Cytotoxicity and apoptosis induced by a plumbagin derivative in estrogen positive MCF-7 breast cancer cells
KAUST DepartmentComputational Bioscience Research Center (CBRC)
Smart Hybrid Materials (SHMs) lab
Advanced Membranes and Porous Materials Research Center
Chemical Science Program
Physical Sciences and Engineering (PSE) Division
Applied Mathematics and Computational Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Permanent link to this recordhttp://hdl.handle.net/10754/325349
MetadataShow full item record
AbstractPlumbagin [5-hydroxy- 2-methyl-1, 4-naphthaquinone] is a well-known plant derived anticancer lead compound. Several efforts have been made to synthesize its analogs and derivatives in order to increase its anticancer potential. In the present study, plumbagin and its five derivatives have been evaluated for their antiproliferative potential in one normal and four human cancer cell lines. Treatment with derivatives resulted in dose- and time-dependent inhibition of growth of various cancer cell lines. Prescreening of compounds led us to focus our further investigations on acetyl plumbagin, which showed remarkably low toxicity towards normal BJ cells and HepG2 cells. The mechanisms of apoptosis induction were determined by APOPercentage staining, caspase-3/7 activation, reactive oxygen species production and cell cycle analysis. The modulation of apoptotic genes (p53, Mdm2, NF-kB, Bad, Bax, Bcl-2 and Casp-7) was also measured using real time PCR. The positive staining using APOPercentage dye, increased caspase-3/7 activity, increased ROS production and enhanced mRNA expression of proapoptotic genes suggested that acetyl plumbagin exhibits anticancer effects on MCF-7 cells through its apoptosis-inducing property. A key highlighting point of the study is low toxicity of acetyl plumbagin towards normal BJ cells and negligible hepatotoxicity (data based on HepG2 cell line). Overall results showed that acetyl plumbagin with reduced toxicity might have the potential to be a new lead molecule for testing against estrogen positive breast cancer. 2014 Bentham Science Publishers.
CitationSagar S, Esau L, Moosa B, Khashab N, Bajic V, et al. (2014) Cytotoxicity and Apoptosis Induced by a Plumbagin Derivative in Estrogen Positive MCF-7 Breast Cancer Cells. Anti-Cancer Agents in Medicinal Chemistry 14: 170-180. doi:10.2174/18715206113136660369.
PublisherBentham Science Publishers Ltd.
PubMed Central IDPMC3894702
CollectionsComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Applied Mathematics and Computational Science Program; Physical Sciences and Engineering (PSE) Division; Chemical Science Program; Advanced Membranes and Porous Materials Research Center; Computational Bioscience Research Center (CBRC); Controlled Release and Delivery Laboratory; Articles
The following license files are associated with this item:
Except where otherwise noted, this item's license is described as This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.
- Plumbagin from a tropical pitcher plant (Nepenthes alata Blanco) induces apoptotic cell death via a p53-dependent pathway in MCF-7 human breast cancer cells.
- Authors: De U, Son JY, Jeon Y, Ha SY, Park YJ, Yoon S, Ha KT, Choi WS, Lee BM, Kim IS, Kwak JH, Kim HS
- Issue date: 2019 Jan
- The natural anticancer agent plumbagin induces potent cytotoxicity in MCF-7 human breast cancer cells by inhibiting a PI-5 kinase for ROS generation.
- Authors: Lee JH, Yeon JH, Kim H, Roh W, Chae J, Park HO, Kim DM
- Issue date: 2012
- The role of thioredoxin reductase and glutathione reductase in plumbagin-induced, reactive oxygen species-mediated apoptosis in cancer cell lines.
- Authors: Hwang GH, Ryu JM, Jeon YJ, Choi J, Han HJ, Lee YM, Lee S, Bae JS, Jung JW, Chang W, Kim LK, Jee JG, Lee MY
- Issue date: 2015 Oct 15
- Targeting Cell Necroptosis and Apoptosis Induced by Shikonin via Receptor Interacting Protein Kinases in Estrogen Receptor Positive Breast Cancer Cell Line, MCF-7.
- Authors: Shahsavari Z, Karami-Tehrani F, Salami S
- Issue date: 2018
- Plumbagin-induced apoptosis of human breast cancer cells is mediated by inactivation of NF-kappaB and Bcl-2.
- Authors: Ahmad A, Banerjee S, Wang Z, Kong D, Sarkar FH
- Issue date: 2008 Dec 15
Showing items related by title, author, creator and subject.
Cholesteryl ester transfer protein (cetp) inhibition in the treatment of cancerKaur, Mandeep; Esau, Luke E.; Sagar, Sunii (2016-09-01) [Patent]In one embodiment, the invention provides methods of treatment which use therapeutically effective amounts of Choleste ryl Ester Transfer Protein (CETP) inhibitors to treat a variety of cancers. In certain embodiments, the inhibitor is a CETP-inhibiting small molecule, CETP-inhibiting antisense oligonucleotide, CETP-inhibiting siRNA or a CETP- inhibiting antibody. Related pharmaceutical compositions, kits, diagnostics and screens are also provided.
Cholesteryl ester transfer protein (cetp) inhibition in the treatment of cancerKaur, Mandeep; Esau, Luke E.; Sagar, Sunii (2018-01-03) [Patent]In one embodiment, the invention provides methods of treatment which use therapeutically effective amounts of Choleste ryl Ester Transfer Protein (CETP) inhibitors to treat a variety of cancers. In certain embodiments, the inhibitor is a CETP-inhibiting small molecule, CETP-inhibiting antisense oligonucleotide, CETP-inhibiting siRNA or a CETP- inhibiting antibody. Related pharmaceutical compositions, kits, diagnostics and screens are also provided.
ProDis-ContSHC: Learning protein dissimilarity measures and hierarchical context coherently for protein-protein comparison in protein database retrievalWang, Jim Jing-Yan; Gao, Xin; Wang, Quanquan; Li, Yongping (BMC Bioinformatics, Springer Nature, 2012-05-08) [Article]Background: The need to retrieve or classify protein molecules using structure or sequence-based similarity measures underlies a wide range of biomedical applications. Traditional protein search methods rely on a pairwise dissimilarity/similarity measure for comparing a pair of proteins. This kind of pairwise measures suffer from the limitation of neglecting the distribution of other proteins and thus cannot satisfy the need for high accuracy of the retrieval systems. Recent work in the machine learning community has shown that exploiting the global structure of the database and learning the contextual dissimilarity/similarity measures can improve the retrieval performance significantly. However, most existing contextual dissimilarity/similarity learning algorithms work in an unsupervised manner, which does not utilize the information of the known class labels of proteins in the database.Results: In this paper, we propose a novel protein-protein dissimilarity learning algorithm, ProDis-ContSHC. ProDis-ContSHC regularizes an existing dissimilarity measure dij by considering the contextual information of the proteins. The context of a protein is defined by its neighboring proteins. The basic idea is, for a pair of proteins (i, j), if their context N (i) and N (j) is similar to each other, the two proteins should also have a high similarity. We implement this idea by regularizing dij by a factor learned from the context N (i) and N (j). Moreover, we divide the context to hierarchial sub-context and get the contextual dissimilarity vector for each protein pair. Using the class label information of the proteins, we select the relevant (a pair of proteins that has the same class labels) and irrelevant (with different labels) protein pairs, and train an SVM model to distinguish between their contextual dissimilarity vectors. The SVM model is further used to learn a supervised regularizing factor. Finally, with the new Supervised learned Dissimilarity measure, we update the Protein Hierarchial Context Coherently in an iterative algorithm--ProDis-ContSHC.We test the performance of ProDis-ContSHC on two benchmark sets, i.e., the ASTRAL 1.73 database and the FSSP/DALI database. Experimental results demonstrate that plugging our supervised contextual dissimilarity measures into the retrieval systems significantly outperforms the context-free dissimilarity/similarity measures and other unsupervised contextual dissimilarity measures that do not use the class label information.Conclusions: Using the contextual proteins with their class labels in the database, we can improve the accuracy of the pairwise dissimilarity/similarity measures dramatically for the protein retrieval tasks. In this work, for the first time, we propose the idea of supervised contextual dissimilarity learning, resulting in the ProDis-ContSHC algorithm. Among different contextual dissimilarity learning approaches that can be used to compare a pair of proteins, ProDis-ContSHC provides the highest accuracy. Finally, ProDis-ContSHC compares favorably with other methods reported in the recent literature. 2012 Wang et al.; licensee BioMed Central Ltd.