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Identification of potential transcriptomic markers in developing pediatric sepsis: a weighted gene co-expression network analysis and a case–control validation study

BACKGROUND: Sepsis represents a complex disease with the dysregulated inflammatory response and high mortality rate. The goal of this study was to identify potential transcriptomic markers in developing pediatric sepsis by a co-expression module analysis of the transcriptomic dataset. METHODS: Using...

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Autores principales: Li, Yiping, Li, Yanhong, Bai, Zhenjiang, Pan, Jian, Wang, Jian, Fang, Fang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5729245/
https://www.ncbi.nlm.nih.gov/pubmed/29237456
http://dx.doi.org/10.1186/s12967-017-1364-8
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author Li, Yiping
Li, Yanhong
Bai, Zhenjiang
Pan, Jian
Wang, Jian
Fang, Fang
author_facet Li, Yiping
Li, Yanhong
Bai, Zhenjiang
Pan, Jian
Wang, Jian
Fang, Fang
author_sort Li, Yiping
collection PubMed
description BACKGROUND: Sepsis represents a complex disease with the dysregulated inflammatory response and high mortality rate. The goal of this study was to identify potential transcriptomic markers in developing pediatric sepsis by a co-expression module analysis of the transcriptomic dataset. METHODS: Using the R software and Bioconductor packages, we performed a weighted gene co-expression network analysis to identify co-expression modules significantly associated with pediatric sepsis. Functional interpretation (gene ontology and pathway analysis) and enrichment analysis with known transcription factors and microRNAs of the identified candidate modules were then performed. In modules significantly associated with sepsis, the intramodular analysis was further performed and “hub genes” were identified and validated by quantitative real-time PCR (qPCR) in this study. RESULTS: 15 co-expression modules in total were detected, and four modules (“midnight blue”, “cyan”, “brown”, and “tan”) were most significantly associated with pediatric sepsis and suggested as potential sepsis-associated modules. Gene ontology analysis and pathway analysis revealed that these four modules strongly associated with immune response. Three of the four sepsis-associated modules were also enriched with known transcription factors (false discovery rate-adjusted P < 0.05). Hub genes were identified in each of the four modules. Four of the identified hub genes (MYB proto-oncogene like 1, killer cell lectin like receptor G1, stomatin, and membrane spanning 4-domains A4A) were further validated to be differentially expressed between septic children and controls by qPCR. CONCLUSIONS: Four pediatric sepsis-associated co-expression modules were identified in this study. qPCR results suggest that hub genes in these modules are potential transcriptomic markers for pediatric sepsis diagnosis. These results provide novel insights into the pathogenesis of pediatric sepsis and promote the generation of diagnostic gene sets. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12967-017-1364-8) contains supplementary material, which is available to authorized users.
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spelling pubmed-57292452017-12-18 Identification of potential transcriptomic markers in developing pediatric sepsis: a weighted gene co-expression network analysis and a case–control validation study Li, Yiping Li, Yanhong Bai, Zhenjiang Pan, Jian Wang, Jian Fang, Fang J Transl Med Research BACKGROUND: Sepsis represents a complex disease with the dysregulated inflammatory response and high mortality rate. The goal of this study was to identify potential transcriptomic markers in developing pediatric sepsis by a co-expression module analysis of the transcriptomic dataset. METHODS: Using the R software and Bioconductor packages, we performed a weighted gene co-expression network analysis to identify co-expression modules significantly associated with pediatric sepsis. Functional interpretation (gene ontology and pathway analysis) and enrichment analysis with known transcription factors and microRNAs of the identified candidate modules were then performed. In modules significantly associated with sepsis, the intramodular analysis was further performed and “hub genes” were identified and validated by quantitative real-time PCR (qPCR) in this study. RESULTS: 15 co-expression modules in total were detected, and four modules (“midnight blue”, “cyan”, “brown”, and “tan”) were most significantly associated with pediatric sepsis and suggested as potential sepsis-associated modules. Gene ontology analysis and pathway analysis revealed that these four modules strongly associated with immune response. Three of the four sepsis-associated modules were also enriched with known transcription factors (false discovery rate-adjusted P < 0.05). Hub genes were identified in each of the four modules. Four of the identified hub genes (MYB proto-oncogene like 1, killer cell lectin like receptor G1, stomatin, and membrane spanning 4-domains A4A) were further validated to be differentially expressed between septic children and controls by qPCR. CONCLUSIONS: Four pediatric sepsis-associated co-expression modules were identified in this study. qPCR results suggest that hub genes in these modules are potential transcriptomic markers for pediatric sepsis diagnosis. These results provide novel insights into the pathogenesis of pediatric sepsis and promote the generation of diagnostic gene sets. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12967-017-1364-8) contains supplementary material, which is available to authorized users. BioMed Central 2017-12-13 /pmc/articles/PMC5729245/ /pubmed/29237456 http://dx.doi.org/10.1186/s12967-017-1364-8 Text en © The Author(s) 2017 Open AccessThis 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
Li, Yiping
Li, Yanhong
Bai, Zhenjiang
Pan, Jian
Wang, Jian
Fang, Fang
Identification of potential transcriptomic markers in developing pediatric sepsis: a weighted gene co-expression network analysis and a case–control validation study
title Identification of potential transcriptomic markers in developing pediatric sepsis: a weighted gene co-expression network analysis and a case–control validation study
title_full Identification of potential transcriptomic markers in developing pediatric sepsis: a weighted gene co-expression network analysis and a case–control validation study
title_fullStr Identification of potential transcriptomic markers in developing pediatric sepsis: a weighted gene co-expression network analysis and a case–control validation study
title_full_unstemmed Identification of potential transcriptomic markers in developing pediatric sepsis: a weighted gene co-expression network analysis and a case–control validation study
title_short Identification of potential transcriptomic markers in developing pediatric sepsis: a weighted gene co-expression network analysis and a case–control validation study
title_sort identification of potential transcriptomic markers in developing pediatric sepsis: a weighted gene co-expression network analysis and a case–control validation study
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5729245/
https://www.ncbi.nlm.nih.gov/pubmed/29237456
http://dx.doi.org/10.1186/s12967-017-1364-8
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