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Connectivity can be used to identify key genes in DNA microarray data: a study based on gene expression in nasal polyps before and after treatment with glucocorticoids

Benson, M.; Hov, D. A. Steenhoff; Clancy, T.; Hovig, E.; Rudemo, M. and Cardell, Lars-Olaf LU (2007) In Acta Oto-Laryngologica 127(10). p.1074-1079
Abstract
Conclusions. The presented analysis of nasal polyposis using connectivity based on the PubGene literature co-citation network demonstrates that this tool can be used to identify key genes in DNA microarray studies of human polygenic diseases. Objectives. DNA microarray studies of complex diseases may reveal differential expression of hundreds of genes. According to network theory and studies of yeast cells, genes that are connected with several other genes appear to have key regulatory roles. This study aimed to examine if this principle can be translated to DNA microarray studies of human disease, using nasal polyposis as a base for the analysis. Materials and methods. The connectivity of differentially expressed genes from a previously... (More)
Conclusions. The presented analysis of nasal polyposis using connectivity based on the PubGene literature co-citation network demonstrates that this tool can be used to identify key genes in DNA microarray studies of human polygenic diseases. Objectives. DNA microarray studies of complex diseases may reveal differential expression of hundreds of genes. According to network theory and studies of yeast cells, genes that are connected with several other genes appear to have key regulatory roles. This study aimed to examine if this principle can be translated to DNA microarray studies of human disease, using nasal polyposis as a base for the analysis. Materials and methods. The connectivity of differentially expressed genes from a previously described microarray study of nasal polyposis before and after treatment with glucocorticoids was determined. This was done using the literature co-citation network PubGene. Results. In all, 166 genes were differentially expressed; 39 of these were previously defined as inflammatory and considered important for nasal polyposis. The connectivity of all differentially expressed genes was analysed using the PubGene literature co-citation network. Seventy-four of the 166 genes were connected to other genes. By contrast, the average number of connected genes among 100 sets of 166 randomly chosen genes was 31.5. A small number of the differentially expressed genes were highly connected, while most genes had few or no connections. This indicated a scale-free network. The most connected gene was interleukin-8, an inflammatory gene of known importance for nasal polyposis. Twenty-eight of the 74 connected genes were inflammatory (38%), compared with 11 of the 92 unconnected genes (12%), p < 0.0001. Since most evidence suggests that nasal polyps are inflammatory in their nature, this supports the hypothesis that connected genes have more disease relevance than unconnected genes. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
network theory, PubGene
in
Acta Oto-Laryngologica
volume
127
issue
10
pages
1074 - 1079
publisher
Taylor & Francis
external identifiers
  • wos:000249588700011
  • scopus:34648824388
ISSN
1651-2251
DOI
10.1080/00016480701200277
language
English
LU publication?
yes
id
5e37d06c-080f-4910-bab2-c81f0b56b170 (old id 656854)
date added to LUP
2007-12-07 15:57:24
date last changed
2017-01-01 06:49:11
@article{5e37d06c-080f-4910-bab2-c81f0b56b170,
  abstract     = {Conclusions. The presented analysis of nasal polyposis using connectivity based on the PubGene literature co-citation network demonstrates that this tool can be used to identify key genes in DNA microarray studies of human polygenic diseases. Objectives. DNA microarray studies of complex diseases may reveal differential expression of hundreds of genes. According to network theory and studies of yeast cells, genes that are connected with several other genes appear to have key regulatory roles. This study aimed to examine if this principle can be translated to DNA microarray studies of human disease, using nasal polyposis as a base for the analysis. Materials and methods. The connectivity of differentially expressed genes from a previously described microarray study of nasal polyposis before and after treatment with glucocorticoids was determined. This was done using the literature co-citation network PubGene. Results. In all, 166 genes were differentially expressed; 39 of these were previously defined as inflammatory and considered important for nasal polyposis. The connectivity of all differentially expressed genes was analysed using the PubGene literature co-citation network. Seventy-four of the 166 genes were connected to other genes. By contrast, the average number of connected genes among 100 sets of 166 randomly chosen genes was 31.5. A small number of the differentially expressed genes were highly connected, while most genes had few or no connections. This indicated a scale-free network. The most connected gene was interleukin-8, an inflammatory gene of known importance for nasal polyposis. Twenty-eight of the 74 connected genes were inflammatory (38%), compared with 11 of the 92 unconnected genes (12%), p &lt; 0.0001. Since most evidence suggests that nasal polyps are inflammatory in their nature, this supports the hypothesis that connected genes have more disease relevance than unconnected genes.},
  author       = {Benson, M. and Hov, D. A. Steenhoff and Clancy, T. and Hovig, E. and Rudemo, M. and Cardell, Lars-Olaf},
  issn         = {1651-2251},
  keyword      = {network theory,PubGene},
  language     = {eng},
  number       = {10},
  pages        = {1074--1079},
  publisher    = {Taylor & Francis},
  series       = {Acta Oto-Laryngologica},
  title        = {Connectivity can be used to identify key genes in DNA microarray data: a study based on gene expression in nasal polyps before and after treatment with glucocorticoids},
  url          = {http://dx.doi.org/10.1080/00016480701200277},
  volume       = {127},
  year         = {2007},
}