Estrogens Determine Adherens Junction Organization and E-Cadherin Clustering in Breast Cancer Cells via Amphiregulin

Cell Painting PLUS: expanding the multiplexing capacity of Cell Painting-based phenotypic profiling using iterative staining-elution cycles

Phenotypic changes in the morphology and internal organization of cells can indicate perturbations in cell functions. Therefore, imaging-based high-throughput phenotypic profiling (HTPP) applications such as Cell Painting (CP) play an important role in basic and translational research, drug discovery, and regulatory toxicology. Here we present the Cell Painting PLUS (CPP) assay, an efficient, robust and broadly applicable approach that further expands the versatility of available HTPP methods and offers additional options for addressing mode-of-action specific research questions. An iterative staining-elution cycle allows multiplexing of at least seven fluorescent dyes that label nine different subcellular compartments and organelles including the plasma membrane, actin cytoskeleton, cytoplasmic RNA, nucleoli, lysosomes, nuclear DNA, endoplasmic reticulum, mitochondria, and Golgi apparatus. In this way, CPP significantly expands the flexibility, customizability, and multiplexing capacity of the original CP method and, importantly, also improves the organelle-specificity and diversity of the phenotypic profiles due to the separate imaging and analysis of single dyes in individual channels.

17β-estradiol inhibits Notch1 activation in murine macrophage cell line RAW 264.7

BACKGROUND

Macrophages are major effectors in regulating immune response and inflammation. The pro-inflammatory phenotype (M1) is induced by the activation of the Toll-like receptor 4 (TLR4) on the macrophage surface, which recognizes lipopolysaccharide (LPS), a component of Gram-negative bacterial wall, and by the binding of interferon-gamma (IFNγ), a cytokine released by activated T lymphocytes, to its receptor (IFNGR). Among the pathways activated by LPS/IFNγ is the Notch pathway, which promotes the M1 phenotype. Conversely, 17β-estradiol (E2) has been shown to blunt LPS-mediated inflammatory response. While it has been shown that E2 regulates the activity of the Notch1 receptor in human endothelial cells, there is no evidence of estrogen-mediated regulation of Notch1 in macrophages.

METHODS AND RESULTS

In this study, RAW 264.7 cells were stimulated with LPS/IFNγ in the presence or absence of E2 and/or N-[N-(3,5-difluorophenacetyl)-L-alanyl]-S-phenylglycine t-butyl ester (DAPT), an inhibitor of γ-secretase, the enzyme involved in Notch activation. The effects of treatment on inducible nitric oxide synthase (iNOS), on components of the Notch pathway, and MAPK (mitogen-activated protein kinase) were assessed by quantitative PCR and Western blotting. We found that E2, through a mechanism involving the inhibition of p38 phosphorylation, reduces the activation of Notch1 induced by LPS/IFNγ. On the contrary, Notch1 exerts a negative control on the estrogen receptor α (ERα) since Notch1 inhibition increases the protein levels of this receptor.

CONCLUSION

In conclusion, we report for the first time a Notch-ERα interaction in macrophages. Our data suggest that E2 may reduce LPS/IFNγ-mediated M1 pro-inflammatory phenotype in macrophages by inhibiting Notch1. This finding encourages further studies on Notch1 inhibitors as novel treatments for inflammation-related diseases.

Identification of key pathways and molecular players potentially involved in endometrial cancer metastasis through integrated bioinformatics analyses

Introduction

Endometrial cancer (EC) is a particularly frequent gynecological cancer, and metastasis is the leading cause of death in patients with EC. Using publicly accessible gene expression data, a bioinformatics study was carried out to increase our knowledge and reveal treatment targets for EC metastasis. This study aimed to identify new important molecular actors and clarify the molecular processes and pathways underlying EC metastasis.

Methods

The GEOexplorer and R programming languages were used to analyze and visualize gene expression data from EC metastatic gene expression datasets, and differentially expressed genes (DEGs) and differentially expressed lncRNAs (DElncRNAs) were identified using bioinformatics with P-value thresholds of < 0.05, and |log2FC| > 1.5. KEGG pathway enrichment analysis and gene ontology enrichment was used to enrich the observed DEGs, protein-protein interactions were established, and hub genes were identified.

Results

The findings revealed that DEGs were considerably enriched in a number of pathways, including the "Pathways in cancer", "Breast cancer", and "Rap1 signaling pathway." DEGs were also found to be involved in a number of biological processes, cellular components, and molecular activities. The PPI network included the hub genes CTNNB1, FGFR3, ESR1, and SRSF3 as well as a number of DElncRNAs, such as LINC01541, SNHG17, LINC00520, BHLHE22-AS1, LOC100509445, H19, and HOTAIRM1.

Conclusion

This study contributes to our understanding of the molecular processes driving EC metastasis, which may result in the development of new treatment targets and indicators for the early identification of EC metastasis. More studies are needed to validate these findings and to understand the functional roles of these key factors in EC metastasis.

The prognostic genes model of breast cancer drug resistance based on single-cell sequencing analysis and transcriptome analysis

Breast cancer (BC) represents a multifaceted malignancy, with escalating incidence and mortality rates annually. Chemotherapy stands as an indispensable approach for treating breast cancer, yet drug resistance poses a formidable challenge. Through transcriptome data analysis, we have identified two sets of genes exhibiting differential expression in this context. Furthermore, we have confirmed the overlap between these genes and those associated with exosomes, which were subsequently validated in cell lines. The investigation screened the identified genes to determine prognostic markers for BC and utilized them to formulate a prognostic model. The disparities in prognosis and immunity between the high- and low-risk groups were validated using the test dataset. We have discerned different BC subtypes based on the expression levels of prognostic genes in BC samples. Variations in prognosis, immunity, and drug sensitivity among distinct subtypes were examined. Leveraging data from single-cell sequencing and prognostic gene expression, the AUCell algorithm was employed to score individual cell clusters and analyze the pathways implicated in high-scoring groups. Prognostic genes (CCT4, CXCL13, MTDH, PSMD2, and RAB27A) were subsewoquently validated using RT-qPCR. Consequently, we have established a model for predicting prognosis in breast cancer that hinges on drug resistance and ERGs. Furthermore, we have evaluated the prognostic value of this model. The genes identified as prognostic markers can now serve as a reference for precise treatment of this condition.

Identifying Key Regulatory Genes in Drug Resistance Acquisition: Modeling Pseudotime Trajectories of Breast Cancer Single-Cell Transcriptome

Single-cell RNA-sequencing (scRNA-seq) technology has provided significant insights into cancer drug resistance at the single-cell level. However, understanding dynamic cell transitions at the molecular systems level remains limited, requiring a systems biology approach. We present an approach that combines mathematical modeling with a pseudotime analysis using time-series scRNA-seq data obtained from the breast cancer cell line MCF-7 treated with tamoxifen. Our single-cell analysis identified five distinct subpopulations, including tamoxifen-sensitive and -resistant groups. Using a single-gene mathematical model, we discovered approximately 560-680 genes out of 6000 exhibiting multistable expression states in each subpopulation, including key estrogen-receptor-positive breast cancer cell survival genes, such as RPS6KB1. A bifurcation analysis elucidated their regulatory mechanisms, and we mapped these genes into a molecular network associated with cell survival and metastasis-related pathways. Our modeling approach comprehensively identifies key regulatory genes for drug resistance acquisition, enhancing our understanding of potential drug targets in breast cancer.

Integrated analysis of diverse cancer types reveals a breast cancer-specific serum miRNA biomarker through relative expression orderings analysis

PURPOSE

Serum microRNA (miRNA) holds great potential as a non-invasive biomarker for diagnosing breast cancer (BrC). However, most diagnostic models rely on the absolute expression levels of miRNAs, which are susceptible to batch effects and challenging for clinical transformation. Furthermore, current studies on liquid biopsy diagnostic biomarkers for BrC mainly focus on distinguishing BrC patients from healthy controls, needing more specificity assessment.

METHODS

We collected a large number of miRNA expression data involving 8465 samples from GEO, including 13 different cancer types and non-cancer controls. Based on the relative expression orderings (REOs) of miRNAs within each sample, we applied the greedy, LASSO multiple linear regression, and random forest algorithms to identify a qualitative biomarker specific to BrC by comparing BrC samples to samples of other cancers as controls.

RESULTS

We developed a BrC-specific biomarker called 7-miRPairs, consisting of seven miRNA pairs. It demonstrated comparable classification performance in our analyzed machine learning algorithms while requiring fewer miRNA pairs, accurately distinguishing BrC from 12 other cancer types. The diagnostic performance of 7-miRPairs was favorable in the training set (accuracy = 98.47%, specificity = 98.14%, sensitivity = 99.25%), and similar results were obtained in the test set (accuracy = 97.22%, specificity = 96.87%, sensitivity = 98.02%). KEGG pathway enrichment analysis of the 11 miRNAs within the 7-miRPairs revealed significant enrichment of target mRNAs in pathways associated with BrC.

CONCLUSION

Our study provides evidence that utilizing serum miRNA pairs can offer significant advantages for BrC-specific diagnosis in clinical practice by directly comparing serum samples with BrC to other cancer types.

The adverse outcome pathway for breast cancer: a knowledge management framework bridging biomedicine and toxicology

Breast cancer is the most common cancer worldwide, with an estimated 2.3 million new cases diagnosed every year. Effective measures for cancer prevention and cancer therapy require a detailed understanding of the individual key disease mechanisms involved and their interactions at the molecular, cellular, tissue, organ, and organism level. In that regard, the rapid progress of biomedical and toxicological research in recent years now allows the pursuit of new approaches based on non-animal methods that provide greater mechanistic insight than traditional animal models and therefore facilitate the development of Adverse Outcome Pathways (AOPs) for human diseases. We performed a systematic review of the current state of published knowledge with regard to breast cancer to identify relevant key mechanisms for inclusion into breast cancer AOPs, i.e. decreased cell stiffness and decreased cell adhesion, and to concurrently map non-animal methods addressing these key events. We conclude that the broader sharing of expertise and methods between biomedical research and toxicology enabled by the AOP knowledge management framework can help to coordinate global research efforts and accelerate the transition to advanced non-animal methods, which, when combined into powerful method batteries, closely mimic human physiology and disease states without the need for animal testing.

Application of self-organizing maps to AFM-based viscoelastic characterization of breast cancer cell mechanics

Cell mechanical properties have been proposed as label free markers for diagnostic purposes in diseases such as cancer. Cancer cells show altered mechanical phenotypes compared to their healthy counterparts. Atomic Force Microscopy (AFM) is a widely utilized tool to study cell mechanics. These measurements often need skilful users, physical modelling of mechanical properties and expertise in data interpretation. Together with the need to perform many measurements for statistical significance and to probe wide enough areas in tissue structures, the application of machine learning and artificial neural network techniques to automatically classify AFM datasets has received interest recently. We propose the use of self-organizing maps (SOMs) as unsupervised artificial neural network applied to mechanical measurements performed via AFM on epithelial breast cancer cells treated with different substances that affect estrogen receptor signalling. We show changes in mechanical properties due to treatments, as estrogen softened the cells, while resveratrol led to an increase in cell stiffness and viscosity. These data were then used as input for SOMs. Our approach was able to distinguish between estrogen treated, control and resveratrol treated cells in an unsupervised manner. In addition, the maps enabled investigation of the relationship of the input variables.

N7-Methylguanosine-Related lncRNAs: Integrated Analysis Associated With Prognosis and Progression in Clear Cell Renal Cell Carcinoma

N7-Methylguanosine (m7G) and long non-coding RNAs (lncRNAs) have been widely reported to play an important role in cancer. However, there is little known about the relationship between m7G-related lncRNAs and clear cell renal cell carcinoma (ccRCC). To find new potential biomarkers and construct an m7G-related lncRNA prognostic signature for ccRCC, we retrieved transcriptome data and clinical data from The Cancer Genome Atlas (TCGA), and divided the entire set into train set and test set with the ratio of 1:1 randomly. The m7G-related lncRNAs were identified by Pearson correlation analysis (|coefficients| &gt; 0.4, and p &lt; 0.001). Then we performed the univariate Cox regression and least absolute shrinkage and selection operator (LASSO) Cox regression analysis to construct a 12 m7G-related lncRNA prognostic signature. Next, principal component analysis (PCA), the Kaplan–Meier method, time-dependent receiver operating characteristics (ROC) were made to verify and evaluate the risk signature. A nomogram based on the risk signature and clinical parameters was developed and showed high accuracy and reliability for predicting the overall survival (OS). Functional enrichment analysis (GO, KEGG and GSEA) was used to investigate the potential biological pathways. We also performed the analysis of tumor mutation burden (TMB), immunological analysis including immune scores, immune cell infiltration (ICI), immune function, tumor immune escape (TIE) and immunotherapeutic drug in our study. In conclusion, using the 12 m7G-related lncRNA risk signature as a prognostic indicator may offer us insight into the oncogenesis and treatment response prediction of ccRCC.

Non-Invasive microRNA Profiling in Saliva can Serve as a Biomarker of Alcohol Exposure and Its Effects in Humans

Alcohol Use Disorder (AUD) is one of the most prevalent mental disorders worldwide. Considering the widespread occurrence of AUD, a reliable, cheap, non-invasive biomarker of alcohol consumption is desired by healthcare providers, clinicians, researchers, public health and criminal justice officials. microRNAs could serve as such biomarkers. They are easily detectable in saliva, which can be sampled from individuals in a non-invasive manner. Moreover, microRNAs expression is dynamically regulated by environmental factors, including alcohol. Since excessive alcohol consumption is a hallmark of alcohol abuse, we have profiled microRNA expression in the saliva of chronic, heavy alcohol abusers using microRNA microarrays. We observed significant changes in salivary microRNA expression caused by excessive alcohol consumption. These changes fell into three categories: downregulated microRNAs, upregulated microRNAs, and microRNAs upregulated de novo. Analysis of these combinatorial changes in microRNA expression suggests dysregulation of specific biological pathways leading to impairment of the immune system and development of several types of epithelial cancer. Moreover, some of the altered microRNAs are also modulators of inflammation, suggesting their contribution to pro-inflammatory mechanisms of alcohol actions. Establishment of the cellular source of microRNAs in saliva corroborated these results. We determined that most of the microRNAs in saliva come from two types of cells: leukocytes involved in immune responses and inflammation, and buccal cells, involved in development of epithelial, oral cancers. In summary, we propose that microRNA profiling in saliva can be a useful, non-invasive biomarker allowing the monitoring of alcohol abuse, as well as alcohol-related inflammation and early detection of cancer.

Quantitative high-throughput phenotypic screening for environmental estrogens using the E-Morph Screening Assay in combination with in silico predictions

BACKGROUND

Exposure to environmental chemicals that interfere with normal estrogen function can lead to adverse health effects, including cancer. High-throughput screening (HTS) approaches facilitate the efficient identification and characterization of such substances.

OBJECTIVES

We recently described the development of the E-Morph Assay, which measures changes at adherens junctions as a clinically-relevant phenotypic readout for estrogen receptor (ER) alpha signaling activity. Here, we describe its further development and application for automated robotic HTS.

METHODS

Using the advanced E-Morph Screening Assay, we screened a substance library comprising 430 toxicologically-relevant industrial chemicals, biocides, and plant protection products to identify novel substances with estrogenic activities. Based on the primary screening data and the publicly available ToxCast dataset, we performed an insilico similarity search to identify further substances with potential estrogenic activity for follow-up hit expansion screening, and built seven insilico ER models using the conformal prediction (CP) framework to evaluate the HTS results.

RESULTS

The primary and hit confirmation screens identified 27 'known' estrogenic substances with potencies correlating very well with the published ToxCast ER Agonist Score (r=+0.95). We additionally detected potential 'novel' estrogenic activities for 10 primary hit substances and for another nine out of 20 structurally similar substances from insilico predictions and follow-up hit expansion screening. The concordance of the E-Morph Screening Assay with the ToxCast ER reference data and the generated CP ER models was 71% and 73%, respectively, with a high predictivity for ER active substances of up to 87%, which is particularly important for regulatory purposes.

DISCUSSION

These data provide a proof-of-concept for the combination of in vitro HTS approaches with insilico methods (similarity search, CP models) for efficient analysis of large substance libraries in order to prioritize substances with potential estrogenic activity for subsequent testing against higher tier human endpoints.

Automated Classification of Cellular Phenotypes Using Machine Learning in Cellprofiler and CellProfiler Analyst

Cell images provide a multitude of phenotypic information, which in its entirety the human eye can hardly perceive. Automated image analysis and machine learning approaches enable the unbiased identification and analysis of cellular mechanisms and associated pathological effects. This protocol describes a customized image analysis pipeline that detects and quantifies changes in the localization of E-Cadherin and the morphology of adherens junctions using image-based measurements generated by CellProfiler and the machine learning functionality of CellProfiler Analyst.

Heat Shock Factor 1 (HSF1) cooperates with estrogen receptor α (ERα) in the regulation of estrogen action in breast cancer cells

Heat shock factor 1 (HSF1), a key regulator of transcriptional responses to proteotoxic stress, was linked to estrogen (E2) signaling through estrogen receptor α (ERα). We found that an HSF1 deficiency may decrease ERα level, attenuate the mitogenic action of E2, counteract E2-stimulated cell scattering, and reduce adhesion to collagens and cell motility in ER-positive breast cancer cells. The stimulatory effect of E2 on the transcriptome is largely weaker in HSF1-deficient cells, in part due to the higher basal expression of E2-dependent genes, which correlates with the enhanced binding of unliganded ERα to chromatin in such cells. HSF1 and ERα can cooperate directly in E2-stimulated regulation of transcription, and HSF1 potentiates the action of ERα through a mechanism involving chromatin reorganization. Furthermore, HSF1 deficiency may increase the sensitivity to hormonal therapy (4-hydroxytamoxifen) or CDK4/6 inhibitors (palbociclib). Analyses of data from The Cancer Genome Atlas database indicate that HSF1 increases the transcriptome disparity in ER-positive breast cancer and can enhance the genomic action of ERα. Moreover, only in ER-positive cancers an elevated HSF1 level is associated with metastatic disease.

About 70% of breast cancers rely on supplies of a hormone called estrogen – which is the main hormone responsible for female physical characteristics – to grow. Breast cancer cells that are sensitive to estrogen possess proteins known as estrogen receptors and are classified as estrogen-receptor positive. When estrogen interacts with its receptor in a cancer cell, it stimulates the cell to grow and migrate to other parts of the body. Therefore, therapies that decrease the amount of estrogen the body produces, or inhibit the receptor itself, are widely used to treat patients with estrogen receptor-positive breast cancers.

When estrogen interacts with an estrogen receptor known as ERα it can also activate a protein called HSF1, which helps cells to survive under stress. In turn, HSF1 regulates several other proteins that are necessary for ERα and other estrogen receptors to work properly. Previous studies have suggested that high levels of HSF1 may worsen the outcomes for patients with estrogen receptor-positive breast cancers, but it remains unclear how HSF1 acts in breast cancer cells.

Vydra, Janus, Kuś et al. used genetics and bioinformatics approaches to study HSF1 in human breast cancer cells. The experiments revealed that breast cancer cells with lower levels of HSF1 also had lower levels of ERα and responded less well to estrogen than cells with higher levels of HSF1. Further experiments suggested that in the absence of estrogen, HSF1 helps to keep ERα inactive. However, when estrogen is present, HSF1 cooperates with ERα and enhances its activity to help cells grow and migrate. Vydra, Janus, Kuś et al. also found that cells with higher levels of HSF1 were less sensitive to two drug therapies that are commonly used to treat estrogen receptor-positive breast cancers.

These findings reveal that the effect HSF1 has on ERα activity depends on the presence of estrogen. Therefore, cancer therapies that decrease the amount of estrogen a patient produces may have a different effect on estrogen receptor-positive tumors with high HSF1 levels than tumors with low HSF1 levels.

Enhanced Directed Random Walk for the Identification of Breast Cancer Prognostic Markers from Multiclass Expression Data

Artificial intelligence in healthcare can potentially identify the probability of contracting a particular disease more accurately. There are five common molecular subtypes of breast cancer: luminal A, luminal B, basal, ERBB2, and normal-like. Previous investigations showed that pathway-based microarray analysis could help in the identification of prognostic markers from gene expressions. For example, directed random walk (DRW) can infer a greater reproducibility power of the pathway activity between two classes of samples with a higher classification accuracy. However, most of the existing methods (including DRW) ignored the characteristics of different cancer subtypes and considered all of the pathways to contribute equally to the analysis. Therefore, an enhanced DRW (eDRW+) is proposed to identify breast cancer prognostic markers from multiclass expression data. An improved weight strategy using one-way ANOVA (F-test) and pathway selection based on the greatest reproducibility power is proposed in eDRW+. The experimental results show that the eDRW+ exceeds other methods in terms of AUC. Besides this, the eDRW+ identifies 294 gene markers and 45 pathway markers from the breast cancer datasets with better AUC. Therefore, the prognostic markers (pathway markers and gene markers) can identify drug targets and look for cancer subtypes with clinically distinct outcomes.

Estrogen Modulates Epithelial Breast Cancer Cell Mechanics and Cell-to-Cell Contacts

Excessive estrogen exposure is connected with increased risk of breast cancer and has been shown to promote epithelial-mesenchymal-transition. Malignant cancer cells accumulate changes in cell mechanical and biochemical properties, often leading to cell softening. In this work we have employed atomic force microscopy to probe the influence of estrogen on the viscoelastic properties of MCF-7 breast cancer cells cultured either in normal or hormone free-medium. Estrogen led to a significant softening of the cells in all studied cases, while growing cells in hormone free medium led to an increase in the studied elastic and viscoelastic moduli. In addition, fluorescence microscopy shows that E-cadherin distribution is changed in cells when culturing them under estrogenic conditions. Furthermore, cell-cell contacts seemed to be weakened. These results were supported by AFM imaging showing changes in surfaces roughness, cell-cell contacts and cell height as result of estrogen treatment. This study therefore provides further evidence for the role of estrogen signaling in breast cancer.

The E-Morph Assay: Identification and characterization of environmental chemicals with estrogenic activity based on quantitative changes in cell-cell contact organization of breast cancer cells

Adverse health effects that are caused by endocrine disrupting chemicals (EDCs) in the environment, food or consumer products are of high public concern. The identification and characterization of EDCs including substances with estrogenic activity still necessitates the use of animal testing as most of the approved alternative test methods only address single mechanistic events of endocrine activity. Therefore, novel human-relevant in vitro assays covering more complex functional endpoints of adversity, including hormone-related tumor formation and progression, are needed. This study describes the development and evaluation of a novel high-throughput screening-compatible assay called "E-Morph Assay". This image-based phenotypic screening assay facilitates robust predictions of the estrogenic potential of environmental chemicals using quantitative changes in the cell-cell contact morphology of human breast cancer cells as a novel functional endpoint. Based on a classification model, which was developed using six reference substances with known estrogenic activity, the E-Morph Assay correctly classified an additional set of 11 reference chemicals commonly used in OECD Test Guidelines and the U.S. EPA ToxCast program. For each of the tested substances, a relative ER bioactivity score was derived that allowed their grouping into four main categories of estrogenic activity, i.e. 'strong' (>0.9; four substances, i.e. natural hormones or pharmaceutical products), 'moderate' (0.9-0.6; six substances, i.e. phytoestrogens and Bisphenol AF), 'weak' (<0.6; three substances, i.e Bisphenol S, B, and A), and 'negative' (0.0; four substances). The E-Morph Assay considerably expands the portfolio of test methods providing the possibility to characterize the influence of environmental chemicals on estrogen-dependent tumor progression.

MicroRNA profiling identifies Forkhead box transcription factor M1 (FOXM1) regulated miR-186 and miR-200b alterations in triple negative breast cancer

Breast cancer (BC) is the most commonly diagnosed malignancy. MicroRNAs (miRNAs) play important roles in the tumorigenesis, metastasis and progression of BC. Forkhead Box M1 (FOXM1) oncogenic transcription factor is involved in events considered as hallmarks of cancer. However, the specific mechanism by which FOXM1 exerts its oncogenic effects remains unclear and little is known about its effects on the regulation of miRNA expression. We have found that FOXM1 is upregulated in breast cancer cells and that its expression is associated with shortened overall survival and poor prognosis in patients with BC. Using microarray technology, we assessed the expression profiles of 752 miRNAs in highly aggressive and metastatic triple negative breast cancer (TNBC) cells in response to FOXM1 knockdown and identified 13 differentialy expressed miRNAs (3 miRNAs upregulated and 10 miRNAs down-regulated). We validated the results of the miRNA expression profile in two different TNBC cells by performing qRT-PCR and identified that miR-186-5p and miR-200b-5p were consistently down- or up-regulated, respectively, after knockdown of FOXM1. We further performed KEGG pathway analysis and GO enrichment analysis for miR-186-5p and miR-200b-5p, and identified that these miRNAs are associated with cancer development and progression involving toll-like receptor signaling, cell cycle, AMPK, p53 and NF-kappa B signaling pathways. Taken together, our results suggest that increased FOXM1 expression is associated with poor patient survival and leads to induction of oncomiR miR-186-5p expression and tumor-suppressor inhibition miR-200b-5p, suggesting that the FOXM1/miRNA signaling pathway may contribute to poor patient prognosis and may be a potential therapeutic target in TNBC.