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Research article
Xun Xu
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Background: Age-related macular degeneration (AMD) represents the leading cause of visual impairment in the aging population. The goal of this study was to identify aberrantly methylated-differentially expressed genes (MDEGs) in AMD and explore the involved pathways by integrated bioinformatic analysis.
Methods: Data of expression profiling GSE29801 and methylation profiling GSE102952 were obtained from the Gene Expression Omnibus database. We analyzed differentially methylated genes and differentially expressed genes in R software. Functional enrichment and protein–protein interaction (PPI) network analysis were performed using R package and Search Tool for the Retrieval of Interacting Genes online database. Hub genes were identified using Cytoscape.
Results: 827 and 592 genes showed high and low expression, respectively, in GSE29801; 4117 hyper-methylated genes and 511 hypo-methylated genes were detected in GSE102952. After overlapping, we categorized 153 genes as hyper-methylated, low-expression genes (Hyper-LGs) and 24 genes as hypo-methylated, high-expression genes (Hypo-HGs). Four Hyper-LGs ( CKB , PPP3CA , TGFB2 , SOCS2 ) overlapped with AMD risk genes in Public Health Genomics and Precision Health Knowledge Base. KEGG pathway enrichment analysis indicated Hypo-HGs were enriched in the calcium signaling pathway, whereas Hyper-LGs were enriched in sphingolipid metabolism. In GO analysis, Hypo-HGs were enriched in fibroblast migration, membrane raft, coenzyme binding, etc. Hyper-LGs were enriched in mRNA transport, nuclear speck, DNA binding, etc. In PPI networks analysis, 23 nodes and 2 edges were established from Hypo-HGs, and 151 nodes and 73 edges were established from Hyper-LGs. Hub genes ( DHX9 , MAPT , PAX6 ) showed the greatest overlap.
Conclusion: This study revealed potentially aberrantly MDEGs and pathways in AMD, which may improve the understanding of this disease.
Keywords
Methylation, Gene expression, Enrichment analysis, Network analysis, Age-related macular degeneration
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View the published article at BMC Ophthalmology.
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On 13 Mar, 2020
Editorial decision: Accept
On 13 Mar, 2020
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Editorial decision: Minor revision
On 11 Mar, 2020
Review #2 received
Received 10 Mar, 2020
Reviewer #2 agreed
On 07 Mar, 2020
Review #1 received
Received 06 Mar, 2020
Reviewers invited
Invitations sent on 06 Mar, 2020
Reviewer #1 agreed
On 06 Mar, 2020
Editor assigned
On 03 Mar, 2020
Submission checks complete
On 02 Mar, 2020
Editor invited
On 02 Mar, 2020
Version 1
Posted 13 Jan, 2020
No comments provided
Editorial decision: Major revision
On 19 Feb, 2020
Review #4 received
Received 11 Feb, 2020
Review #2 received
Received 11 Feb, 2020
Review #3 received
Received 11 Feb, 2020
Reviewer #4 agreed
On 06 Feb, 2020
Reviewer #3 agreed
On 30 Jan, 2020
Reviewer #2 agreed
On 29 Jan, 2020
Review #1 received
Received 29 Jan, 2020
Reviewers invited
Invitations sent on 29 Jan, 2020
Reviewer #1 agreed
On 29 Jan, 2020
Editor assigned
On 15 Jan, 2020
Submission checks complete
On 10 Jan, 2020
Editor invited
On 09 Jan, 2020
First submitted
On 23 Dec, 2019
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Subject Areas
Ophthalmology
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See the published version of this preprint at BMC Ophthalmology.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
Xun Xu
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at BMC Ophthalmology.
No community comments so far
Submission checks complete
On 13 Mar, 2020
Editorial decision: Accept
On 13 Mar, 2020
No comments provided
Editorial decision: Minor revision
On 11 Mar, 2020
Review #2 received
Received 10 Mar, 2020
Reviewer #2 agreed
On 07 Mar, 2020
Review #1 received
Received 06 Mar, 2020
Reviewers invited
Invitations sent on 06 Mar, 2020
Reviewer #1 agreed
On 06 Mar, 2020
Editor assigned
On 03 Mar, 2020
Submission checks complete
On 02 Mar, 2020
Editor invited
On 02 Mar, 2020
Version 1
Posted 13 Jan, 2020
No comments provided
Editorial decision: Major revision
On 19 Feb, 2020
Review #4 received
Received 11 Feb, 2020
Review #2 received
Received 11 Feb, 2020
Review #3 received
Received 11 Feb, 2020
Reviewer #4 agreed
On 06 Feb, 2020
Reviewer #3 agreed
On 30 Jan, 2020
Reviewer #2 agreed
On 29 Jan, 2020
Review #1 received
Received 29 Jan, 2020
Reviewers invited
Invitations sent on 29 Jan, 2020
Reviewer #1 agreed
On 29 Jan, 2020
Editor assigned
On 15 Jan, 2020
Submission checks complete
On 10 Jan, 2020
Editor invited
On 09 Jan, 2020
First submitted
On 23 Dec, 2019
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Ophthalmology
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BMC Ophthalmology.
Background: Age-related macular degeneration (AMD) represents the leading cause of visual impairment in the aging population. The goal of this study was to identify aberrantly methylated-differentially expressed genes (MDEGs) in AMD and explore the involved pathways by integrated bioinformatic analysis.
Methods: Data of expression profiling GSE29801 and methylation profiling GSE102952 were obtained from the Gene Expression Omnibus database. We analyzed differentially methylated genes and differentially expressed genes in R software. Functional enrichment and protein–protein interaction (PPI) network analysis were performed using R package and Search Tool for the Retrieval of Interacting Genes online database. Hub genes were identified using Cytoscape.
Results: 827 and 592 genes showed high and low expression, respectively, in GSE29801; 4117 hyper-methylated genes and 511 hypo-methylated genes were detected in GSE102952. After overlapping, we categorized 153 genes as hyper-methylated, low-expression genes (Hyper-LGs) and 24 genes as hypo-methylated, high-expression genes (Hypo-HGs). Four Hyper-LGs ( CKB , PPP3CA , TGFB2 , SOCS2 ) overlapped with AMD risk genes in Public Health Genomics and Precision Health Knowledge Base. KEGG pathway enrichment analysis indicated Hypo-HGs were enriched in the calcium signaling pathway, whereas Hyper-LGs were enriched in sphingolipid metabolism. In GO analysis, Hypo-HGs were enriched in fibroblast migration, membrane raft, coenzyme binding, etc. Hyper-LGs were enriched in mRNA transport, nuclear speck, DNA binding, etc. In PPI networks analysis, 23 nodes and 2 edges were established from Hypo-HGs, and 151 nodes and 73 edges were established from Hyper-LGs. Hub genes ( DHX9 , MAPT , PAX6 ) showed the greatest overlap.
Conclusion: This study revealed potentially aberrantly MDEGs and pathways in AMD, which may improve the understanding of this disease.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
This is a list of supplementary files associated with this preprint. Click to download.
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