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Bioinformatics analysis of multi-omics data identifying molecular biomarker candidates and epigenetically regulatory targets associated with retinoblastoma

Retinoblastoma (RB) is the commonest malignant tumor of the infant retina. Besides genetic changes, epigenetic events are also considered to implicate the occurrence of RB. This study aimed to identify significantly altered protein-coding genes, DNA methylation, microRNAs (miRNAs), long noncoding RN...

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Autores principales: Zeng, Yuyang, He, Tao, Liu, Juejun, Li, Zongyuan, Xie, Feijia, Chen, Changzheng, Xing, Yiqiao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7676602/
https://www.ncbi.nlm.nih.gov/pubmed/33217867
http://dx.doi.org/10.1097/MD.0000000000023314
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author Zeng, Yuyang
He, Tao
Liu, Juejun
Li, Zongyuan
Xie, Feijia
Chen, Changzheng
Xing, Yiqiao
author_facet Zeng, Yuyang
He, Tao
Liu, Juejun
Li, Zongyuan
Xie, Feijia
Chen, Changzheng
Xing, Yiqiao
author_sort Zeng, Yuyang
collection PubMed
description Retinoblastoma (RB) is the commonest malignant tumor of the infant retina. Besides genetic changes, epigenetic events are also considered to implicate the occurrence of RB. This study aimed to identify significantly altered protein-coding genes, DNA methylation, microRNAs (miRNAs), long noncoding RNAs (lncRNAs), and their molecular functions and pathways associated with RB, and investigate the epigenetically regulatory mechanism of DNA methylation modification and non-coding RNAs on key genes of RB via bioinformatics method. We obtained multi-omics data on protein-coding genes, DNA methylation, miRNAs, and lncRNAs from the Gene Expression Omnibus database. We identified differentially expressed genes (DEGs) using the Limma package in R, discerned their biological functions and pathways using enrichment analysis, and conducted the modular analysis based on protein-protein interaction network to identify hub genes of RB. Survival analyses based on The Cancer Genome Atlas clinical database were performed to analyze prognostic values of key genes of RB. Subsequently, we identified the differentially methylated genes, differentially expressed miRNAs (DEMs) and lncRNAs (DELs), and intersected them with key genes to analyze possible targets of the underlying epigenetic regulatory mechanisms. Finally, the ceRNA network of lncRNAs-miRNAs-mRNAs was constructed using Cytoscape. A total of 193 DEGs, 74 differentially methylated-DEGs (DM-DEGs), 45 DEMs, 5 DELs were identified. The molecular pathways of DEGs were enriched in cell cycle, p53 signaling pathway, and DNA replication. A total of 10 key genes were identified and found significantly associated with poor survival outcome based on survival analyses, including CDK1, BUB1, CCNB2, TOP2A, CCNB1, RRM2, KIF11, KIF20A, NDC80, and TTK. We further found that hub genes MCM6 and KIF14 were differentially methylated, key gene RRM2 was targeted by DEMs, and key genes TTK, RRM2, and CDK1 were indirectly regulated by DELs. Additionally, the ceRNA network with 222 regulatory associations was constructed to visualize the correlations between lncRNAs-miRNAs-mRNAs. This study presents an integrated bioinformatics analysis of genetic and epigenetic changes that may be associated with the development of RB. Findings may yield many new insights into the molecular biomarker candidates and epigenetically regulatory targets of RB.
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spelling pubmed-76766022020-11-24 Bioinformatics analysis of multi-omics data identifying molecular biomarker candidates and epigenetically regulatory targets associated with retinoblastoma Zeng, Yuyang He, Tao Liu, Juejun Li, Zongyuan Xie, Feijia Chen, Changzheng Xing, Yiqiao Medicine (Baltimore) 5800 Retinoblastoma (RB) is the commonest malignant tumor of the infant retina. Besides genetic changes, epigenetic events are also considered to implicate the occurrence of RB. This study aimed to identify significantly altered protein-coding genes, DNA methylation, microRNAs (miRNAs), long noncoding RNAs (lncRNAs), and their molecular functions and pathways associated with RB, and investigate the epigenetically regulatory mechanism of DNA methylation modification and non-coding RNAs on key genes of RB via bioinformatics method. We obtained multi-omics data on protein-coding genes, DNA methylation, miRNAs, and lncRNAs from the Gene Expression Omnibus database. We identified differentially expressed genes (DEGs) using the Limma package in R, discerned their biological functions and pathways using enrichment analysis, and conducted the modular analysis based on protein-protein interaction network to identify hub genes of RB. Survival analyses based on The Cancer Genome Atlas clinical database were performed to analyze prognostic values of key genes of RB. Subsequently, we identified the differentially methylated genes, differentially expressed miRNAs (DEMs) and lncRNAs (DELs), and intersected them with key genes to analyze possible targets of the underlying epigenetic regulatory mechanisms. Finally, the ceRNA network of lncRNAs-miRNAs-mRNAs was constructed using Cytoscape. A total of 193 DEGs, 74 differentially methylated-DEGs (DM-DEGs), 45 DEMs, 5 DELs were identified. The molecular pathways of DEGs were enriched in cell cycle, p53 signaling pathway, and DNA replication. A total of 10 key genes were identified and found significantly associated with poor survival outcome based on survival analyses, including CDK1, BUB1, CCNB2, TOP2A, CCNB1, RRM2, KIF11, KIF20A, NDC80, and TTK. We further found that hub genes MCM6 and KIF14 were differentially methylated, key gene RRM2 was targeted by DEMs, and key genes TTK, RRM2, and CDK1 were indirectly regulated by DELs. Additionally, the ceRNA network with 222 regulatory associations was constructed to visualize the correlations between lncRNAs-miRNAs-mRNAs. This study presents an integrated bioinformatics analysis of genetic and epigenetic changes that may be associated with the development of RB. Findings may yield many new insights into the molecular biomarker candidates and epigenetically regulatory targets of RB. Lippincott Williams & Wilkins 2020-11-20 /pmc/articles/PMC7676602/ /pubmed/33217867 http://dx.doi.org/10.1097/MD.0000000000023314 Text en Copyright © 2020 the Author(s). Published by Wolters Kluwer Health, Inc. http://creativecommons.org/licenses/by-nc/4.0 This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc/4.0
spellingShingle 5800
Zeng, Yuyang
He, Tao
Liu, Juejun
Li, Zongyuan
Xie, Feijia
Chen, Changzheng
Xing, Yiqiao
Bioinformatics analysis of multi-omics data identifying molecular biomarker candidates and epigenetically regulatory targets associated with retinoblastoma
title Bioinformatics analysis of multi-omics data identifying molecular biomarker candidates and epigenetically regulatory targets associated with retinoblastoma
title_full Bioinformatics analysis of multi-omics data identifying molecular biomarker candidates and epigenetically regulatory targets associated with retinoblastoma
title_fullStr Bioinformatics analysis of multi-omics data identifying molecular biomarker candidates and epigenetically regulatory targets associated with retinoblastoma
title_full_unstemmed Bioinformatics analysis of multi-omics data identifying molecular biomarker candidates and epigenetically regulatory targets associated with retinoblastoma
title_short Bioinformatics analysis of multi-omics data identifying molecular biomarker candidates and epigenetically regulatory targets associated with retinoblastoma
title_sort bioinformatics analysis of multi-omics data identifying molecular biomarker candidates and epigenetically regulatory targets associated with retinoblastoma
topic 5800
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7676602/
https://www.ncbi.nlm.nih.gov/pubmed/33217867
http://dx.doi.org/10.1097/MD.0000000000023314
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