Computational analysis of fused co-expression networks for the identification of candidate cancer gene biomarkers

Abstract The complexity of cancer has always been a huge issue in understanding the source of this disease. However, by appreciating its complexity, we can shed some light on crucial gene associations across and in specific cancer types. In this study, we develop a general framework to infer relevan...

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Autores principales:	Sara Pidò, Gaia Ceddia, Marco Masseroli
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/ebc80e4041a64c0186c0c1816ed19791
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spelling	oai:doaj.org-article:ebc80e4041a64c0186c0c1816ed197912021-12-02T15:52:15ZComputational analysis of fused co-expression networks for the identification of candidate cancer gene biomarkers10.1038/s41540-021-00175-92056-7189https://doaj.org/article/ebc80e4041a64c0186c0c1816ed197912021-03-01T00:00:00Zhttps://doi.org/10.1038/s41540-021-00175-9https://doaj.org/toc/2056-7189Abstract The complexity of cancer has always been a huge issue in understanding the source of this disease. However, by appreciating its complexity, we can shed some light on crucial gene associations across and in specific cancer types. In this study, we develop a general framework to infer relevant gene biomarkers and their gene-to-gene associations using multiple gene co-expression networks for each cancer type. Specifically, we infer computationally and biologically interesting communities of genes from kidney renal clear cell carcinoma, liver hepatocellular carcinoma, and prostate adenocarcinoma data sets of The Cancer Genome Atlas (TCGA) database. The gene communities are extracted through a data-driven pipeline and then evaluated through both functional analyses and literature findings. Furthermore, we provide a computational validation of their relevance for each cancer type by comparing the performance of normal/cancer classification for our identified gene sets and other gene signatures, including the typically-used differentially expressed genes. The hallmark of this study is its approach based on gene co-expression networks from different similarity measures: using a combination of multiple gene networks and then fusing normal and cancer networks for each cancer type, we can have better insights on the overall structure of the cancer-type-specific network.Sara PidòGaia CeddiaMarco MasseroliNature PortfolioarticleBiology (General)QH301-705.5ENnpj Systems Biology and Applications, Vol 7, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Sara Pidò Gaia Ceddia Marco Masseroli Computational analysis of fused co-expression networks for the identification of candidate cancer gene biomarkers
description	Abstract The complexity of cancer has always been a huge issue in understanding the source of this disease. However, by appreciating its complexity, we can shed some light on crucial gene associations across and in specific cancer types. In this study, we develop a general framework to infer relevant gene biomarkers and their gene-to-gene associations using multiple gene co-expression networks for each cancer type. Specifically, we infer computationally and biologically interesting communities of genes from kidney renal clear cell carcinoma, liver hepatocellular carcinoma, and prostate adenocarcinoma data sets of The Cancer Genome Atlas (TCGA) database. The gene communities are extracted through a data-driven pipeline and then evaluated through both functional analyses and literature findings. Furthermore, we provide a computational validation of their relevance for each cancer type by comparing the performance of normal/cancer classification for our identified gene sets and other gene signatures, including the typically-used differentially expressed genes. The hallmark of this study is its approach based on gene co-expression networks from different similarity measures: using a combination of multiple gene networks and then fusing normal and cancer networks for each cancer type, we can have better insights on the overall structure of the cancer-type-specific network.
format	article
author	Sara Pidò Gaia Ceddia Marco Masseroli
author_facet	Sara Pidò Gaia Ceddia Marco Masseroli
author_sort	Sara Pidò
title	Computational analysis of fused co-expression networks for the identification of candidate cancer gene biomarkers
title_short	Computational analysis of fused co-expression networks for the identification of candidate cancer gene biomarkers
title_full	Computational analysis of fused co-expression networks for the identification of candidate cancer gene biomarkers
title_fullStr	Computational analysis of fused co-expression networks for the identification of candidate cancer gene biomarkers
title_full_unstemmed	Computational analysis of fused co-expression networks for the identification of candidate cancer gene biomarkers
title_sort	computational analysis of fused co-expression networks for the identification of candidate cancer gene biomarkers
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/ebc80e4041a64c0186c0c1816ed19791
work_keys_str_mv	AT sarapido computationalanalysisoffusedcoexpressionnetworksfortheidentificationofcandidatecancergenebiomarkers AT gaiaceddia computationalanalysisoffusedcoexpressionnetworksfortheidentificationofcandidatecancergenebiomarkers AT marcomasseroli computationalanalysisoffusedcoexpressionnetworksfortheidentificationofcandidatecancergenebiomarkers
_version_	1718385604741300224

Computational analysis of fused co-expression networks for the identification of candidate cancer gene biomarkers

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