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In silico ranking of phenolics for therapeutic effectiveness on cancer stem cells

BACKGROUND: Cancer stem cells (CSCs) have features such as the ability to self-renew, differentiate into defined progenies and initiate the tumor growth. Treatments of cancer include drugs, chemotherapy and radiotherapy or a combination. However, treatment of cancer by various therapeutic strategies...

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Autores principales: Mandal, Monalisa, Sahoo, Sanjeeb Kumar, Patra, Priyadarsan, Mallik, Saurav, Zhao, Zhongming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7768647/
https://www.ncbi.nlm.nih.gov/pubmed/33371879
http://dx.doi.org/10.1186/s12859-020-03849-z
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author Mandal, Monalisa
Sahoo, Sanjeeb Kumar
Patra, Priyadarsan
Mallik, Saurav
Zhao, Zhongming
author_facet Mandal, Monalisa
Sahoo, Sanjeeb Kumar
Patra, Priyadarsan
Mallik, Saurav
Zhao, Zhongming
author_sort Mandal, Monalisa
collection PubMed
description BACKGROUND: Cancer stem cells (CSCs) have features such as the ability to self-renew, differentiate into defined progenies and initiate the tumor growth. Treatments of cancer include drugs, chemotherapy and radiotherapy or a combination. However, treatment of cancer by various therapeutic strategies often fail. One possible reason is that the nature of CSCs, which has stem-like properties, make it more dynamic and complex and may cause the therapeutic resistance. Another limitation is the side effects associated with the treatment of chemotherapy or radiotherapy. To explore better or alternative treatment options the current study aims to investigate the natural drug-like molecules that can be used as CSC-targeted therapy. Among various natural products, anticancer potential of phenolics is well established. We collected the 21 phytochemicals from phenolic group and their interacting CSC genes from the publicly available databases. Then a bipartite graph is constructed from the collected CSC genes along with their interacting phytochemicals from phenolic group as other. The bipartite graph is then transformed into weighted bipartite graph by considering the interaction strength between the phenolics and the CSC genes. The CSC genes are also weighted by two scores, namely, DSI (Disease Specificity Index) and DPI (Disease Pleiotropy Index). For each gene, its DSI score reflects the specific relationship with the disease and DPI score reflects the association with multiple diseases. Finally, a ranking technique is developed based on PageRank (PR) algorithm for ranking the phenolics. RESULTS: We collected 21 phytochemicals from phenolic group and 1118 CSC genes. The top ranked phenolics were evaluated by their molecular and pharmacokinetics properties and disease association networks. We selected top five ranked phenolics (Resveratrol, Curcumin, Quercetin, Epigallocatechin Gallate, and Genistein) for further examination of their oral bioavailability through molecular properties, drug likeness through pharmacokinetic properties, and associated network with CSC genes. CONCLUSION: Our PR ranking based approach is useful to rank the phenolics that are associated with CSC genes. Our results suggested some phenolics are potential molecules for CSC-related cancer treatment.
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spelling pubmed-77686472020-12-29 In silico ranking of phenolics for therapeutic effectiveness on cancer stem cells Mandal, Monalisa Sahoo, Sanjeeb Kumar Patra, Priyadarsan Mallik, Saurav Zhao, Zhongming BMC Bioinformatics Research BACKGROUND: Cancer stem cells (CSCs) have features such as the ability to self-renew, differentiate into defined progenies and initiate the tumor growth. Treatments of cancer include drugs, chemotherapy and radiotherapy or a combination. However, treatment of cancer by various therapeutic strategies often fail. One possible reason is that the nature of CSCs, which has stem-like properties, make it more dynamic and complex and may cause the therapeutic resistance. Another limitation is the side effects associated with the treatment of chemotherapy or radiotherapy. To explore better or alternative treatment options the current study aims to investigate the natural drug-like molecules that can be used as CSC-targeted therapy. Among various natural products, anticancer potential of phenolics is well established. We collected the 21 phytochemicals from phenolic group and their interacting CSC genes from the publicly available databases. Then a bipartite graph is constructed from the collected CSC genes along with their interacting phytochemicals from phenolic group as other. The bipartite graph is then transformed into weighted bipartite graph by considering the interaction strength between the phenolics and the CSC genes. The CSC genes are also weighted by two scores, namely, DSI (Disease Specificity Index) and DPI (Disease Pleiotropy Index). For each gene, its DSI score reflects the specific relationship with the disease and DPI score reflects the association with multiple diseases. Finally, a ranking technique is developed based on PageRank (PR) algorithm for ranking the phenolics. RESULTS: We collected 21 phytochemicals from phenolic group and 1118 CSC genes. The top ranked phenolics were evaluated by their molecular and pharmacokinetics properties and disease association networks. We selected top five ranked phenolics (Resveratrol, Curcumin, Quercetin, Epigallocatechin Gallate, and Genistein) for further examination of their oral bioavailability through molecular properties, drug likeness through pharmacokinetic properties, and associated network with CSC genes. CONCLUSION: Our PR ranking based approach is useful to rank the phenolics that are associated with CSC genes. Our results suggested some phenolics are potential molecules for CSC-related cancer treatment. BioMed Central 2020-12-28 /pmc/articles/PMC7768647/ /pubmed/33371879 http://dx.doi.org/10.1186/s12859-020-03849-z Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Mandal, Monalisa
Sahoo, Sanjeeb Kumar
Patra, Priyadarsan
Mallik, Saurav
Zhao, Zhongming
In silico ranking of phenolics for therapeutic effectiveness on cancer stem cells
title In silico ranking of phenolics for therapeutic effectiveness on cancer stem cells
title_full In silico ranking of phenolics for therapeutic effectiveness on cancer stem cells
title_fullStr In silico ranking of phenolics for therapeutic effectiveness on cancer stem cells
title_full_unstemmed In silico ranking of phenolics for therapeutic effectiveness on cancer stem cells
title_short In silico ranking of phenolics for therapeutic effectiveness on cancer stem cells
title_sort in silico ranking of phenolics for therapeutic effectiveness on cancer stem cells
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7768647/
https://www.ncbi.nlm.nih.gov/pubmed/33371879
http://dx.doi.org/10.1186/s12859-020-03849-z
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