Article
Apigenin causes G(2)/M arrest associated with the modulation of p21(Cip1) and Cdc2 and activates p53-dependent apoptosis pathway in human breast cancer SK-BR-3 cells.
Plant Resources Research Institute, Duksung Women's University,Tobong-ku, Seoul, South Korea.
The Journal of nutritional biochemistry (impact factor:
4.29).
04/2009;
20(4):285-90.
DOI:10.1016/j.jnutbio.2008.03.005
Source: PubMed
-
Citations (0)
- Cited In (4)
-
Article: Bcl-2 inhibitor and apigenin worked synergistically in human malignant neuroblastoma cell lines and increased apoptosis with activation of extrinsic and intrinsic pathways.
[show abstract] [hide abstract]
ABSTRACT: Neuroblastoma is the most common extracranial solid tumor in infants and young children. Current treatments are not always effective and new therapies are needed. We examined efficacy of combination of the small molecule Bcl-2 inhibitor HA14-1 (HA) and the dietary isoflavonoid apigenin (APG) in human malignant neuroblastoma cells. Dose-response studies indicated that treatment with HA and APG for 24 h synergistically reduced cell viability in human malignant neuroblastoma SK-N-DZ, SH-SY5Y, and IMR32 cells. For further studies, we selected SK-N-DZ cells that showed the highest sensitivity following treatment with 2.5 microM HA, 100 microM APG, or combination (2.5 microM HA+100 microM APG). Wright staining showed increase in morphological features of apoptosis. Cell cycle distribution and Annexin V assay showed that combination therapy caused more apoptosis than either treatment alone. Western blotting revealed that combination therapy downregulated angiogenic factors and also induced extrinsic pathway of apoptosis with activation of caspase-8 for Bid cleavage to tBid. Alterations in Bax and Bcl-2 levels resulted in an increase in Bax:Bcl-2 ratio to activate intrinsic pathway of apoptosis with mitochondrial release of cytochrome c into the cytosol and activation of proteases. Increases in calpain and caspase-3 activities generated 145 kD spectrin break down product (SBDP) and 120 kD SBDP, respectively. Results showed that combination of HA and APG could be used for downregulation of angiogenic factors and activation of extrinsic and intrinsic pathways of apoptosis in malignant neuroblastoma cells.Biochemical and Biophysical Research Communications 09/2009; 388(4):705-10. · 2.48 Impact Factor -
Article: Pathway analysis using random forests with bivariate node-split for survival outcomes.
[show abstract] [hide abstract]
ABSTRACT: There is great interest in pathway-based methods for genomics data analysis in the research community. Although machine learning methods, such as random forests, have been developed to correlate survival outcomes with a set of genes, no study has assessed the abilities of these methods in incorporating pathway information for analyzing microarray data. In general, genes that are identified without incorporating biological knowledge are more difficult to interpret. Correlating pathway-based gene expression with survival outcomes may lead to biologically more meaningful prognosis biomarkers. Thus, a comprehensive study on how these methods perform in a pathway-based setting is warranted. In this article, we describe a pathway-based method using random forests to correlate gene expression data with survival outcomes and introduce a novel bivariate node-splitting random survival forests. The proposed method allows researchers to identify important pathways for predicting patient prognosis and time to disease progression, and discover important genes within those pathways. We compared different implementations of random forests with different split criteria and found that bivariate node-splitting random survival forests with log-rank test is among the best. We also performed simulation studies that showed random forests outperforms several other machine learning algorithms and has comparable results with a newly developed component-wise Cox boosting model. Thus, pathway-based survival analysis using machine learning tools represents a promising approach in dissecting pathways and for generating new biological hypothesis from microarray studies. R package Pwayrfsurvival is available from URL: http://www.duke.edu/~hp44/pwayrfsurvival.htm. Supplementary data are available at Bioinformatics online.Bioinformatics 11/2009; 26(2):250-8. · 5.47 Impact Factor -
Article: Cytotoxicity of apigenin on leukemia cell lines: implications for prevention and therapy.
[show abstract] [hide abstract]
ABSTRACT: Natural-food-based compounds show substantial promise for prevention and biotherapy of cancers including leukemia. In general, their mechanism of action remains unclear, hampering rational use of these compounds. Herein we show that the common dietary flavonoid apigenin has anticancer activity, but also may decrease chemotherapy sensitivity, depending on the cell type. We analyzed the molecular consequences of apigenin treatment in two types of leukemia, the myeloid and erythroid subtypes. Apigenin blocked proliferation in both lineages through cell-cycle arrest in G(2)/M phase for myeloid HL60 and G(0)/G(1) phase for erythroid TF1 cells. In both cell lines the JAK/STAT pathway was one of major targets of apigenin. Apigenin inhibited PI3K/PKB pathway in HL60 and induced caspase-dependent apoptosis. In contrast, no apoptosis was detected in TF1 cells, but initiation of autophagy was observed. The block in cell cycle and induction of autophagy observed in this erythroleukemia cell line resulted in a reduced susceptibility toward the commonly used therapeutic agent vincristine. Thus, this study shows that although apigenin is a potential chemopreventive agent due to the induction of leukemia cell-cycle arrest, caution in dietary intake of apigenin should be taken during disease as it potentially interferes with cancer treatment.Cell Death & Disease 01/2010; 1:e19. · 5.33 Impact Factor
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed.
The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual
current impact factor.
Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence
agreement may be applicable.
Keywords
100 microM apigenin
50 microM
50 microM apigenin
72 h. Apigenin
antiproliferative effects
apigenin causes cell cycle arrest
apigenin-induced accumulation
cell cycle arrest
cell cycle distribution
cell population
concentration treatment
concentrations
cyclin D
cyclin E expression
cytochrome c
human breast cancer cells
p53 downstream target
significant cell cycle arrest
SK-BR-3 cells
slight decrease