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Large-scale gene network analysis reveals the significance of extracellular matrix pathway and homeobox genes in acute myeloid leukemia: an introduction to the Pigengene package and its applications

BACKGROUND: The distinct types of hematological malignancies have different biological mechanisms and prognoses. For instance, myelodysplastic syndrome (MDS) is generally indolent and low risk; however, it may transform into acute myeloid leukemia (AML), which is much more aggressive. METHODS: We de...

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Autores principales: Foroushani, Amir, Agrahari, Rupesh, Docking, Roderick, Chang, Linda, Duns, Gerben, Hudoba, Monika, Karsan, Aly, Zare, Habil
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5353782/
https://www.ncbi.nlm.nih.gov/pubmed/28298217
http://dx.doi.org/10.1186/s12920-017-0253-6
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author Foroushani, Amir
Agrahari, Rupesh
Docking, Roderick
Chang, Linda
Duns, Gerben
Hudoba, Monika
Karsan, Aly
Zare, Habil
author_facet Foroushani, Amir
Agrahari, Rupesh
Docking, Roderick
Chang, Linda
Duns, Gerben
Hudoba, Monika
Karsan, Aly
Zare, Habil
author_sort Foroushani, Amir
collection PubMed
description BACKGROUND: The distinct types of hematological malignancies have different biological mechanisms and prognoses. For instance, myelodysplastic syndrome (MDS) is generally indolent and low risk; however, it may transform into acute myeloid leukemia (AML), which is much more aggressive. METHODS: We develop a novel network analysis approach that uses expression of eigengenes to delineate the biological differences between these two diseases. RESULTS: We find that specific genes in the extracellular matrix pathway are underexpressed in AML. We validate this finding in three ways: (a) We train our model on a microarray dataset of 364 cases and test it on an RNA Seq dataset of 74 cases. Our model showed 95% sensitivity and 86% specificity in the training dataset and showed 98% sensitivity and 91% specificity in the test dataset. This confirms that the identified biological signatures are independent from the expression profiling technology and independent from the training dataset. (b) Immunocytochemistry confirms that MMP9, an exemplar protein in the extracellular matrix, is underexpressed in AML. (c) MMP9 is hypermethylated in the majority of AML cases (n=194, Welch’s t-test p-value <10(−138)), which complies with its low expression in AML. Our novel network analysis approach is generalizable and useful in studying other complex diseases (e.g., breast cancer prognosis). We implement our methodology in the Pigengene software package, which is publicly available through Bioconductor. CONCLUSIONS: Eigengenes define informative biological signatures that are robust with respect to expression profiling technology. These signatures provide valuable information about the underlying biology of diseases, and they are useful in predicting diagnosis and prognosis. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12920-017-0253-6) contains supplementary material, which is available to authorized users.
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spelling pubmed-53537822017-03-22 Large-scale gene network analysis reveals the significance of extracellular matrix pathway and homeobox genes in acute myeloid leukemia: an introduction to the Pigengene package and its applications Foroushani, Amir Agrahari, Rupesh Docking, Roderick Chang, Linda Duns, Gerben Hudoba, Monika Karsan, Aly Zare, Habil BMC Med Genomics Research Article BACKGROUND: The distinct types of hematological malignancies have different biological mechanisms and prognoses. For instance, myelodysplastic syndrome (MDS) is generally indolent and low risk; however, it may transform into acute myeloid leukemia (AML), which is much more aggressive. METHODS: We develop a novel network analysis approach that uses expression of eigengenes to delineate the biological differences between these two diseases. RESULTS: We find that specific genes in the extracellular matrix pathway are underexpressed in AML. We validate this finding in three ways: (a) We train our model on a microarray dataset of 364 cases and test it on an RNA Seq dataset of 74 cases. Our model showed 95% sensitivity and 86% specificity in the training dataset and showed 98% sensitivity and 91% specificity in the test dataset. This confirms that the identified biological signatures are independent from the expression profiling technology and independent from the training dataset. (b) Immunocytochemistry confirms that MMP9, an exemplar protein in the extracellular matrix, is underexpressed in AML. (c) MMP9 is hypermethylated in the majority of AML cases (n=194, Welch’s t-test p-value <10(−138)), which complies with its low expression in AML. Our novel network analysis approach is generalizable and useful in studying other complex diseases (e.g., breast cancer prognosis). We implement our methodology in the Pigengene software package, which is publicly available through Bioconductor. CONCLUSIONS: Eigengenes define informative biological signatures that are robust with respect to expression profiling technology. These signatures provide valuable information about the underlying biology of diseases, and they are useful in predicting diagnosis and prognosis. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12920-017-0253-6) contains supplementary material, which is available to authorized users. BioMed Central 2017-03-16 /pmc/articles/PMC5353782/ /pubmed/28298217 http://dx.doi.org/10.1186/s12920-017-0253-6 Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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.
spellingShingle Research Article
Foroushani, Amir
Agrahari, Rupesh
Docking, Roderick
Chang, Linda
Duns, Gerben
Hudoba, Monika
Karsan, Aly
Zare, Habil
Large-scale gene network analysis reveals the significance of extracellular matrix pathway and homeobox genes in acute myeloid leukemia: an introduction to the Pigengene package and its applications
title Large-scale gene network analysis reveals the significance of extracellular matrix pathway and homeobox genes in acute myeloid leukemia: an introduction to the Pigengene package and its applications
title_full Large-scale gene network analysis reveals the significance of extracellular matrix pathway and homeobox genes in acute myeloid leukemia: an introduction to the Pigengene package and its applications
title_fullStr Large-scale gene network analysis reveals the significance of extracellular matrix pathway and homeobox genes in acute myeloid leukemia: an introduction to the Pigengene package and its applications
title_full_unstemmed Large-scale gene network analysis reveals the significance of extracellular matrix pathway and homeobox genes in acute myeloid leukemia: an introduction to the Pigengene package and its applications
title_short Large-scale gene network analysis reveals the significance of extracellular matrix pathway and homeobox genes in acute myeloid leukemia: an introduction to the Pigengene package and its applications
title_sort large-scale gene network analysis reveals the significance of extracellular matrix pathway and homeobox genes in acute myeloid leukemia: an introduction to the pigengene package and its applications
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5353782/
https://www.ncbi.nlm.nih.gov/pubmed/28298217
http://dx.doi.org/10.1186/s12920-017-0253-6
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