Prediction of potential cancer-risk regions based on transcriptome data: towards a comprehensive view.

A novel integrative pipeline is presented for discovery of potential cancer-susceptibility regions (PCSRs) by calculating the number of altered genes at each chromosomal region, using expression microarray datasets of different human cancers (HCs). Our novel approach comprises primarily predicting P...

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Autores principales:	Arghavan Alisoltani, Hossein Fallahi, Mahdi Ebrahimi, Mansour Ebrahimi, Esmaeil Ebrahimie
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c6ef6a313d1a4fada50df6d64ae70d2a
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spelling	oai:doaj.org-article:c6ef6a313d1a4fada50df6d64ae70d2a2021-11-18T08:20:53ZPrediction of potential cancer-risk regions based on transcriptome data: towards a comprehensive view.1932-620310.1371/journal.pone.0096320https://doaj.org/article/c6ef6a313d1a4fada50df6d64ae70d2a2014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24796549/?tool=EBIhttps://doaj.org/toc/1932-6203A novel integrative pipeline is presented for discovery of potential cancer-susceptibility regions (PCSRs) by calculating the number of altered genes at each chromosomal region, using expression microarray datasets of different human cancers (HCs). Our novel approach comprises primarily predicting PCSRs followed by identification of key genes in these regions to obtain potential regions harboring new cancer-associated variants. In addition to finding new cancer causal variants, another advantage in prediction of such risk regions is simultaneous study of different types of genomic variants in line with focusing on specific chromosomal regions. Using this pipeline we extracted numbers of regions with highly altered expression levels in cancer condition. Regulatory networks were also constructed for different types of cancers following the identification of altered mRNA and microRNAs. Interestingly, results showed that GAPDH, LIFR, ZEB2, mir-21, mir-30a, mir-141 and mir-200c, all located at PCSRs, are common altered factors in constructed networks. We found a number of clusters of altered mRNAs and miRNAs on predicted PCSRs (e.g.12p13.31) and their common regulators including KLF4 and SOX10. Large scale prediction of risk regions based on transcriptome data can open a window in comprehensive study of cancer risk factors and the other human diseases.Arghavan AlisoltaniHossein FallahiMahdi EbrahimiMansour EbrahimiEsmaeil EbrahimiePublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 5, p e96320 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Arghavan Alisoltani Hossein Fallahi Mahdi Ebrahimi Mansour Ebrahimi Esmaeil Ebrahimie Prediction of potential cancer-risk regions based on transcriptome data: towards a comprehensive view.
description	A novel integrative pipeline is presented for discovery of potential cancer-susceptibility regions (PCSRs) by calculating the number of altered genes at each chromosomal region, using expression microarray datasets of different human cancers (HCs). Our novel approach comprises primarily predicting PCSRs followed by identification of key genes in these regions to obtain potential regions harboring new cancer-associated variants. In addition to finding new cancer causal variants, another advantage in prediction of such risk regions is simultaneous study of different types of genomic variants in line with focusing on specific chromosomal regions. Using this pipeline we extracted numbers of regions with highly altered expression levels in cancer condition. Regulatory networks were also constructed for different types of cancers following the identification of altered mRNA and microRNAs. Interestingly, results showed that GAPDH, LIFR, ZEB2, mir-21, mir-30a, mir-141 and mir-200c, all located at PCSRs, are common altered factors in constructed networks. We found a number of clusters of altered mRNAs and miRNAs on predicted PCSRs (e.g.12p13.31) and their common regulators including KLF4 and SOX10. Large scale prediction of risk regions based on transcriptome data can open a window in comprehensive study of cancer risk factors and the other human diseases.
format	article
author	Arghavan Alisoltani Hossein Fallahi Mahdi Ebrahimi Mansour Ebrahimi Esmaeil Ebrahimie
author_facet	Arghavan Alisoltani Hossein Fallahi Mahdi Ebrahimi Mansour Ebrahimi Esmaeil Ebrahimie
author_sort	Arghavan Alisoltani
title	Prediction of potential cancer-risk regions based on transcriptome data: towards a comprehensive view.
title_short	Prediction of potential cancer-risk regions based on transcriptome data: towards a comprehensive view.
title_full	Prediction of potential cancer-risk regions based on transcriptome data: towards a comprehensive view.
title_fullStr	Prediction of potential cancer-risk regions based on transcriptome data: towards a comprehensive view.
title_full_unstemmed	Prediction of potential cancer-risk regions based on transcriptome data: towards a comprehensive view.
title_sort	prediction of potential cancer-risk regions based on transcriptome data: towards a comprehensive view.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/c6ef6a313d1a4fada50df6d64ae70d2a
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Prediction of potential cancer-risk regions based on transcriptome data: towards a comprehensive view.

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