Highly Expressed DLL4 and JAG1: Their Role in Incidence of Breast Cancer Metastasis

Zohny, Samir F.; Zamzami, Mazin A.; Al-Malki, Abdulrahman L.; Trabulsi, Nora

doi:10.1016/j.arcmed.2019.12.019

Cited by 23 publications

(21 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous studies have demonstrated that EGR1 could suppress the proliferation and migration of breast cancer cells [ 44 , 45 ]. The overexpression of JAG1 has been observed in breast cancer patients and is associated with the development of distant metastasis [ 46 ].…”

Section: Resultsmentioning

confidence: 99%

Transcriptome study of oleanolic acid in the inhibition of breast tumor growth based on high-throughput sequencing

Liang

Pan

Xia

et al. 2021

Aging

View full text Add to dashboard Cite

The function of oleanolic acid (OA) in various types of cancer has been reported frequently, especially for breast cancer. However, the regulation of breast tumor growth in response to OA treatment has not been studied in depth. Here, we first explored the effect of OA treatment on breast tumors in vitro and in vivo and then used RNA-seq technology to study the effect and molecular mechanism of OA treatment of MCF-7 cells, particularly at the level of functional genomics. The results showed that 40 μM OA treatment could significantly inhibit the proliferation and induce the apoptosis of MCF-7 cells. Through analysis of RNA sequencing data quality and differentially expressed genes (DEGs), 67 significantly downregulated genes and 260 significantly upregulated genes were identified to be involved in OA treatment of MCF-7 cells. Among these genes, 43 unique DEGs were enriched in several signaling pathways and Gene Ontology terms, such as p53 signaling pathway, TNF signaling pathway and mTOR signaling pathway. Six downregulated genes, including THBS1, EDN1, CACNG4, CCN2, AXIN2 and BMP4, as well as six upregulated genes, including ATF4, SERPINE1, SESN2, PPARGC1A, EGR1 and JAG1, were selected as target genes in response to OA treatment. The inhibitory effect of OA on breast cancer was also found in the following mouse experiments. Our study provides evidence and molecular support for the treatment of breast cancer with OA.

show abstract

Section: Resultsmentioning

confidence: 99%

Transcriptome study of oleanolic acid in the inhibition of breast tumor growth based on high-throughput sequencing

Liang

Pan

Xia

et al. 2021

Aging

View full text Add to dashboard Cite

show abstract

“…The up-regulation of another Notch ligand, DLL4 in tumor, suggests that it has a prime role in tumor angiogenesis. Zohny et al (2020) and Jia et al (2016) demonstrated that inhibition of DLL4 impaired vasculature development and reduced breast tumor growth along with tumor angiogenesis in DLL4 heterozygous knockout mice and in a human patient-derived xenograft (PDX) model, respectively. DLL4 is also overexpressed in breast cancer cells and is linked to nodal and distant metastasis (Kontomanolis et al 2014).…”

Section: Notch Ligands In Different Subtypes Of Breast Cancermentioning

confidence: 99%

The many facets of Notch signaling in breast cancer: toward overcoming therapeutic resistance

Nandi

Chakrabarti

2020

Genes Dev.

View full text Add to dashboard Cite

Breast cancer is the second leading cause of cancer-related death in women and is a complex disease with high intratumoral and intertumoral heterogeneity. Such heterogeneity is a major driving force behind failure of current therapies and development of resistance. Due to the limitations of conventional therapies and inevitable emergence of acquired drug resistance (chemo and endocrine) as well as radio resistance, it is essential to design novel therapeutic strategies to improve the prognosis for breast cancer patients. Deregulated Notch signaling within the breast tumor and its tumor microenvironment (TME) is linked to poor clinical outcomes in treatment of resistant breast cancer. Notch receptors and ligands are also important for normal mammary development, suggesting the potential for conserved signaling pathways between normal mammary gland development and breast cancer. In this review, we focus on mechanisms by which Notch receptors and ligands contribute to normal mammary gland development and breast tumor progression. We also discuss how complex interactions between cancer cells and the TME may reduce treatment efficacy and ultimately lead to acquired drug or radio resistance. Potential combinatorial approaches aimed at disrupting Notchand TME-mediated resistance that may aid in achieving in an improved patient prognosis are also highlighted.Breast cancer is the most prevalent cancer among women worldwide (ShahidSales et al. 2018;Ghasemi et al. 2019). Breast cancer is a highly heterogeneous disease with many subtypes, and treatment choice is based on the presence or absence of different hormone receptors, such as estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2), as well as tumor grade and age of the patient (Desmedt et al. 2008). Unfortunately, traditional treatment methods, including chemotherapy, endocrine therapy, and radiation therapy are often not curative, and only im-prove clinical outcome (Schmidberger et al. 2003;

show abstract

“…ACVRL1 expression can be used as a prognostic marker for metastatic colorectal cancer patients receiving chemotherapy and bevacizumab [36]. DLL4, as one of the main components of the Notch pathway, is reported to be highly expressed in breast cancer and associated with advanced stage and distant metastasis of the patient [37]. these studies con rm the correctness of PCDH12 SLIT3 ACVRL1 and DLL4 genes as risk factors for breast cancer prognosis.…”

Section: Discussionmentioning

confidence: 77%

Bioinformatics Analysis of Pivotal Module and Biomarkers Related to the Prognosis of Breast Cancer Based on Single-cell Transcriptome Data

Yang

Quan

et al. 2021

Preprint

View full text Add to dashboard Cite

Background: The effect of breast cancer heterogeneity on prognosis of patients is still unclear, especially the role of immune cells in prognosis of breast cancer. Therefore, the discovery of new markers to assess the effect of breast cancer heterogeneity on patient prognosis is crucial to improve prognosis and survival of patients.Methods: Single cell transcriptome sequencing data of breast cancer were downloaded from GEO database. PCA and UMAP were used for dimensionality reduction analysis and cell clustering. Find All Markers function was used to calculate differential genes in each cluster, and Do Heatmap function was used to plot the distribution of differential genes in each clusters. CellPhoneDB was used to analyze ligand-receptor interactions. TRRUST database combined with Cytoscape were used to construct a receptor-ligand-transcription factor interaction network. WGCNA is used to analyze pivotal modules associated with breast cancer prognosis. Univariate regression analysis and KM survival analysis were used to identify prognostic genes in prognostic modules. Multivariate regression analysis combined with risk scoring were used to construct a breast prognosis model, which was verified by TCGA and ICGC sample data.Result: In this study, 14 cell clusters were identified in two single-cell datasets (GSE75688 and G118389). The results of ligand receptor interaction network revealed that macrophages and DC cells were the most frequently interacting cells with other cells in breast cancer. The results of WGCNA analysis suggested that the MEblue module is most relevant to the overall survival time of triple-negative breast cancer. Twenty-four prognostic genes in the blue module were identified by univariate Cox regression analysis and KM survival analysis. Multivariate regression analysis combined with risk analysis was used to analyze 24 prognostic genes to construct a prognostic model. The verification result of our prognostic model showed that there were significant differences in the expression of PCDH12, SLIT3, ACVRL1, and DLL4 genes between the high-risk group and the low-risk group.Conclusion: PCDH12, SLIT3, ACVRL1 and DLL4 are prognostic biomarkers and relate to the type and proportion of immune cells in breast cancer.

show abstract

Highly Expressed DLL4 and JAG1: Their Role in Incidence of Breast Cancer Metastasis

Cited by 23 publications

References 28 publications

Transcriptome study of oleanolic acid in the inhibition of breast tumor growth based on high-throughput sequencing

Transcriptome study of oleanolic acid in the inhibition of breast tumor growth based on high-throughput sequencing

The many facets of Notch signaling in breast cancer: toward overcoming therapeutic resistance

Bioinformatics Analysis of Pivotal Module and Biomarkers Related to the Prognosis of Breast Cancer Based on Single-cell Transcriptome Data

Contact Info

Product

Resources

About