Predicting pathway cross-talks in ankylosing spondylitis through investigating the interactions among pathways

Detalhes bibliográficos
Autor(a) principal: Gu,Xiang
Data de Publicação: 2018
Outros Autores: Liu,Cong-Jian, Wei,Jian-Jie
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Brazilian Journal of Medical and Biological Research
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-879X2018000100607
Resumo: Given that the pathogenesis of ankylosing spondylitis (AS) remains unclear, the aim of this study was to detect the potentially functional pathway cross-talk in AS to further reveal the pathogenesis of this disease. Using microarray profile of AS and biological pathways as study objects, Monte Carlo cross-validation method was used to identify the significant pathway cross-talks. In the process of Monte Carlo cross-validation, all steps were iterated 50 times. For each run, detection of differentially expressed genes (DEGs) between two groups was conducted. The extraction of the potential disrupted pathways enriched by DEGs was then implemented. Subsequently, we established a discriminating score (DS) for each pathway pair according to the distribution of gene expression levels. After that, we utilized random forest (RF) classification model to screen out the top 10 paired pathways with the highest area under the curve (AUCs), which was computed using 10-fold cross-validation approach. After 50 bootstrap, the best pairs of pathways were identified. According to their AUC values, the pair of pathways, antigen presentation pathway and fMLP signaling in neutrophils, achieved the best AUC value of 1.000, which indicated that this pathway cross-talk could distinguish AS patients from normal subjects. Moreover, the paired pathways of SAPK/JNK signaling and mitochondrial dysfunction were involved in 5 bootstraps. Two paired pathways (antigen presentation pathway and fMLP signaling in neutrophil, as well as SAPK/JNK signaling and mitochondrial dysfunction) can accurately distinguish AS and control samples. These paired pathways may be helpful to identify patients with AS for early intervention.
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spelling Predicting pathway cross-talks in ankylosing spondylitis through investigating the interactions among pathwaysAnkylosing spondylitisMonte Carlo cross-validationDifferentially expressed genesRandom forest classificationPathway cross-talkGiven that the pathogenesis of ankylosing spondylitis (AS) remains unclear, the aim of this study was to detect the potentially functional pathway cross-talk in AS to further reveal the pathogenesis of this disease. Using microarray profile of AS and biological pathways as study objects, Monte Carlo cross-validation method was used to identify the significant pathway cross-talks. In the process of Monte Carlo cross-validation, all steps were iterated 50 times. For each run, detection of differentially expressed genes (DEGs) between two groups was conducted. The extraction of the potential disrupted pathways enriched by DEGs was then implemented. Subsequently, we established a discriminating score (DS) for each pathway pair according to the distribution of gene expression levels. After that, we utilized random forest (RF) classification model to screen out the top 10 paired pathways with the highest area under the curve (AUCs), which was computed using 10-fold cross-validation approach. After 50 bootstrap, the best pairs of pathways were identified. According to their AUC values, the pair of pathways, antigen presentation pathway and fMLP signaling in neutrophils, achieved the best AUC value of 1.000, which indicated that this pathway cross-talk could distinguish AS patients from normal subjects. Moreover, the paired pathways of SAPK/JNK signaling and mitochondrial dysfunction were involved in 5 bootstraps. Two paired pathways (antigen presentation pathway and fMLP signaling in neutrophil, as well as SAPK/JNK signaling and mitochondrial dysfunction) can accurately distinguish AS and control samples. These paired pathways may be helpful to identify patients with AS for early intervention.Associação Brasileira de Divulgação Científica2018-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-879X2018000100607Brazilian Journal of Medical and Biological Research v.51 n.1 2018reponame:Brazilian Journal of Medical and Biological Researchinstname:Associação Brasileira de Divulgação Científica (ABDC)instacron:ABDC10.1590/1414-431x20176698info:eu-repo/semantics/openAccessGu,XiangLiu,Cong-JianWei,Jian-Jieeng2019-03-19T00:00:00Zoai:scielo:S0100-879X2018000100607Revistahttps://www.bjournal.org/https://old.scielo.br/oai/scielo-oai.phpbjournal@terra.com.br||bjournal@terra.com.br1414-431X0100-879Xopendoar:2019-03-19T00:00Brazilian Journal of Medical and Biological Research - Associação Brasileira de Divulgação Científica (ABDC)false
dc.title.none.fl_str_mv Predicting pathway cross-talks in ankylosing spondylitis through investigating the interactions among pathways
title Predicting pathway cross-talks in ankylosing spondylitis through investigating the interactions among pathways
spellingShingle Predicting pathway cross-talks in ankylosing spondylitis through investigating the interactions among pathways
Gu,Xiang
Ankylosing spondylitis
Monte Carlo cross-validation
Differentially expressed genes
Random forest classification
Pathway cross-talk
title_short Predicting pathway cross-talks in ankylosing spondylitis through investigating the interactions among pathways
title_full Predicting pathway cross-talks in ankylosing spondylitis through investigating the interactions among pathways
title_fullStr Predicting pathway cross-talks in ankylosing spondylitis through investigating the interactions among pathways
title_full_unstemmed Predicting pathway cross-talks in ankylosing spondylitis through investigating the interactions among pathways
title_sort Predicting pathway cross-talks in ankylosing spondylitis through investigating the interactions among pathways
author Gu,Xiang
author_facet Gu,Xiang
Liu,Cong-Jian
Wei,Jian-Jie
author_role author
author2 Liu,Cong-Jian
Wei,Jian-Jie
author2_role author
author
dc.contributor.author.fl_str_mv Gu,Xiang
Liu,Cong-Jian
Wei,Jian-Jie
dc.subject.por.fl_str_mv Ankylosing spondylitis
Monte Carlo cross-validation
Differentially expressed genes
Random forest classification
Pathway cross-talk
topic Ankylosing spondylitis
Monte Carlo cross-validation
Differentially expressed genes
Random forest classification
Pathway cross-talk
description Given that the pathogenesis of ankylosing spondylitis (AS) remains unclear, the aim of this study was to detect the potentially functional pathway cross-talk in AS to further reveal the pathogenesis of this disease. Using microarray profile of AS and biological pathways as study objects, Monte Carlo cross-validation method was used to identify the significant pathway cross-talks. In the process of Monte Carlo cross-validation, all steps were iterated 50 times. For each run, detection of differentially expressed genes (DEGs) between two groups was conducted. The extraction of the potential disrupted pathways enriched by DEGs was then implemented. Subsequently, we established a discriminating score (DS) for each pathway pair according to the distribution of gene expression levels. After that, we utilized random forest (RF) classification model to screen out the top 10 paired pathways with the highest area under the curve (AUCs), which was computed using 10-fold cross-validation approach. After 50 bootstrap, the best pairs of pathways were identified. According to their AUC values, the pair of pathways, antigen presentation pathway and fMLP signaling in neutrophils, achieved the best AUC value of 1.000, which indicated that this pathway cross-talk could distinguish AS patients from normal subjects. Moreover, the paired pathways of SAPK/JNK signaling and mitochondrial dysfunction were involved in 5 bootstraps. Two paired pathways (antigen presentation pathway and fMLP signaling in neutrophil, as well as SAPK/JNK signaling and mitochondrial dysfunction) can accurately distinguish AS and control samples. These paired pathways may be helpful to identify patients with AS for early intervention.
publishDate 2018
dc.date.none.fl_str_mv 2018-01-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-879X2018000100607
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0100-879X2018000100607
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/1414-431x20176698
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Associação Brasileira de Divulgação Científica
publisher.none.fl_str_mv Associação Brasileira de Divulgação Científica
dc.source.none.fl_str_mv Brazilian Journal of Medical and Biological Research v.51 n.1 2018
reponame:Brazilian Journal of Medical and Biological Research
instname:Associação Brasileira de Divulgação Científica (ABDC)
instacron:ABDC
instname_str Associação Brasileira de Divulgação Científica (ABDC)
instacron_str ABDC
institution ABDC
reponame_str Brazilian Journal of Medical and Biological Research
collection Brazilian Journal of Medical and Biological Research
repository.name.fl_str_mv Brazilian Journal of Medical and Biological Research - Associação Brasileira de Divulgação Científica (ABDC)
repository.mail.fl_str_mv bjournal@terra.com.br||bjournal@terra.com.br
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