scholarly journals Prediction of Bone Metastasis in Breast Cancer Based on Minimal Driver Gene Set in Gene Dependency Network

Genes ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 466
Author(s):  
Li ◽  
Zhong ◽  
Zhou
Keyword(s):  
Breast Cancer ◽  
Bone Metastasis ◽  
Cancer Metastasis ◽  
Breast Cancer Metastasis ◽  
Driver Gene ◽  
Data Set ◽  
Gene Set ◽  
Signature Genes ◽  
Dependency Network ◽  
Better Than

Bone is the most frequent organ for breast cancer metastasis, and thus it is essential to predict the bone metastasis of breast cancer. In our work, we constructed a gene dependency network based on the hypothesis that the relation between one gene and the risk of bone metastasis might be affected by another gene. Then, based on the structure controllability theory, we mined the driver gene set which can control the whole network in the gene dependency network, and the signature genes were selected from them. Survival analysis showed that the signature could distinguish the bone metastasis risks of cancer patients in the test data set and independent data set. Besides, we used the signature genes to construct a centroid classifier. The results showed that our method is effective and performed better than published methods.

scholarly journals Bridging Cancer Biology with the Clinic: Relative Expression of a GRHL2-Mediated Gene-Set Pair Predicts Breast Cancer Metastasis

PLoS ONE ◽  
2013 ◽  
Vol 8 (2) ◽  
pp. e56195 ◽  
Author(s):  
Xinan Yang ◽  
Prabhakaran Vasudevan ◽  
Vishwas Parekh ◽  
Aleks Penev ◽  
John M. Cunningham
Keyword(s):  
Breast Cancer ◽  
Cancer Biology ◽  
Cancer Metastasis ◽  
Breast Cancer Metastasis ◽  
Relative Expression ◽  
Gene Set

BRMS1L inhibits bone metastasis of breast cancer cells through epigenetic silence of CXCR4.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e13002-e13002
Author(s):  
Yinghuan Cen ◽  
Chang Gong ◽  
Jun Li ◽  
Gehao Liang ◽  
Zihao Liu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Bone Metastasis ◽  
Cancer Cells ◽  
Breast Cancer Cells ◽  
Cancer Metastasis ◽  
Breast Cancer Metastasis ◽  
Breast Cancer Cell Lines ◽  
Breast Cancer Patients ◽  
Qrt Pcr ◽  
Metastatic Sites

e13002 Background: We previously demonstrated that BRMS1L (breast cancer metastasis suppressor 1 like) suppresses breast cancer metastasis through HDAC1 recruitment and histone H3K9 deacetylation at the promoter of FZD10, a receptor for Wnt signaling. It is still unclear whether BRMS1L regulates organ-specific metastases, such as bone metastasis, the most prevalent metastatic site of breast cancer. Methods: Examination of the expression of BRMS1L in primary tumors, bone metastatic and other metastatic tissues from breast cancer patients was implemented using qRT-PCR and immunohistochemistry staining. To investigate the mechanism by which BRMS1L drives breast cancer bone metastasis, we tested the mRNA expression by qRT-PCR of a set of potential bone related genes (BRGs) based on PubMed database in MDA-MB-231 cells over expressing BRMS1L and MCF-7 cells knocking-down BRMS1L, and detected the expression of CXCR4 in these established cells by western blot. Transwell assays were performed to assess the migration abilities of breast cancer cells towards osteoblasts. ChIP (Chromatin Immuno-Precipitation) were employed to test the interaction between BRMS1L and CXCR4. Results: At both mRNA and protein levels, the expression of BRMS1L was significantly lower in bone metastatic sites than that in primary cancer tissues and other metastatic sites of breast cancer patients. CXCR4 was screened out in a set of BRGs and negatively correlated with the expression of BRMS1L in breast cancer cell lines. BRMS1L inhibited the migration of breast cancer cells towards osteoblasts through CXCL12/CXCR4 axis. In the presence of TSA treatment, breast cancer cell lines showed an increased expression of CXCR4 in a TSA concentration-dependent manner. In addition, ChIP assays verified that BRMS1L directly bound to the promoter region of CXCR4 and inhibited its transcription through promoter histone deacetylation. Conclusions: BRMS1L mediates the migration abilities of breast cancer cells to bone microenvironment via targeting CXCR4 and contributes to bone metastasis of breast cancer cells. Thus, BRMS1L may be a potential biomarker for predicting bone metastasis in breast cancer.

scholarly journals Involvement of a Transcription factor, Nfe2, in Breast Cancer Metastasis to Bone

Cancers ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 3003
Author(s):  
Di Zhang ◽  
Sadahiro Iwabuchi ◽  
Tomohisa Baba ◽  
Shin-ichi Hashimoto ◽  
Naofumi Mukaida ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Transcription Factor ◽  
Bone Metastasis ◽  
Cancer Metastasis ◽  
High Growth ◽  
Breast Cancer Metastasis ◽  
Tumor Formation ◽  
Bone Microenvironment ◽  
Tnbc Cell

Patients with triple negative breast cancer (TNBC) is frequently complicated by bone metastasis, which deteriorates the life expectancy of this patient cohort. In order to develop a novel type of therapy for bone metastasis, we established 4T1.3 clone with a high capacity to metastasize to bone after orthotopic injection, from a murine TNBC cell line, 4T1.0. To elucidate the molecular mechanism underlying a high growth ability of 4T1.3 in a bone cavity, we searched for a novel candidate molecule with a focus on a transcription factor whose expression was selectively enhanced in a bone cavity. Comprehensive gene expression analysis detected enhanced Nfe2 mRNA expression in 4T1.3 grown in a bone cavity, compared with in vitro culture conditions. Moreover, Nfe2 gene transduction into 4T1.0 cells enhanced their capability to form intraosseous tumors. Moreover, Nfe2 shRNA treatment reduced tumor formation arising from intraosseous injection of 4T1.3 clone as well as another mouse TNBC-derived TS/A.3 clone with an augmented intraosseous tumor formation ability. Furthermore, NFE2 expression was associated with in vitro growth advantages of these TNBC cell lines under hypoxic condition, which mimics the bone microenvironment, as well as Wnt pathway activation. These observations suggest that NFE2 can potentially contribute to breast cancer cell survival in the bone microenvironment.

scholarly journals Machine Learning Model for Lymph Node Metastasis Prediction in Breast Cancer Using Random Forest Algorithm and Mitochondrial Metabolism Hub Genes

2021 ◽  
Vol 11 (7) ◽  
pp. 2897
Author(s):  
Byung-Chul Kim ◽  
Jingyu Kim ◽  
Ilhan Lim ◽  
Dong Ho Kim ◽  
Sang Moo Lim ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Random Forest ◽  
Prediction Model ◽  
Cancer Metastasis ◽  
Breast Cancer Metastasis ◽  
Hub Genes ◽  
Hub Gene ◽  
Gene Set ◽  
Machine Learning Model

Breast cancer metastasis can have a fatal outcome, with the prediction of metastasis being critical for establishing effective treatment strategies. RNA-sequencing (RNA-seq) is a good tool for identifying genes that promote and support metastasis development. The hub gene analysis method is a bioinformatics method that can effectively analyze RNA sequencing results. This can be used to specify the set of genes most relevant to the function of the cell involved in metastasis. Herein, a new machine learning model based on RNA-seq data using the random forest algorithm and hub genes to estimate the accuracy of breast cancer metastasis prediction. Single-cell breast cancer samples (56 metastatic and 38 non-metastatic samples) were obtained from the Gene Expression Omnibus database, and the Weighted Gene Correlation Network Analysis package was used for the selection of gene modules and hub genes (function in mitochondrial metabolism). A machine learning prediction model using the hub gene set was devised and its accuracy was evaluated. A prediction model comprising 54-functional-gene modules and the hub gene set (NDUFA9, NDUFB5, and NDUFB3) showed an accuracy of 0.769 ± 0.02, 0.782 ± 0.012, and 0.945 ± 0.016, respectively. The test accuracy of the hub gene set was over 93% and that of the prediction model with random forest and hub genes was over 91%. A breast cancer metastasis dataset from The Cancer Genome Atlas was used for external validation, showing an accuracy of over 91%. The hub gene assay can be used to predict breast cancer metastasis by machine learning.

scholarly journals Embracing Mechanobiology in Next Generation Organ-On-A-Chip Models of Bone Metastasis

2021 ◽  
Vol 3 ◽  
Author(s):  
Ellen E. Slay ◽  
Fiona C. Meldrum ◽  
Virginia Pensabene ◽  
Mahetab H. Amer
Keyword(s):  
Breast Cancer ◽  
Bone Metastasis ◽  
Cancer Metastasis ◽  
Breast Cancer Metastasis ◽  
Vital Role ◽  
Next Generation ◽  
Organ On A Chip ◽  
Cell Matrix Interactions

Bone metastasis in breast cancer is associated with high mortality. Biomechanical cues presented by the extracellular matrix play a vital role in driving cancer metastasis. The lack of in vitro models that recapitulate the mechanical aspects of the in vivo microenvironment hinders the development of novel targeted therapies. Organ-on-a-chip (OOAC) platforms have recently emerged as a new generation of in vitro models that can mimic cell-cell interactions, enable control over fluid flow and allow the introduction of mechanical cues. Biomaterials used within OOAC platforms can determine the physical microenvironment that cells reside in and affect their behavior, adhesion, and localization. Refining the design of OOAC platforms to recreate microenvironmental regulation of metastasis and probe cell-matrix interactions will advance our understanding of breast cancer metastasis and support the development of next-generation metastasis-on-a-chip platforms. In this mini-review, we discuss the role of mechanobiology on the behavior of breast cancer and bone-residing cells, summarize the current capabilities of OOAC platforms for modeling breast cancer metastasis to bone, and highlight design opportunities offered by the incorporation of mechanobiological cues in these platforms.

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