Project description:Metastasis is the main cause of mortality of breast cancer. To explore the mechanisms of arsenic trioxide (ATO) in inhibition of breast cancer metastasis, ATO regulated genes in breast cancer MDA-MB-231 and LM2-4175 cells were studied. After data analysis, ATO regulated genes were involved in TP53, TGFβ and TNFα signaling pathways. Furthermore, TGFβ and TNFα activated genes in breast cancer MDA-MB-231 cells were studied.
Project description:Breast Cancer is the cancer with most incidence and mortality in women. microRNAs are emerging as novel prognosis/diagnostic tools. Our aim was to identify a serum microRNA signature useful to predict cancer development. We focused on studying the expression levels of 30 microRNAs in the serum of 96 breast cancer patients versus 92 control individuals. Bioinformatic studies provide a microRNA signature, designated as a predictor, based upon the expression levels of 5 microRNAs. Then, we tested the predictor in a group of 60 randomly chosen women. Lastly, a proteomic study unveiled the over-expression and down-regulation of proteins differently expressed in the serum of breast cancer patients versus that of control individuals. Twenty-six microRNAs differentiate cancer tissue from healthy tissue and 16 microRNAs differentiate the serum of cancer patients from that of the control group. The tissue expression of miR-99a-5p, mir-497-5p, miR-362, and miR-1274, and the serum levels of miR-141 correlated with patient survival. Moreover, the predictor consisting of mir-125b-5p, miR-29c-3p, mir-16-5p, miR-1260, and miR-451a was able to differentiate breast cancer patients from controls. The predictor was validated in 20 new cases of breast cancer patients and tested in 60 volunteer women, assigning 11 out of 60 women to the cancer group. An association of low levels of mir-16-5p with a high content of CD44 protein in serum was found. Circulating microRNAs in serum can represent biomarkers for cancer prediction. Their clinical relevance and use of the predictor here described might be of potential importance for breast cancer prediction.
Project description:Cancer prevention has a profound impact on cancer-associated mortality and morbidity. We previously identified TGFβ signaling as a candidate regulator of mammary epithelial cells associated with breast cancer risk. Here, we show that short-term TGFBR inhibitor (TGFBRi) treatment of peripubertal ACI inbred and Sprague Dawley outbred rats induces lasting changes and prevents estrogen- and carcinogen-induced mammary tumors, respectively. We identify TGFBRi-responsive cell populations by single cell RNA-sequencing, including a unique epithelial subpopulation designated secretory basal cells (SBCs) with progenitor features. We detect SBCs in normal human breast tissues and find them to be associated with breast cancer risk. Interactome analysis identifies SBCs as the most interactive cell population and the main source of insulin-IGF signaling. Accordingly, inhibition of TGFBR and IGF1R decrease proliferation of organoid cultures. Our results reveal a critical role for TGFβ in regulating mammary epithelial cells relevant to breast cancer and serve as a proof-of-principle cancer prevention strategy.
Project description:Introduction: microRNAs are promising candidate breast cancer biomarkers due to their cancer-specific expression profiles. However, efforts to develop circulating breast cancer biomarkers are challenged by the heterogeneity of microRNAs in the blood. To overcome this challenge, we aimed to develop a molecular profile of microRNAs specifically secreted from breast cancer cells. Our first step towards this direction relates to capturing and analyzing the contents of exosomes, which are small secretory vesicles that selectively encapsulate microRNAs indicative of their cell of origin. To our knowledge, circulating exosome microRNAs have not been well evaluated as biomarkers for breast cancer diagnosis or monitoring. Methods: Exosomes were collected from the conditioned media of human breast cancer cell lines, mouse plasma of patient-derived orthotopic xenograft models (PDX), and human plasma samples. Exosomes were verified by electron microscopy, nanoparticle tracking analysis, and western blot. Cellular and exosome microRNAs from breast cancer cell lines were profiled by next-generation small RNA sequencing. Plasma exosome microRNA expression was analyzed by qRT-PCR analysis. Results: Small RNA sequencing and qRT-PCR analysis showed that several microRNAs are selectively encapsulated or highly enriched in breast cancer exosomes. Importantly, the selectively enriched exosome microRNA, human miR-1246, was detected at significantly higher levels in exosomes isolated from PDX mouse plasma, indicating that tumor exosome microRNAs are released into the circulation and can serve as plasma biomarkers for breast cancer. This observation was extended to human plasma samples where miR-1246 and miR-21 were detected at significantly higher levels in the plasma exosomes of 16 breast cancer patients as compared to the plasma exosomes of healthy control subjects. Receiver Operating Characteristic (ROC) curve analysis indicated that the combination of plasma exosome miR-1246 and miR-21 levels is a better indicator of breast cancer than their individual levels. Conclusions: Our results demonstrate that certain microRNA species, such as miR-21 and miR-1246, are selectively enriched in human breast cancer exosomes and significantly elevated in the plasma of breast cancer patients. These findings indicate a potential new strategy to selectively analyze plasma breast cancer microRNAs indicative of the presence of breast cancer.
Project description:To understand the mechanism by which TGFβ regulates tumor initiation in triple negative breast cancer (TNBC), we performed microarray analysis using Illumina Human HT-12 Gene Expression BeadChip. A TNBC cell line of SCP2 was treated or not with TGFβ for 24 hours. Differentially expressed genes were analyzed by comparing treated versus non-treated samples
Project description:Uncontrolled Transforming growth factor-beta (TGFβ) signaling promotes aggressive metastatic properties in late-stage breast cancers. However, how TGFβ-mediated cues are directed to induce late-stage tumorigenic events is poorly understood, particularly given that TGFβ has clear tumor suppressing activity in other contexts. Here we demonstrate that the transcriptional regulators TAZ and YAP (TAZ/YAP), key effectors of the Hippo pathway, are necessary to promote and maintain TGFβ-induced tumorigenic phenotypes in breast cancer cells. Interactions between TAZ/YAP, TGFβ-activated SMAD2/3, and TEAD transcription factors reveal convergent roles for these factors in the nucleus. Genome-wide expression analyses indicate that TAZ/YAP, TEADs and TGFβ-induced signals coordinate a specific pro-tumorigenic transcriptional program. Importantly, genes cooperatively regulated by TAZ/YAP, TEAD, and TGFβ, such as the novel targets NEGR1 and UCA1, are necessary for maintaining tumorigenic activity in metastatic breast cancer cells. Nuclear TAZ/YAP also cooperate with TGFβ signaling to promote phenotypic and transcriptional changes in non-tumorigenic cells to overcome TGFβ repressive effects. Our work thus identifies crosstalk between nuclear TAZ/YAP and TGFβ signaling in breast cancer cells, revealing novel insight into late-stage disease-driving mechanisms. Expression profiling was conducted following the repression of the transcriptional regulators TAZ and YAP (TAZ/YAP), the TEAD family of transcription factors (TEAD1/2/3/4), or the TGFb signaling pathway (with SB-431542, an inhibitor of the TBRI recpeptor) in human MDA-MB-231-LM2 breast cancer cells treated with TGFβ1. Human MDA-MB-231-LM2-4 breast cancer cells were transfected with control siRNA, or siRNAs targeting TAZ/YAP or all four TEADs and were treated 24 hours later with 500pM TGFβ1 or 5mM SB-431542 for an additional 24 hours. Total RNA was isolated and twelve microarrays in total were performed, with each condition carried out three times on separate days. The Boston University Microarray Core generated the data using the Affymetrix Human Gene 1.0 St Array.