Connectivity can be used to identify key genes in DNA microarray data: a study based on gene expression in nasal...

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.

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.

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.

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.