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pmid:34603387

Identification of LINC00173 in Myasthenia Gravis by Integration Analysis of Aberrantly Methylated- Differentially Expressed Genes and ceRNA Networks.

Si Xu · Tianfeng Wang · Xiaoyu Lu · Huixue Zhang · Li Liu · Xiaotong Kong · Shuang Li · Xu Wang · Hongyu Gao · Jianjian Wang · Lihua Wang

Frontiers in genetics · 2021 doi:10.3389/fgene.2021.726751

Paper

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Experiment

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Data

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Analysis

partial

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title
Identification of LINC00173 in Myasthenia Gravis by Integration Analysis of Aberrantly Methylated- Differentially Expressed Genes and ceRNA Networks.
doi
10.3389/fgene.2021.726751
pmid
34603387
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[0]
Si Xu
[1]
Tianfeng Wang
[2]
Xiaoyu Lu
[3]
Huixue Zhang
[4]
Li Liu
[5]
Xiaotong Kong
[6]
Shuang Li
[7]
Xu Wang
[8]
Hongyu Gao
[9]
Jianjian Wang
[10]
Lihua Wang
journal
Frontiers in genetics
year
2021
abstract
Myasthenia gravis (MG) is an autoimmune disease associated with autoantibody production that leads to skeletal muscle weakness. The molecular mechanisms underlying MG are not fully understood. We analyzed the gene expression profile (GSE85452) and methylation profile (GSE85647) of MG samples from the GEO database to identify aberrantly methylated-differentially expressed genes. By integrating the datasets, we identified 143 hypermethylation-low expression genes and 91 hypomethylation-high expression genes. Then we constructed PPI network and ceRNA networks by these genes. Phosphatase and tensin homolog (PTEN) and Abelson tyrosine-protein kinase (ABL)1 were critical genes in both PPI networks and ceRNA networks. And potential MG associated lncRNAs were selected by comprehensive analysis of the critical genes and ceRNA networks. In the hypermethylation-low expression genes associated ceRNA network, sirtuin (SIRT)1 was the most important gene and the lncRNA HLA complex (HC) P5 had the highest connection degree. Meanwhile, PTEN was the most important gene and the lncRNA LINC00173 had the highest connection degree in the hypomethylation-high expression genes associated ceRNA network. LINC00173 was validated to be upregulated in MG patients by qRT-PCR (P = 0.005), which indicated LINC00173 might be a potential biomarker for MG. These results provide a basis for future studies on the molecular pathogenesis of MG.
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ceRNAs associated with PTEN.
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Identification of Biomarkers Based on Aberrantly Methylated-Differentially Expressed Genes and ceRNA Networks
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The molecular mechanisms underlying MG are not fully understood. We analyzed the gene expression profile (GSE85452) and methylation profile (GSE85647) of MG samples from the GEO database to identify aberrantly methylated-differentially expressed genes. By integrating the datasets, we identified 143 hypermethylation-low expression genes and 91 hypomethylation-high expression genes. Then we constructed PPI network and ceRNA networks by these genes. Phosphatase and tensin homolog (PTEN) and Abelson tyrosine-protein kinase (ABL)1 were critical genes in both PPI networks and ceRNA networks. And potential MG associated lncRNAs were selected by comprehensive analysis of the critical genes and ceRNA networks. In the hypermethylation-low expression genes associated ceRNA network, sirtuin (SIRT)1 was the most important gene and the lncRNA HLA complex (HC) P5 had the highest connection degree. Meanwhile, PTEN was the most important gene and the lncRNA LINC00173 had the highest connection degree in the hypomethylation-high expression genes associated ceRNA network. LINC00173 was validated to be upregulated in MG patients by qRT-PCR (P = 0.005), which indicated LINC00173 might be a potential biomarker for MG. 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