Immune cell infiltration landscape and immune marker molecular typing in preeclampsia

Preeclampsia (PE) is an important topic in obstetrics. In this study, we used weighted gene co-expression network analysis (WGCNA) to screen the key modules related to immune cell infiltration and to identify the hub genes for the molecular subtyping of PE. We first downloaded a set of PE transcript...

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Main Authors: YiLin Meng, Chuang Li, Cai-Xia Liu
Format: Article
Language:English
Published: Taylor & Francis Group 2021-01-01
Series:Bioengineered
Subjects:
Online Access:http://dx.doi.org/10.1080/21655979.2021.1875707
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spelling doaj-de5ca997c92d49ceb81f6794a5fb3af72021-02-08T14:35:51ZengTaylor & Francis GroupBioengineered2165-59792165-59872021-01-0112154055410.1080/21655979.2021.18757071875707Immune cell infiltration landscape and immune marker molecular typing in preeclampsiaYiLin Meng0Chuang Li1Cai-Xia Liu2Shengjing Hospital of China Medical University, ShenyangShengjing Hospital of China Medical University, ShenyangShengjing Hospital of China Medical University, ShenyangPreeclampsia (PE) is an important topic in obstetrics. In this study, we used weighted gene co-expression network analysis (WGCNA) to screen the key modules related to immune cell infiltration and to identify the hub genes for the molecular subtyping of PE. We first downloaded a set of PE transcriptional data (GSE75010; 157 samples: 80 PE and 77 non-PE) from the GEO database. We then analyzed the PE samples and non-PE samples for immune cell infiltration and screened cells with differences in such infiltration. Next, we downloaded the immune-related genes from an immune-related database to screen the expression profile of the immune-related genes. Then, we obtained a candidate gene set by screening the immune-related genes differentially expressed between the two groups. We used WGCNA to construct a weighted co-expression network for these candidate genes, mined co-expression modules, and then calculated the correlation between each module and immune cells with differential infiltration. We screened the modules related to infiltrating immune cells, identified the key modules’ hub genes, and determined the key module genes that interacted with each other. Finally, we obtained the hub genes related to the infiltrating immune cells. We classified the preeclampsia patients by unsupervised cluster molecular typing, determined the difference of immune cell infiltration among the different PE subtypes, and calculated the expression of hub genes in these different subtypes. In conclusion, we found 41 hub genes that may be closely related to the molecular typing of PE.http://dx.doi.org/10.1080/21655979.2021.1875707preeclampsiaimmune infiltrationimmune-related genedifferential analysiswgcnanetwork miningprotein interaction networkdisease typing
collection DOAJ
language English
format Article
sources DOAJ
author YiLin Meng
Chuang Li
Cai-Xia Liu
spellingShingle YiLin Meng
Chuang Li
Cai-Xia Liu
Immune cell infiltration landscape and immune marker molecular typing in preeclampsia
Bioengineered
preeclampsia
immune infiltration
immune-related gene
differential analysis
wgcna
network mining
protein interaction network
disease typing
author_facet YiLin Meng
Chuang Li
Cai-Xia Liu
author_sort YiLin Meng
title Immune cell infiltration landscape and immune marker molecular typing in preeclampsia
title_short Immune cell infiltration landscape and immune marker molecular typing in preeclampsia
title_full Immune cell infiltration landscape and immune marker molecular typing in preeclampsia
title_fullStr Immune cell infiltration landscape and immune marker molecular typing in preeclampsia
title_full_unstemmed Immune cell infiltration landscape and immune marker molecular typing in preeclampsia
title_sort immune cell infiltration landscape and immune marker molecular typing in preeclampsia
publisher Taylor & Francis Group
series Bioengineered
issn 2165-5979
2165-5987
publishDate 2021-01-01
description Preeclampsia (PE) is an important topic in obstetrics. In this study, we used weighted gene co-expression network analysis (WGCNA) to screen the key modules related to immune cell infiltration and to identify the hub genes for the molecular subtyping of PE. We first downloaded a set of PE transcriptional data (GSE75010; 157 samples: 80 PE and 77 non-PE) from the GEO database. We then analyzed the PE samples and non-PE samples for immune cell infiltration and screened cells with differences in such infiltration. Next, we downloaded the immune-related genes from an immune-related database to screen the expression profile of the immune-related genes. Then, we obtained a candidate gene set by screening the immune-related genes differentially expressed between the two groups. We used WGCNA to construct a weighted co-expression network for these candidate genes, mined co-expression modules, and then calculated the correlation between each module and immune cells with differential infiltration. We screened the modules related to infiltrating immune cells, identified the key modules’ hub genes, and determined the key module genes that interacted with each other. Finally, we obtained the hub genes related to the infiltrating immune cells. We classified the preeclampsia patients by unsupervised cluster molecular typing, determined the difference of immune cell infiltration among the different PE subtypes, and calculated the expression of hub genes in these different subtypes. In conclusion, we found 41 hub genes that may be closely related to the molecular typing of PE.
topic preeclampsia
immune infiltration
immune-related gene
differential analysis
wgcna
network mining
protein interaction network
disease typing
url http://dx.doi.org/10.1080/21655979.2021.1875707
work_keys_str_mv AT yilinmeng immunecellinfiltrationlandscapeandimmunemarkermoleculartypinginpreeclampsia
AT chuangli immunecellinfiltrationlandscapeandimmunemarkermoleculartypinginpreeclampsia
AT caixialiu immunecellinfiltrationlandscapeandimmunemarkermoleculartypinginpreeclampsia
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