Predictive modeling of gene expression regulation

Abstract Background In-depth analysis of regulation networks of genes aberrantly expressed in cancer is essential for better understanding tumors and identifying key genes that could be therapeutically targeted. Results We developed a quantitative analysis approach to investigate the main biological...

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Autores principales:	Chiara Regondi, Maddalena Fratelli, Giovanna Damia, Federica Guffanti, Monica Ganzinelli, Matteo Matteucci, Marco Masseroli
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Gene expression regulation Regulatory network Cancer Predictive modeling Machine learning Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/44fdf15ce88a4e7ebad41d54628386a8
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spelling	oai:doaj.org-article:44fdf15ce88a4e7ebad41d54628386a82021-11-28T12:11:09ZPredictive modeling of gene expression regulation10.1186/s12859-021-04481-11471-2105https://doaj.org/article/44fdf15ce88a4e7ebad41d54628386a82021-11-01T00:00:00Zhttps://doi.org/10.1186/s12859-021-04481-1https://doaj.org/toc/1471-2105Abstract Background In-depth analysis of regulation networks of genes aberrantly expressed in cancer is essential for better understanding tumors and identifying key genes that could be therapeutically targeted. Results We developed a quantitative analysis approach to investigate the main biological relationships among different regulatory elements and target genes; we applied it to Ovarian Serous Cystadenocarcinoma and 177 target genes belonging to three main pathways (DNA REPAIR, STEM CELLS and GLUCOSE METABOLISM) relevant for this tumor. Combining data from ENCODE and TCGA datasets, we built a predictive linear model for the regulation of each target gene, assessing the relationships between its expression, promoter methylation, expression of genes in the same or in the other pathways and of putative transcription factors. We proved the reliability and significance of our approach in a similar tumor type (basal-like Breast cancer) and using a different existing algorithm (ARACNe), and we obtained experimental confirmations on potentially interesting results. Conclusions The analysis of the proposed models allowed disclosing the relations between a gene and its related biological processes, the interconnections between the different gene sets, and the evaluation of the relevant regulatory elements at single gene level. This led to the identification of already known regulators and/or gene correlations and to unveil a set of still unknown and potentially interesting biological relationships for their pharmacological and clinical use.Chiara RegondiMaddalena FratelliGiovanna DamiaFederica GuffantiMonica GanzinelliMatteo MatteucciMarco MasseroliBMCarticleGene expression regulationRegulatory networkCancerPredictive modelingMachine learningComputer applications to medicine. Medical informaticsR858-859.7Biology (General)QH301-705.5ENBMC Bioinformatics, Vol 22, Iss 1, Pp 1-21 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Gene expression regulation Regulatory network Cancer Predictive modeling Machine learning Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5
spellingShingle	Gene expression regulation Regulatory network Cancer Predictive modeling Machine learning Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5 Chiara Regondi Maddalena Fratelli Giovanna Damia Federica Guffanti Monica Ganzinelli Matteo Matteucci Marco Masseroli Predictive modeling of gene expression regulation
description	Abstract Background In-depth analysis of regulation networks of genes aberrantly expressed in cancer is essential for better understanding tumors and identifying key genes that could be therapeutically targeted. Results We developed a quantitative analysis approach to investigate the main biological relationships among different regulatory elements and target genes; we applied it to Ovarian Serous Cystadenocarcinoma and 177 target genes belonging to three main pathways (DNA REPAIR, STEM CELLS and GLUCOSE METABOLISM) relevant for this tumor. Combining data from ENCODE and TCGA datasets, we built a predictive linear model for the regulation of each target gene, assessing the relationships between its expression, promoter methylation, expression of genes in the same or in the other pathways and of putative transcription factors. We proved the reliability and significance of our approach in a similar tumor type (basal-like Breast cancer) and using a different existing algorithm (ARACNe), and we obtained experimental confirmations on potentially interesting results. Conclusions The analysis of the proposed models allowed disclosing the relations between a gene and its related biological processes, the interconnections between the different gene sets, and the evaluation of the relevant regulatory elements at single gene level. This led to the identification of already known regulators and/or gene correlations and to unveil a set of still unknown and potentially interesting biological relationships for their pharmacological and clinical use.
format	article
author	Chiara Regondi Maddalena Fratelli Giovanna Damia Federica Guffanti Monica Ganzinelli Matteo Matteucci Marco Masseroli
author_facet	Chiara Regondi Maddalena Fratelli Giovanna Damia Federica Guffanti Monica Ganzinelli Matteo Matteucci Marco Masseroli
author_sort	Chiara Regondi
title	Predictive modeling of gene expression regulation
title_short	Predictive modeling of gene expression regulation
title_full	Predictive modeling of gene expression regulation
title_fullStr	Predictive modeling of gene expression regulation
title_full_unstemmed	Predictive modeling of gene expression regulation
title_sort	predictive modeling of gene expression regulation
publisher	BMC
publishDate	2021
url	https://doaj.org/article/44fdf15ce88a4e7ebad41d54628386a8
work_keys_str_mv	AT chiararegondi predictivemodelingofgeneexpressionregulation AT maddalenafratelli predictivemodelingofgeneexpressionregulation AT giovannadamia predictivemodelingofgeneexpressionregulation AT federicaguffanti predictivemodelingofgeneexpressionregulation AT monicaganzinelli predictivemodelingofgeneexpressionregulation AT matteomatteucci predictivemodelingofgeneexpressionregulation AT marcomasseroli predictivemodelingofgeneexpressionregulation
_version_	1718408135345963008

Predictive modeling of gene expression regulation

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