononetin has anti-inflammatory functions, such as
   the modulation of mast cells and basophils through the activation of
   the JAK/STAT/PI3-Akt pathways^[52]22, suggesting a possible immune
   activation response. In the present study, we applied network
   pharmacology together with single-cell sequencing data of ER-positive
   breast cancer obtained from a previous report to determine the targets
   of formononetin for treating breast cancer at the single-cell
   level^[53]23. Following bioinformatics analysis, we further highlighted
   the mechanisms underlying the anti-breast cancer effects of
   formononetin.

Materials and methods

Source and analysis of single cell sequencing data of ER-positive breast
cancer

   To determine the differential gene expression in ER-positive breast
   cancer at the single-cell level, the keywords “ER-positive breast
   cancer” and “single cell” were used to search in the Gene Expression
   Omnibus (GEO) database ([54]https://www.ncbi.nlm.nih.gov/geo/). The
   single-cell sequencing dataset [55]GSE245601 was obtained^[56]23. The
   sequencing data were analyzed using the tools “Seurat,” “cowplot,”
   “BiocManager,” “SingleR,” “dplyr,” “tidyverse,” and “patchwork” of R
   packages. The “Seurat” tool was used for QC, the included data criteria
   including the number of genes in a cell should be > 200, and the genes
   should be expressed in at least 10 cells. The PercentageFeatureSet tool
   was used to calculate the percentage of mitochondria-related genes in
   each sample. The cell with 500 < gene < 3000 and with a percentage of
   mitochondria-related genes < 15% were used for further analysis. A
   total of 2000 genes with highly variable coefficients between cells
   were extracted, and the data were analyzed using canonical correlation
   analysis to eliminate batch effects between different groups of data.
   After principal component analysis and uniform manifold approximation
   and projection (Umap) cluster analysis, the cells were divided into
   different sub-clusters. Cell types for each cluster were annotated
   using the SingleR automated annotation package. After grouping the
   data, the data from two normal breast tissues (named “N”) and the data
   from 10 ER-positive breast cancerous tissues (named “T”) was subjected
   to the FindMarkers function to determine differentially expressed genes
   (DEGs) between cancerous tissues and normal tissues of the same cell
   type, and genes with p < 0.05 and |log2 FoldChange|> 1 were defined as
   DEGs.

Harvesting formononetin’s targets against ER-positive breast cancer at the
single-cell level

   The targeted genes of formononetin were identified by scanning online
   databases, including the Swiss Target Prediction^[57]24, PharmMapper,
   and SuperPred. The targets obtained were corrected using the UniProtKB
   database. The harvested genes were compared with DEGs obtained from
   single-cell sequencing data of ER-positive breast cancer cells to
   determine the targets of formononetin in ER-positive cancer.

Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway
enrichment analysis

   The “ClusterProfiler” package of the R language was used for
   biological-term classification and the enrichment analysis of gene
   clusters, and the R package “GOplot” was used to visualize the
   expression data with functional analysis. GO terms and pathways were
   considered statistically significant at p < 0.05.

Results

DEGs in ER-positive breast cancer at the single-cell level

   To determine differential gene expression in ER-positive breast cancer
   at the single-cell level, we downloaded single-cell sequencing data
   containing two normal breast tissues and 10 ER-positive breast
   cancerous tissues from the GEO database^[58]23. We then determined the
   number of expressed genes, sum of gene expression, and percentage of
   mitochondria-related genes per cell (Supplementary Fig. [59]1). Using
   Umap cluster analysis and SingleR automatic annotation package, we
   identified 24 cell clusters in normal and ER-positive breast cancer
   tissues (Fig. [60]1A) belonging to six distinct cell types, including
   stromal cells, epithelial cells, fibroblasts, neutrophils, eosinophils,
   and macrophages (Fig. [61]1B). When we compared the gene expression
   profiles of normal breast tissues and ER-positive breast cancer
   tissues, we identified 3899 DEGs in stromal cells, 3395 DEGs in
   epithelial cells, 333 DEGs in fibroblasts, 398 DEGs in neutrophils, 398
   DEGs in eosinophils, and 17 DEGs in macrophages.

Fig. 1.

   [62]Fig. 1
   [63]Open in a new tab

   Comparative single cell sequencing analysis of estrogen receptor
   (ER)-positive breast cancer. (A) Uniform manifold approximation and
   projection (Umap) classified the single cell of ER-positive breast
   cancer tissues and normal breast tissues into 24 cell clusters. (B) The
   clusters were classified into stromal cells, epithelial cells,
   neutrophils, fibroblasts, eosinophils, and macrophages using the
   SingleR automatic annotation package.

Formononetin’s targets against ER-positive breast cancer tissues at the
single-cell level

   By searching online databases, followed by correcting using the
   UniProtKB database, 402 formononetin-related genes were identified.
   When we overlaid the formononetin-associated genes and DEGs in each
   ER-positive breast cancer cell type, we identified the target genes of
   formononetin in ER-positive breast cancer at the single-cell level
   (Fig. [64]2).

Fig. 2.

   [65]Fig. 2
   [66]Open in a new tab

   Network pharmacology analysis identified formononetin’s targets against
   estrogen receptor-positive breast cancer in single-cell level. Venn
   diagram showed the number of overlapped genes in stromal cells,
   epithelial cells, neutrophils, fibroblasts, eosinophils, and
   macrophages.

Formononetin targeted the genes related to immune and inflammatory responses
in immune cells of ER-positive breast cancer

   GO enrichment analysis of the biological processes of immune cells
   revealed that formononetin targeted genes in response to external
   stimuli and defense responses, including the response to wounds and
   blood coagulation (Fig. [67]3A). In addition, formononetin targeted
   genes that contribute to different stresses, including oxidative and
   chemical stresses (Fig. [68]3A). This triggered immune and inflammatory
   responses, such as endocytosis and phagocytosis (Fig. [69]3B).
   Additionally, the targets of formononetin were involved in the
   regulation of cell signal transduction (Fig. [70]3B). In the GO
   enrichment analysis of molecular functions, formononetin regulated many
   enzyme activities, such as protein kinase and phosphatase activities
   (Fig. [71]3C). Moreover, the KEGG pathway analysis suggested the
   involvement of formononetin in mitogen activated protein kinase (MAPK),
   5′ adenosine monophosphate-activated protein kinase (AMPK), and Rap1
   signaling (Fig. [72]3D). More importantly, formononetin targets
   contributed to transcriptional dysregulation in cancer cells
   (Fig. [73]3D).

Fig. 3.

   [74]Fig. 3

   [75]Fig. 3
   [76]Open in a new tab

   Functions of formononetin’s targets in immune cells of estrogen
   receptor-positive breast cancer. Gene ontology (GO) enrichment analysis
   highlighted the biological roles and molecular functions of
   formononetin’s targets in (A) in response to wound healing and blood
   coagulation, (B) immune and inflammatory responses and cell signaling,
   (C) protein kinase and phosphatase activities. (D) Kyoto Encyclopedia
   of Genes and Genomes pathway enrichment analysis further highlighted
   the roles of formononetin’s targets in MAPK signaling, AMPK signaling,
   and Rap1 signaling; and transcriptional dysregulation in cancer. The
   bubble’s size represents the number of involved gene. The bubble’s
   color represents the significance of the processes and pathways.

Formononetin mediated stress response, metastasis ability, and proliferation
of the epithelial cells in ER-positive breast cancer

   The GO analysis of the epithelial cells of ER-positive breast cancer
   revealed that formononetin targeted genes involved in stress responses,
   such as cell death in response to oxidative stress and cellular
   response to chemical stress, through activated MAPK cascade and protein
   kinase signaling cascade (Fig. [77]4A). In addition, formononetin
   targets contributed to apoptosis by regulating the intrinsic apoptotic
   signaling pathway (Fig. [78]4A). More importantly, formononetin
   regulated the expression of genes involved in cancer metastasis,
   including those involved in wound healing and cell migration
   (Fig. [79]4B). Formononetin targets also played an important role in
   cell proliferation and regulation of tumor necrosis factor production
   (Fig. [80]4C). In the KEGG pathway analysis, formononetin could target
   the genes involved in breast cancer development including epidermal
   growth factor receptor (EGFR), ESR1, KIT, FGFR1, CDK6, insulin-like
   growth factor 1 (IGF1), and HRas proto-oncogene (HRAS). In addition,
   formononetin’s targets controlled numerous cell signaling pathways
   contributed to the breast carcinogenesis, such as Ras, MAPK, Relaxin,
   IL-17, TNF, Rap1, estrogen, phospholipase D, PI3K–Akt, HIF-1, FoxO,
   GnRH, NF-kappa B, and JAK–STAT signaling pathways (Fig. [81]4D).

Fig. 4.

   [82]Fig. 4

   [83]Fig. 4

   [84]Fig. 4

   [85]Fig. 4
   [86]Open in a new tab

   Functions of formononetin’s targets in epithelial cells of estrogen
   receptor-positive breast cancer. Gene ontology enrichment analysis
   showed the biological roles of formononetin’s targets in (A) stress
   responses and cell apoptosis, (B) cancer metastasis, such as wound
   healing and cell migration, and (C) cell proliferation and regulation
   of tumor necrosis factor production. (D) Kyoto Encyclopedia of Genes
   and Genomes pathway enrichment analysis further highlighted the roles
   of formononetin’s targets in cell signaling of breast cancer
   development. The bubble’s size represents the number of involved genes.
   The bubble’s color represents the significance of the processes and
   pathways.

Formononetin mediated cellular response to estrogen stimulus and gland
development through targeting the stromal cells of ER-positive breast cancer

   We focused on the effects of formononetin on stromal cells in
   ER-positive breast cancer. The results of the GO enrichment analysis
   highlighted the importance of formononetin targets in biological
   processes related to stress-associated cell death, especially apoptosis
   (Fig. [87]5A). In addition, formononetin targeted the stromal cell
   genes involved in cell proliferation by regulating the cell cycle
   (Fig. [88]5B). More importantly, formononetin targets played important
   roles in response to estrogen, mammary gland development, and
   morphogenesis (Fig. [89]5C). In addition, the targets of formononetin
   contributed to the regulation of angiogenesis, cell migration, and
   wound healing, which are closely associated with cancer metastasis
   (Fig. [90]5D). In KEGG pathway enrichment analysis, our results further
   highlighted the importance of formononetin in breast carcinogenesis by
   targeting EGFR, ESR1, phosphoinositide-3-kinase regulatory subunit 1
   (PIK3R1), PGR, CDK6, and IGF1. In addition, formononetin targets were
   involved in many important signaling pathways closely associated with
   breast cancer tumorigenicity, especially estrogen, PI3K–Akt, insulin,
   MAPK, JAK–STAT, FoxO, VEGF, TNF, and p53 signaling (Fig. [91]5E).

Fig. 5.

   [92]Fig. 5

   [93]Fig. 5

   [94]Fig. 5

   [95]Fig. 5
   [96]Open in a new tab

   Functions of formononetin’s targets in stromal cells of estrogen
   receptor-positive breast cancer. Gene ontology enrichment analysis
   showed the biological roles of formononetin’s targets in (A)
   stress-associated cell death, (B) cell proliferation, (C) in response
   to estrogen and mammary gland development and morphogenesis, and (D)
   regulation of angiogenesis, cell migration ability, and wound healing.
   (E) Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis
   further highlighted the roles of formononetin’s targets in breast
   carcinogenicity and cell signaling. The bubble’s size represents the
   number of involved gene. The bubble’s color represents the significance
   of the processes and pathways.

Formononetin targeted NFKB1 and monoamine oxidase B (MAOB) to mediate
metabolisms in fibroblasts of ER-positive breast cancer

   Formononetin only targeted limited gene candidates, including NFKB1 and
   MAOB, to regulate metabolic processes, such as polysaccharide,
   carbohydrate, steroid, and hydrogen peroxide biosynthesis (Fig. [97]6).
   Furthermore, formononetin targeted genes involved in mammary gland
   morphogenesis (Fig. [98]6).

Fig. 6.

   [99]Fig. 6
   [100]Open in a new tab

   Functions of formononetin’s targets in fibroblasts of estrogen
   receptor-positive breast cancer. Gene ontology enrichment analysis
   revealed the biological roles of formononetin targets in metabolism and
   mammary gland morphogenesis. The bubble’s size represents the number of
   genes involved. The bubble’s color represents the significance of the
   processes.

Discussion

   Breast cancer is one of the most common malignant tumors in women
   worldwide and poses a serious threat to their physical and mental
   health^[101]25. ER-positive breast cancer, a major subtype of breast
   cancer, is closely related to ER expression^[102]26. In the present
   study, we used single-cell sequencing analysis and network pharmacology
   to identify the targets of formononetin in ER-positive breast cancer at
   the single-cell level, including immune, epithelial, and stromal cells.

Role of immune cells in ER-positive breast cancer

   With the increase in immunotherapy in recent years, the role of immune
   cells in ER-positive breast cancer has gradually received attention.
   Immune cells recognize and kill ER-positive breast cancer cells. For
   example, T-cells specifically recognize antigens on the surface of
   breast cancer cells and directly kill them by releasing cytotoxins. A
   significant increase in CD(4)(+) and CD(25)(+) regulatory T cells has
   been reported in the peripheral blood of patients with breast
   cancer^[103]27. Natural killer (NK) cells can kill tumor cells by
   exerting a cytotoxic effect^[104]28. Moreover, immune cells play an
   important regulatory role in the breast cancer tumor microenvironment
   (TME), and can be classified into lymphocytes, tumor-associated
   macrophages, and myeloid-derived suppressor cells^[105]29. Tumor
   antagonist cells, such as effector T cells and NK cells, inhibit tumor
   growth and metastasis^[106]30. Meanwhile, tumor-promoting cells, such
   as M2-like macrophages, stimulated by cytokines secreted by type
   2 T-helper cells, such as IL-4, IL-10 and IL-13 as well as transforming
   growth factor-beta, exhibit strong tumor-promoting
   characteristics^[107]29,[108]31 Therefore, formononetin targeting the
   immune cells in ER-positive breast cancer cell could be a promising
   approach for treating breast cancer.

Role of epithelial cells in ER-positive breast cancer

   In addition, formononetin could target epithelial cells in ER-positive
   breast cancer, which not only participates in the proliferation and
   invasion of tumor cells but also influences the prognosis and treatment
   response of patients with breast cancer. In breast tissue, epithelial
   cells are the main cell type that make up the ducts and alveoli of the
   breast, and mammary epithelial cells are mainly classified as either
   basal or luminal cells, which are essential for maintaining the normal
   physiological function of the breast^[109]32. Recent studies have shown
   that changes in the polarity of breast epithelial cells play an
   important role in breast cancer development. During
   epithelial–mesenchymal transition (EMT), the loss of cell apex–base
   polarity, accompanied by the loss of epithelial phenotype and the
   formation of a mesenchymal phenotype, enhances epithelial cell motility
   and acquires the ability to migrate and infiltrate^[110]33. In
   addition, epithelial cells in ER-positive breast cancer provide a
   favorable environment for tumor cell growth through the secretion of
   growth factors and cytokines, thereby promoting the proliferation of
   epithelial and tumor cells. For example, sustained expression of CCL2
   in mammary epithelial cells induces low levels of chronic inflammation,
   thereby increasing the risk of cancer^[111]34. Moreover, the abnormal
   proliferation and differentiation of epithelial cells in breast cancer
   can lead to increased invasiveness of tumor cells. In addition, the
   layer of myoepithelial cells surrounding the ducts of the breast can
   capture cancer cells that intend to escape, preventing them from
   spreading throughout the body^[112]35. However, when the myoepithelial
   cell layer is damaged, cancer cells may break through the defects and
   form invasive ductal carcinoma^[113]36. Moreover, the state of
   epithelial cells may also affect the patient’s sensitivity to systemic
   therapies, such as chemotherapy and targeted therapies, thereby
   affecting the patient’s prognosis.

Role of stromal cells in ER-positive breast cancer

   Stromal cells, an important part of the TME, play a key role in the
   carcinogenesis, progression, and metastasis of ER-positive breast
   cancer^[114]37. In addition to providing support during tumorigenesis,
   stromal cells and their products can alter neighboring tissues by
   inducing phenotypic and genomic changes in epithelial cells^[115]38.
   Moreover, stromal cells may provide supportive functions during breast
   cancer development and promote angiogenesis to facilitate tumor
   metastasis^[116]37. Fibroblasts, as a type of stromal cell, break down
   to mimic traumatic damage to the tissue in invasive breast cancer.
   These fibroblasts become tumor-associated fibroblasts, which provide
   essential nutritional support and growth signals to ER-positive breast
   cancer cells^[117]37. Stromal cells can promote angiogenesis by
   releasing high levels of stromal-derived factor-1, which attracts
   endothelial progenitor cells in the tumor mass and directly promotes
   cancer cell growth by interacting with their receptor CXCR4^[118]39. In
   addition, stromal cells release growth factors, such as epidermal
   growth factor (EGF) and transforming growth factor β (TGF), to create
   an environment conducive to tumor growth^[119]40.

Role of formononetin’s targets EGFR, ESR1, KIT, FGFR1, CDK6, IGF1, HRAS,
PIK3R1, PGR, NFKB1, and MAOB in ER-positive breast cancer

   Network pharmacology analysis identified several formononetin targets
   that have been reported to play important roles in the tumorigenesis of
   ER-positive breast cancer. EGFR, also known as HER1 or ErbB-1, is a
   member of the epidermal growth factor receptor (HER) family. EGFR, with
   its tyrosine kinase activity, is a transmembrane glycoprotein that is
   widely distributed across the cell membranes of various human tissues
   and is particularly highly expressed in various tumor cells^[120]41.
   Moreover, the overexpression of EGFR in primary tumors indicates poor
   prognosis and is inversely correlated with ER in breast cancer^[121]42.
   In addition, the expression level of transforming growth factor alpha,
   a ligand of EGFR, is significantly lower in ER-positive than in
   ER-negative breast cancer^[122]43. ESR1 is a hallmark of ER-positive
   breast cancer. This gene encodes the ER, and its corresponding protein
   product is ERα, whose positive expression implies that the tumor cells
   are sensitive to estrogen, one of the main targets for endocrine
   therapy^[123]44. Increased activity of ERα is positively correlated
   with breast cancer progression, and overactivation of ERα makes
   ER-positive breast cancers more aggressive and refractory^[124]45.

   The proto-oncogene tyrosine–protein kinase kit (KIT), also known as
   proto-oncogene c-Kit, encodes a receptor tyrosine kinase that can
   activate various signaling pathways. KIT phosphorylates PIK3R1, PLCG1,
   SH2B2/APS, and CBL; stimulates major cellular functions, such as cell
   survival, proliferation, differentiation, adhesion, and chemotaxis; and
   induces apoptosis and cancer cell invasiveness^[125]46, which is used
   to differentiate between malignant and benign breast lesions^[126]47.
   Fibroblast growth factor receptor 1 (FGFR1) is a growth factor receptor
   tyrosine kinase that contains extracellular, transmembrane, and
   cytoplasmic structural domains^[127]48. Amplification of the
   chromosomal region of FGFR1 has been detected in approximately 10% of
   human breast cancers and is predominantly present in ER-positive breast
   cancers, where it negatively influences the overall survival of the
   patients with breast cancer^[128]49. In addition, FGFR1 amplification
   contributes to breast carcinogenesis and promotes resistance to
   endocrine therapy^[129]50. Cyclin-dependent kinase 6 (CDK6), also known
   as cell cycle protein-dependent kinase 6, plays an important role in
   cell-cycle progression and regulation. CDK6 binds to D-type cell cycle
   proteins to form an active kinase complex that regulates the
   progression of G1 phase of the cell cycle^[130]51. Early preclinical
   data suggest that breast cancer, particularly ER-positive breast
   cancer, is very sensitive to CDK6 inhibition, and the median
   progression-free survival of patients with breast cancer can be
   significantly prolonged by the administration of CDK4/6 inhibitors such
   as Palbociclib^[131]52.

   IGF1 promotes cell growth and differentiation. The binding of IGF-1 to
   its cognate receptor IGF-1R triggers a signaling cascade, leading to
   cell proliferation and anti-apoptotic effects. Although IGF-1 receptor
   signaling is critical for cancer cell growth, IRS-1 can be
   overexpressed in ER-positive breast cancer^[132]53, and IGF-1 levels
   positively correlate with poor cancer prognosis^[133]54. Moreover,
   IGF-1 receptor inhibitors may accelerate tumor growth through aberrant
   feedback loops in resistant tumors^[134]54. Abnormal feedback loops
   accelerate tumor growth^[135]55. HRAS is an important signal
   transduction protein involved in intracellular signal transduction.
   HRAS mutation results in tumor development^[136]56. Moreover, the RAS
   oncogene family can regulate p63 to induce EMT, leading to an increased
   invasive capacity of breast cancer epithelial cells^[137]57. PIK3R1 is
   a tumor suppressor that is downregulated in many tumor types^[138]58.
   Upregulation of PIK3R1, particularly its p50α isoform, greatly
   activates the PI3K/Akt pathway, which may attenuate resistance to
   chemotherapy in breast cancer^[139]59.

   PRs are important regulators of breast cancer cell growth. PR mutations
   may lead to abnormal progesterone receptor function, thereby increasing
   the risk of breast cancer. Since PRs are an estrogen-responsive gene,
   plasma estradiol levels are associated with the expression levels of PR
   in ER-positive breast cancer^[140]60. In breast cancer, PRs
   synergistically interact with ERs to regulate tumor cell growth and
   differentiation, and PRs are commonly reduced in recurrent breast
   cancer^[141]61. Nuclear factor kappa B subunit 1, an important nuclear
   transcription factor in the cell, is involved in the body’s
   inflammatory and immune responses, and it can regulate apoptosis and
   stress response. NF-kB hyperactivation is associated with many human
   diseases^[142]62. In addition, the typical pathway of NF-κB is
   associated with adriamycin resistance in breast cancer cells^[143]63.
   Additionally, NFKB1 upregulates ETS proto-oncogene 1 expression to
   promote breast cancer cell invasiveness^[144]64. MAOB is involved in
   monoamine neurotransmitters^[145]65. Studies on breast cancer cell
   lines have shown that estrogen-related receptors increase MAOB
   expression, whereas ER decreases MAOB expression, and its expression is
   generally reduced in ER-positive breast cancers^[146]66 Thus,
   formononetin targeting all these important candidates involved in
   tumorigenesis of ER-positive breast cancer may provide us opportunities
   to identify novel approaches for treating breast cancer. More
   importantly, most of these targets are involved in the estrogen
   response.

Role of estrogen response in ER-positive breast cancer

   The estrogen signaling pathway is one of the most prominent signaling
   pathways in ER-positive breast cancer^[147]67. When estrogen binds to
   ER, it undergoes a conformational change to form an ER/estrogen
   complex, which regulates gene transcription and expression, thereby
   promoting breast cancer development and progression^[148]68. Moreover,
   the regulation of breast cancer tumor growth by estrogen may be an
   overmodification of the normal regulatory mechanisms of breast
   epithelial cell proliferation and differentiation. Systematic studies
   using mouse models have demonstrated that estrogen can control the
   growth of ER-positive breast cancers by inducing pituitary synthesis
   and prolactin secretion^[149]69. In addition, estrogen induces
   invasiveness and rearrangement of cytoskeletal and adhesion structures
   in breast cancer cells^[150]70,[151]71. Therefore, endocrine therapy is
   the mainstay of treatment for patients with ER-positive breast cancer,
   aiming to inhibit or block the effects of estrogen^[152]72and delay
   tumor growth. Commonly used drugs for endocrine therapy include
   tamoxifen, toremifene, letrozole, and anastrozole^[153]73. These drugs
   inhibit tumor cell growth by binding to or degrading ER and blocking
   estrogen signaling. Estrogen also plays a crucial role in the
   recurrence, development, and treatment of ER-positive breast cancer. An
   in-depth study of the relationship between estrogen and ER-positive
   breast cancer and its mechanism of action can provide new ideas and
   methods for clinical diagnosis and treatment. Owing to the study
   limitations, the results were mainly obtained using systems biology and
   network pharmacology analyses. These findings need to be validated
   through experimental studies.

Supplementary Information

   [154]Supplementary Information.^ (202.3KB, pdf)

Acknowledgements