Weighted gene co-expression network analysis revealed key biomarkers associated with the diagnosis of hypertrophic cardiomyopathy

Abstract Objective To reveal the molecular mechanism underlying the pathogenesis of HCM and find new effective therapeutic strategies using a systematic biological approach. Methods The WGCNA algorithm was applied to building the co-expression network of HCM samples. A sample cluster analysis was pe...

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Main Authors: Xin Li, Chenxin Wang, Xiaoqing Zhang, Jiali Liu, Yu Wang, Chunpu Li, Dongmei Guo
Format: Article
Language:English
Published: BMC 2020-10-01
Series:Hereditas
Subjects:
Online Access:http://link.springer.com/article/10.1186/s41065-020-00155-9
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spelling doaj-27ece8721ad54464b3b0fa916fe17f442020-11-25T03:40:44ZengBMCHereditas1601-52232020-10-01157111110.1186/s41065-020-00155-9Weighted gene co-expression network analysis revealed key biomarkers associated with the diagnosis of hypertrophic cardiomyopathyXin Li0Chenxin Wang1Xiaoqing Zhang2Jiali Liu3Yu Wang4Chunpu Li5Dongmei Guo6Department of Cardiovascular, The Third Central Hospital of TianjinDepartment of Respiratory medicine, The Third Central Hospital of TianjinDepartment of internal medicine, Affiliated Hospital of Nankai UniversityDepartment of Hematology, Taian City Central HospitalDepartment of Cardiovascular, The Third Central Hospital of TianjinDepartment of Orthopedics, Taian City Central HospitalDepartment of Hematology, Taian City Central HospitalAbstract Objective To reveal the molecular mechanism underlying the pathogenesis of HCM and find new effective therapeutic strategies using a systematic biological approach. Methods The WGCNA algorithm was applied to building the co-expression network of HCM samples. A sample cluster analysis was performed using the hclust tool and a co-expression module was constructed. The WGCNA algorithm was used to study the interactive connection between co-expression modules and draw a heat map to show the strength of interactions between modules. The genetic information of the respective modules was mapped to the associated GO terms and KEGG pathways, and the Hub Genes with the highest connectivity in each module were identified. The Wilcoxon test was used to verify the expression level of hub genes between HCM and normal samples, and the “pROC” R package was used to verify the possibility of hub genes as biomarkers. Finally, the potential functions of hub genes were analyzed by GSEA software. Results Seven co-expression modules were constructed using sample clustering analysis. GO and KEGG enrichment analysis judged that the turquoise module is an important module. The hub genes of each module are RPL35A for module Black, FH for module Blue, PREI3 for module Brown, CREB1 for module Green, LOC641848 for module Pink, MYH7 for module Turquoise and MYL6 for module Yellow. The results of the differential expression analysis indicate that MYH7 and FH are considered true hub genes. In addition, the ROC curves revealed their high diagnostic value as biomarkers for HCM. Finally, in the results of the GSEA analysis, MYH7 and FH highly expressed genes were enriched with the “proteasome” and a “PPAR signaling pathway,” respectively. Conclusions The MYH7 and FH genes may be the true hub genes of HCM. Their respective enriched pathways, namely the “proteasome” and the “PPAR signaling pathway,” may play an important role in the development of HCM.http://link.springer.com/article/10.1186/s41065-020-00155-9Hypertrophic cardiomyopathyWGCNAHub gene
collection DOAJ
language English
format Article
sources DOAJ
author Xin Li
Chenxin Wang
Xiaoqing Zhang
Jiali Liu
Yu Wang
Chunpu Li
Dongmei Guo
spellingShingle Xin Li
Chenxin Wang
Xiaoqing Zhang
Jiali Liu
Yu Wang
Chunpu Li
Dongmei Guo
Weighted gene co-expression network analysis revealed key biomarkers associated with the diagnosis of hypertrophic cardiomyopathy
Hereditas
Hypertrophic cardiomyopathy
WGCNA
Hub gene
author_facet Xin Li
Chenxin Wang
Xiaoqing Zhang
Jiali Liu
Yu Wang
Chunpu Li
Dongmei Guo
author_sort Xin Li
title Weighted gene co-expression network analysis revealed key biomarkers associated with the diagnosis of hypertrophic cardiomyopathy
title_short Weighted gene co-expression network analysis revealed key biomarkers associated with the diagnosis of hypertrophic cardiomyopathy
title_full Weighted gene co-expression network analysis revealed key biomarkers associated with the diagnosis of hypertrophic cardiomyopathy
title_fullStr Weighted gene co-expression network analysis revealed key biomarkers associated with the diagnosis of hypertrophic cardiomyopathy
title_full_unstemmed Weighted gene co-expression network analysis revealed key biomarkers associated with the diagnosis of hypertrophic cardiomyopathy
title_sort weighted gene co-expression network analysis revealed key biomarkers associated with the diagnosis of hypertrophic cardiomyopathy
publisher BMC
series Hereditas
issn 1601-5223
publishDate 2020-10-01
description Abstract Objective To reveal the molecular mechanism underlying the pathogenesis of HCM and find new effective therapeutic strategies using a systematic biological approach. Methods The WGCNA algorithm was applied to building the co-expression network of HCM samples. A sample cluster analysis was performed using the hclust tool and a co-expression module was constructed. The WGCNA algorithm was used to study the interactive connection between co-expression modules and draw a heat map to show the strength of interactions between modules. The genetic information of the respective modules was mapped to the associated GO terms and KEGG pathways, and the Hub Genes with the highest connectivity in each module were identified. The Wilcoxon test was used to verify the expression level of hub genes between HCM and normal samples, and the “pROC” R package was used to verify the possibility of hub genes as biomarkers. Finally, the potential functions of hub genes were analyzed by GSEA software. Results Seven co-expression modules were constructed using sample clustering analysis. GO and KEGG enrichment analysis judged that the turquoise module is an important module. The hub genes of each module are RPL35A for module Black, FH for module Blue, PREI3 for module Brown, CREB1 for module Green, LOC641848 for module Pink, MYH7 for module Turquoise and MYL6 for module Yellow. The results of the differential expression analysis indicate that MYH7 and FH are considered true hub genes. In addition, the ROC curves revealed their high diagnostic value as biomarkers for HCM. Finally, in the results of the GSEA analysis, MYH7 and FH highly expressed genes were enriched with the “proteasome” and a “PPAR signaling pathway,” respectively. Conclusions The MYH7 and FH genes may be the true hub genes of HCM. Their respective enriched pathways, namely the “proteasome” and the “PPAR signaling pathway,” may play an important role in the development of HCM.
topic Hypertrophic cardiomyopathy
WGCNA
Hub gene
url http://link.springer.com/article/10.1186/s41065-020-00155-9
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AT xiaoqingzhang weightedgenecoexpressionnetworkanalysisrevealedkeybiomarkersassociatedwiththediagnosisofhypertrophiccardiomyopathy
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