Cargando…

Identification of potential crucial genes and construction of microRNA-mRNA negative regulatory networks in osteosarcoma

BACKGROUND: This study aimed to identify potential crucial genes and construction of microRNA-mRNA negative regulatory networks in osteosarcoma by comprehensive bioinformatics analysis. METHODS: Data of gene expression profiles (GSE28424) and miRNA expression profiles (GSE28423) were downloaded from...

Descripción completa

Detalles Bibliográficos
Autores principales: Pan, Yue, Lu, Lingyun, Chen, Junquan, Zhong, Yong, Dai, Zhehao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5941338/
https://www.ncbi.nlm.nih.gov/pubmed/29760609
http://dx.doi.org/10.1186/s41065-018-0061-9
_version_ 1783321266911969280
author Pan, Yue
Lu, Lingyun
Chen, Junquan
Zhong, Yong
Dai, Zhehao
author_facet Pan, Yue
Lu, Lingyun
Chen, Junquan
Zhong, Yong
Dai, Zhehao
author_sort Pan, Yue
collection PubMed
description BACKGROUND: This study aimed to identify potential crucial genes and construction of microRNA-mRNA negative regulatory networks in osteosarcoma by comprehensive bioinformatics analysis. METHODS: Data of gene expression profiles (GSE28424) and miRNA expression profiles (GSE28423) were downloaded from GEO database. The differentially expressed genes (DEGs) and miRNAs (DEMIs) were obtained by R Bioconductor packages. Functional and enrichment analyses of selected genes were performed using DAVID database. Protein–protein interaction (PPI) network was constructed by STRING and visualized in Cytoscape. The relationships among the DEGs and module in PPI network were analyzed by plug-in NetworkAnalyzer and MCODE seperately. Through the TargetScan and comparing target genes with DEGs, the miRNA-mRNA regulation network was established. RESULTS: Totally 346 DEGs and 90 DEMIs were found to be differentially expressed. These DEGs were enriched in biological processes and KEGG pathway of inflammatory immune response. 25 genes in the PPI network were selected as hub genes. Top 10 hub genes were TYROBP, HLA-DRA, VWF, PPBP, SERPING1, HLA-DPA1, SERPINA1, KIF20A, FERMT3, HLA-E. PPI network of DEGs followed a pattern of power law network and met the characteristics of small-world network. MCODE analysis identified 4 clusters and the most significant cluster consisted of 11 nodes and 55 edges. SEPP1, CKS2, TCAP, BPI were identified as the seed genes in their own clusters, respectively. The miRNA-mRNA regulation network which was composed of 89 pairs was established. MiR-210 had the highest connectivity with 12 target genes. Among the predicted target of MiR-96, HLA-DPA1 and TYROBP were the hub genes. CONCLUSION: Our study indicated possible differentially expressed genes and miRNA, and microRNA-mRNA negative regulatory networks in osteosarcoma by bioinformatics analysis, which may provide novel insights for unraveling pathogenesis of osteosarcoma.
format Online
Article
Text
id pubmed-5941338
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-59413382018-05-14 Identification of potential crucial genes and construction of microRNA-mRNA negative regulatory networks in osteosarcoma Pan, Yue Lu, Lingyun Chen, Junquan Zhong, Yong Dai, Zhehao Hereditas Research BACKGROUND: This study aimed to identify potential crucial genes and construction of microRNA-mRNA negative regulatory networks in osteosarcoma by comprehensive bioinformatics analysis. METHODS: Data of gene expression profiles (GSE28424) and miRNA expression profiles (GSE28423) were downloaded from GEO database. The differentially expressed genes (DEGs) and miRNAs (DEMIs) were obtained by R Bioconductor packages. Functional and enrichment analyses of selected genes were performed using DAVID database. Protein–protein interaction (PPI) network was constructed by STRING and visualized in Cytoscape. The relationships among the DEGs and module in PPI network were analyzed by plug-in NetworkAnalyzer and MCODE seperately. Through the TargetScan and comparing target genes with DEGs, the miRNA-mRNA regulation network was established. RESULTS: Totally 346 DEGs and 90 DEMIs were found to be differentially expressed. These DEGs were enriched in biological processes and KEGG pathway of inflammatory immune response. 25 genes in the PPI network were selected as hub genes. Top 10 hub genes were TYROBP, HLA-DRA, VWF, PPBP, SERPING1, HLA-DPA1, SERPINA1, KIF20A, FERMT3, HLA-E. PPI network of DEGs followed a pattern of power law network and met the characteristics of small-world network. MCODE analysis identified 4 clusters and the most significant cluster consisted of 11 nodes and 55 edges. SEPP1, CKS2, TCAP, BPI were identified as the seed genes in their own clusters, respectively. The miRNA-mRNA regulation network which was composed of 89 pairs was established. MiR-210 had the highest connectivity with 12 target genes. Among the predicted target of MiR-96, HLA-DPA1 and TYROBP were the hub genes. CONCLUSION: Our study indicated possible differentially expressed genes and miRNA, and microRNA-mRNA negative regulatory networks in osteosarcoma by bioinformatics analysis, which may provide novel insights for unraveling pathogenesis of osteosarcoma. BioMed Central 2018-05-09 /pmc/articles/PMC5941338/ /pubmed/29760609 http://dx.doi.org/10.1186/s41065-018-0061-9 Text en © The Author(s) 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Pan, Yue
Lu, Lingyun
Chen, Junquan
Zhong, Yong
Dai, Zhehao
Identification of potential crucial genes and construction of microRNA-mRNA negative regulatory networks in osteosarcoma
title Identification of potential crucial genes and construction of microRNA-mRNA negative regulatory networks in osteosarcoma
title_full Identification of potential crucial genes and construction of microRNA-mRNA negative regulatory networks in osteosarcoma
title_fullStr Identification of potential crucial genes and construction of microRNA-mRNA negative regulatory networks in osteosarcoma
title_full_unstemmed Identification of potential crucial genes and construction of microRNA-mRNA negative regulatory networks in osteosarcoma
title_short Identification of potential crucial genes and construction of microRNA-mRNA negative regulatory networks in osteosarcoma
title_sort identification of potential crucial genes and construction of microrna-mrna negative regulatory networks in osteosarcoma
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5941338/
https://www.ncbi.nlm.nih.gov/pubmed/29760609
http://dx.doi.org/10.1186/s41065-018-0061-9
work_keys_str_mv AT panyue identificationofpotentialcrucialgenesandconstructionofmicrornamrnanegativeregulatorynetworksinosteosarcoma
AT lulingyun identificationofpotentialcrucialgenesandconstructionofmicrornamrnanegativeregulatorynetworksinosteosarcoma
AT chenjunquan identificationofpotentialcrucialgenesandconstructionofmicrornamrnanegativeregulatorynetworksinosteosarcoma
AT zhongyong identificationofpotentialcrucialgenesandconstructionofmicrornamrnanegativeregulatorynetworksinosteosarcoma
AT daizhehao identificationofpotentialcrucialgenesandconstructionofmicrornamrnanegativeregulatorynetworksinosteosarcoma