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Screening for genes and subnetworks associated with pancreatic cancer based on the gene expression profile

The present study aimed to screen for potential genes and subnetworks associated with pancreatic cancer (PC) using the gene expression profile. The expression profile GSE 16515 was downloaded from the Gene Expression Omnibus database, which included 36 PC tissue samples and 16 normal samples. Limma...

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Autores principales: LONG, JIN, LIU, ZHE, WU, XINGDA, XU, YUANHONG, GE, CHUNLIN
Formato: Online Artículo Texto
Lenguaje:English
Publicado: D.A. Spandidos 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4838159/
https://www.ncbi.nlm.nih.gov/pubmed/27035224
http://dx.doi.org/10.3892/mmr.2016.5007
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author LONG, JIN
LIU, ZHE
WU, XINGDA
XU, YUANHONG
GE, CHUNLIN
author_facet LONG, JIN
LIU, ZHE
WU, XINGDA
XU, YUANHONG
GE, CHUNLIN
author_sort LONG, JIN
collection PubMed
description The present study aimed to screen for potential genes and subnetworks associated with pancreatic cancer (PC) using the gene expression profile. The expression profile GSE 16515 was downloaded from the Gene Expression Omnibus database, which included 36 PC tissue samples and 16 normal samples. Limma package in R language was used to screen differentially expressed genes (DEGs), which were grouped as up- and downregulated genes. Then, PFSNet was applied to perform subnetwork analysis for all the DEGs. Moreover, Gene Ontology (GO) and REACTOME pathway enrichment analysis of up- and downregulated genes was performed, followed by protein-protein interaction (PPI) network construction using Search Tool for the Retrieval of Interacting Genes Search Tool for the Retrieval of Interacting Genes. In total, 1,989 DEGs including 1,461 up- and 528 downregulated genes were screened out. Subnetworks including pancreatic cancer in PC tissue samples and intercellular adhesion in normal samples were identified, respectively. A total of 8 significant REACTOME pathways for upregulated DEGs, such as hemostasis and cell cycle, mitotic were identified. Moreover, 4 significant REACTOME pathways for downregulated DEGs, including regulation of β-cell development and transmembrane transport of small molecules were screened out. Additionally, DEGs with high connectivity degrees, such as CCNA2 (cyclin A2) and PBK (PDZ binding kinase), of the module in the protein-protein interaction network were mainly enriched with cell-division cycle. CCNA2 and PBK of the module and their relative pathway cell-division cycle, and two subnetworks (pancreatic cancer and intercellular adhesion subnetworks) may be pivotal for further understanding of the molecular mechanism of PC.
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spelling pubmed-48381592016-04-21 Screening for genes and subnetworks associated with pancreatic cancer based on the gene expression profile LONG, JIN LIU, ZHE WU, XINGDA XU, YUANHONG GE, CHUNLIN Mol Med Rep Articles The present study aimed to screen for potential genes and subnetworks associated with pancreatic cancer (PC) using the gene expression profile. The expression profile GSE 16515 was downloaded from the Gene Expression Omnibus database, which included 36 PC tissue samples and 16 normal samples. Limma package in R language was used to screen differentially expressed genes (DEGs), which were grouped as up- and downregulated genes. Then, PFSNet was applied to perform subnetwork analysis for all the DEGs. Moreover, Gene Ontology (GO) and REACTOME pathway enrichment analysis of up- and downregulated genes was performed, followed by protein-protein interaction (PPI) network construction using Search Tool for the Retrieval of Interacting Genes Search Tool for the Retrieval of Interacting Genes. In total, 1,989 DEGs including 1,461 up- and 528 downregulated genes were screened out. Subnetworks including pancreatic cancer in PC tissue samples and intercellular adhesion in normal samples were identified, respectively. A total of 8 significant REACTOME pathways for upregulated DEGs, such as hemostasis and cell cycle, mitotic were identified. Moreover, 4 significant REACTOME pathways for downregulated DEGs, including regulation of β-cell development and transmembrane transport of small molecules were screened out. Additionally, DEGs with high connectivity degrees, such as CCNA2 (cyclin A2) and PBK (PDZ binding kinase), of the module in the protein-protein interaction network were mainly enriched with cell-division cycle. CCNA2 and PBK of the module and their relative pathway cell-division cycle, and two subnetworks (pancreatic cancer and intercellular adhesion subnetworks) may be pivotal for further understanding of the molecular mechanism of PC. D.A. Spandidos 2016-05 2016-03-18 /pmc/articles/PMC4838159/ /pubmed/27035224 http://dx.doi.org/10.3892/mmr.2016.5007 Text en Copyright: © Long et al. This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
spellingShingle Articles
LONG, JIN
LIU, ZHE
WU, XINGDA
XU, YUANHONG
GE, CHUNLIN
Screening for genes and subnetworks associated with pancreatic cancer based on the gene expression profile
title Screening for genes and subnetworks associated with pancreatic cancer based on the gene expression profile
title_full Screening for genes and subnetworks associated with pancreatic cancer based on the gene expression profile
title_fullStr Screening for genes and subnetworks associated with pancreatic cancer based on the gene expression profile
title_full_unstemmed Screening for genes and subnetworks associated with pancreatic cancer based on the gene expression profile
title_short Screening for genes and subnetworks associated with pancreatic cancer based on the gene expression profile
title_sort screening for genes and subnetworks associated with pancreatic cancer based on the gene expression profile
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4838159/
https://www.ncbi.nlm.nih.gov/pubmed/27035224
http://dx.doi.org/10.3892/mmr.2016.5007
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