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Prediction of optimal gene functions for osteosarcoma using gene ontology and microarray profiles

In the current study, we planned to predict the optimal gene functions for osteosarcoma (OS) by integrating network-based method with guilt by association (GBA) principle (called as network-based gene function inference approach) based on gene ontology (GO) data and gene expression profile. To begin...

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Autor principal: Chen, Xinrang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5396855/
https://www.ncbi.nlm.nih.gov/pubmed/28443230
http://dx.doi.org/10.1016/j.jbo.2017.04.003
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author Chen, Xinrang
author_facet Chen, Xinrang
author_sort Chen, Xinrang
collection PubMed
description In the current study, we planned to predict the optimal gene functions for osteosarcoma (OS) by integrating network-based method with guilt by association (GBA) principle (called as network-based gene function inference approach) based on gene ontology (GO) data and gene expression profile. To begin with, differentially expressed genes (DEGs) were extracted using linear models for microarray data (LIMMA) package. Then, construction of differential co-expression network (DCN) relying on DEGs was implemented, and sub-DCN was identified using Spearman correlation coefficient (SCC). Subsequently, GO annotations for OS were collected according to known confirmed database and DEGs. Ultimately, gene functions were predicted by means of GBA principle based on the area under the curve (AUC) for GO terms, and we determined GO terms with AUC >0.7 as the optimal gene functions for OS. Totally, 123 DEGs and 137 GO terms were obtained for further analysis. A DCN was constructed, which included 123 DEGs and 7503 interactions. A total of 105 GO terms were identified when the threshold was set as AUC >0.5, which had a good classification performance. Among these 105 GO terms, 2 functions had the AUC >0.7 and were determined as the optimal gene functions including angiogenesis (AUC =0.767) and regulation of immune system process (AUC =0.710). These gene functions appear to have potential for early detection and clinical treatment of OS in the future.
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spelling pubmed-53968552017-04-25 Prediction of optimal gene functions for osteosarcoma using gene ontology and microarray profiles Chen, Xinrang J Bone Oncol Research Paper In the current study, we planned to predict the optimal gene functions for osteosarcoma (OS) by integrating network-based method with guilt by association (GBA) principle (called as network-based gene function inference approach) based on gene ontology (GO) data and gene expression profile. To begin with, differentially expressed genes (DEGs) were extracted using linear models for microarray data (LIMMA) package. Then, construction of differential co-expression network (DCN) relying on DEGs was implemented, and sub-DCN was identified using Spearman correlation coefficient (SCC). Subsequently, GO annotations for OS were collected according to known confirmed database and DEGs. Ultimately, gene functions were predicted by means of GBA principle based on the area under the curve (AUC) for GO terms, and we determined GO terms with AUC >0.7 as the optimal gene functions for OS. Totally, 123 DEGs and 137 GO terms were obtained for further analysis. A DCN was constructed, which included 123 DEGs and 7503 interactions. A total of 105 GO terms were identified when the threshold was set as AUC >0.5, which had a good classification performance. Among these 105 GO terms, 2 functions had the AUC >0.7 and were determined as the optimal gene functions including angiogenesis (AUC =0.767) and regulation of immune system process (AUC =0.710). These gene functions appear to have potential for early detection and clinical treatment of OS in the future. Elsevier 2017-04-08 /pmc/articles/PMC5396855/ /pubmed/28443230 http://dx.doi.org/10.1016/j.jbo.2017.04.003 Text en © 2017 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Paper
Chen, Xinrang
Prediction of optimal gene functions for osteosarcoma using gene ontology and microarray profiles
title Prediction of optimal gene functions for osteosarcoma using gene ontology and microarray profiles
title_full Prediction of optimal gene functions for osteosarcoma using gene ontology and microarray profiles
title_fullStr Prediction of optimal gene functions for osteosarcoma using gene ontology and microarray profiles
title_full_unstemmed Prediction of optimal gene functions for osteosarcoma using gene ontology and microarray profiles
title_short Prediction of optimal gene functions for osteosarcoma using gene ontology and microarray profiles
title_sort prediction of optimal gene functions for osteosarcoma using gene ontology and microarray profiles
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5396855/
https://www.ncbi.nlm.nih.gov/pubmed/28443230
http://dx.doi.org/10.1016/j.jbo.2017.04.003
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