Research Article

Bicluster and regulatory network analysis of differentially expressed genes in adenocarcinoma and squamous cell carcinoma

Published: May 21, 2013
Genet. Mol. Res. 12 (2) : 1710-1719 DOI: https://doi.org/10.4238/2013.May.21.2
Cite this Article:
(2013). Bicluster and regulatory network analysis of differentially expressed genes in adenocarcinoma and squamous cell carcinoma. Genet. Mol. Res. 12(2): gmr2306. https://doi.org/10.4238/2013.May.21.2
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Abstract

Squamous cell carcinoma (SCC) and adenocarcinoma (AC) are the major histological types of non-small cell lung cancer (NSCLC). Although differences in molecular, histological, and clinical characteristics have been reported for both subtypes, no specific therapy exists thus far. The aim of this analysis was to identify potential therapeutic target genes that are specific to SCC and AC. We used microarray data to analyze the global gene expression profile of 58 human NSCLC specimens. We identified more than 2400 genes that were differentially expressed in SCC and AC. Bicluster analysis with iterative signature algorithm revealed 22 biclusters that were strongly associated with the histological subtypes AC and SCC of NSCLC. We also built a regulatory network of genes differentially expressed in SCC and AC. Some transcription factors and target genes related to lung cancer are linked in our network. Furthermore, we used the Database for Annotation, Visualization and Integrated Discovery to identify the main pathways in which these differentially expressed genes were involved. Eight pathways were enriched by this analysis. Our data provide a comprehensive transcriptional profile of candidate genes that may be involved in the complex regulatory networks underlying the different NSCLC subtypes.

Squamous cell carcinoma (SCC) and adenocarcinoma (AC) are the major histological types of non-small cell lung cancer (NSCLC). Although differences in molecular, histological, and clinical characteristics have been reported for both subtypes, no specific therapy exists thus far. The aim of this analysis was to identify potential therapeutic target genes that are specific to SCC and AC. We used microarray data to analyze the global gene expression profile of 58 human NSCLC specimens. We identified more than 2400 genes that were differentially expressed in SCC and AC. Bicluster analysis with iterative signature algorithm revealed 22 biclusters that were strongly associated with the histological subtypes AC and SCC of NSCLC. We also built a regulatory network of genes differentially expressed in SCC and AC. Some transcription factors and target genes related to lung cancer are linked in our network. Furthermore, we used the Database for Annotation, Visualization and Integrated Discovery to identify the main pathways in which these differentially expressed genes were involved. Eight pathways were enriched by this analysis. Our data provide a comprehensive transcriptional profile of candidate genes that may be involved in the complex regulatory networks underlying the different NSCLC subtypes.

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