Cargando…

Bioinformatic Analysis Identifies Potential Key Genes in the Pathogenesis of Turner Syndrome

Background: Turner syndrome (TS) is a sex chromosome aneuploidy with a variable spectrum of symptoms including short stature, ovarian failure and skeletal abnormalities. The etiology of TS is complex, and the mechanisms driving its pathogenesis remain unclear. Methods: In our study, we used the onli...

Descripción completa

Detalles Bibliográficos
Autores principales: Wang, Hao, Zhu, Hui, Zhu, Wenjiao, Xu, Yue, Wang, Nan, Han, Bing, Song, Huaidong, Qiao, Jie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7069359/
https://www.ncbi.nlm.nih.gov/pubmed/32210915
http://dx.doi.org/10.3389/fendo.2020.00104
_version_ 1783505764340465664
author Wang, Hao
Zhu, Hui
Zhu, Wenjiao
Xu, Yue
Wang, Nan
Han, Bing
Song, Huaidong
Qiao, Jie
author_facet Wang, Hao
Zhu, Hui
Zhu, Wenjiao
Xu, Yue
Wang, Nan
Han, Bing
Song, Huaidong
Qiao, Jie
author_sort Wang, Hao
collection PubMed
description Background: Turner syndrome (TS) is a sex chromosome aneuploidy with a variable spectrum of symptoms including short stature, ovarian failure and skeletal abnormalities. The etiology of TS is complex, and the mechanisms driving its pathogenesis remain unclear. Methods: In our study, we used the online Gene Expression Omnibus (GEO) microarray expression profiling dataset GSE46687 to identify differentially expressed genes (DEGs) between monosomy X TS patients and normal female individuals. The relevant data on 26 subjects with TS (45,XO) and 10 subjects with the normal karyotype (46,XX) was investigated. Then, tissue-specific gene expression, functional enrichment, and protein-protein interaction (PPI) network analyses were performed, and the key modules were identified. Results: In total, 25 upregulated and 60 downregulated genes were identified in the differential expression analysis. The tissue-specific gene expression analysis of the DEGs revealed that the system with the most highly enriched tissue-specific gene expression was the hematologic/immune system, followed by the skin/skeletal muscle and neurologic systems. The PPI network analysis, construction of key modules and manual screening of tissue-specific gene expression resulted in the identification of the following five genes of interest: CD99, CSF2RA, MYL9, MYLPF, and IGFBP2. CD99 and CSF2RA are involved in the hematologic/immune system, MYL9 and MYLPF are related to the circulatory system, and IGFBP2 is related to skeletal abnormalities. In addition, several genes of interest with possible roles in the pathogenesis of TS were identified as being associated with the hematologic/immune system or metabolism. Conclusion: This discovery-driven analysis may be a useful method for elucidating novel mechanisms underlying TS. However, more experiments are needed to further explore the relationships between these genes and TS in the future.
format Online
Article
Text
id pubmed-7069359
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-70693592020-03-24 Bioinformatic Analysis Identifies Potential Key Genes in the Pathogenesis of Turner Syndrome Wang, Hao Zhu, Hui Zhu, Wenjiao Xu, Yue Wang, Nan Han, Bing Song, Huaidong Qiao, Jie Front Endocrinol (Lausanne) Endocrinology Background: Turner syndrome (TS) is a sex chromosome aneuploidy with a variable spectrum of symptoms including short stature, ovarian failure and skeletal abnormalities. The etiology of TS is complex, and the mechanisms driving its pathogenesis remain unclear. Methods: In our study, we used the online Gene Expression Omnibus (GEO) microarray expression profiling dataset GSE46687 to identify differentially expressed genes (DEGs) between monosomy X TS patients and normal female individuals. The relevant data on 26 subjects with TS (45,XO) and 10 subjects with the normal karyotype (46,XX) was investigated. Then, tissue-specific gene expression, functional enrichment, and protein-protein interaction (PPI) network analyses were performed, and the key modules were identified. Results: In total, 25 upregulated and 60 downregulated genes were identified in the differential expression analysis. The tissue-specific gene expression analysis of the DEGs revealed that the system with the most highly enriched tissue-specific gene expression was the hematologic/immune system, followed by the skin/skeletal muscle and neurologic systems. The PPI network analysis, construction of key modules and manual screening of tissue-specific gene expression resulted in the identification of the following five genes of interest: CD99, CSF2RA, MYL9, MYLPF, and IGFBP2. CD99 and CSF2RA are involved in the hematologic/immune system, MYL9 and MYLPF are related to the circulatory system, and IGFBP2 is related to skeletal abnormalities. In addition, several genes of interest with possible roles in the pathogenesis of TS were identified as being associated with the hematologic/immune system or metabolism. Conclusion: This discovery-driven analysis may be a useful method for elucidating novel mechanisms underlying TS. However, more experiments are needed to further explore the relationships between these genes and TS in the future. Frontiers Media S.A. 2020-03-06 /pmc/articles/PMC7069359/ /pubmed/32210915 http://dx.doi.org/10.3389/fendo.2020.00104 Text en Copyright © 2020 Wang, Zhu, Zhu, Xu, Wang, Han, Song and Qiao. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Endocrinology
Wang, Hao
Zhu, Hui
Zhu, Wenjiao
Xu, Yue
Wang, Nan
Han, Bing
Song, Huaidong
Qiao, Jie
Bioinformatic Analysis Identifies Potential Key Genes in the Pathogenesis of Turner Syndrome
title Bioinformatic Analysis Identifies Potential Key Genes in the Pathogenesis of Turner Syndrome
title_full Bioinformatic Analysis Identifies Potential Key Genes in the Pathogenesis of Turner Syndrome
title_fullStr Bioinformatic Analysis Identifies Potential Key Genes in the Pathogenesis of Turner Syndrome
title_full_unstemmed Bioinformatic Analysis Identifies Potential Key Genes in the Pathogenesis of Turner Syndrome
title_short Bioinformatic Analysis Identifies Potential Key Genes in the Pathogenesis of Turner Syndrome
title_sort bioinformatic analysis identifies potential key genes in the pathogenesis of turner syndrome
topic Endocrinology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7069359/
https://www.ncbi.nlm.nih.gov/pubmed/32210915
http://dx.doi.org/10.3389/fendo.2020.00104
work_keys_str_mv AT wanghao bioinformaticanalysisidentifiespotentialkeygenesinthepathogenesisofturnersyndrome
AT zhuhui bioinformaticanalysisidentifiespotentialkeygenesinthepathogenesisofturnersyndrome
AT zhuwenjiao bioinformaticanalysisidentifiespotentialkeygenesinthepathogenesisofturnersyndrome
AT xuyue bioinformaticanalysisidentifiespotentialkeygenesinthepathogenesisofturnersyndrome
AT wangnan bioinformaticanalysisidentifiespotentialkeygenesinthepathogenesisofturnersyndrome
AT hanbing bioinformaticanalysisidentifiespotentialkeygenesinthepathogenesisofturnersyndrome
AT songhuaidong bioinformaticanalysisidentifiespotentialkeygenesinthepathogenesisofturnersyndrome
AT qiaojie bioinformaticanalysisidentifiespotentialkeygenesinthepathogenesisofturnersyndrome