Research Article

Bioinformatics analysis with graph-based clustering to detect gastric cancer-related pathways

Published: September 26, 2012
Genet. Mol. Res. 11 (3) : 3497-3504 DOI: https://doi.org/10.4238/2012.September.26.5
Cite this Article:
(2012). Bioinformatics analysis with graph-based clustering to detect gastric cancer-related pathways. Genet. Mol. Res. 11(3): gmr1816. https://doi.org/10.4238/2012.September.26.5
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Abstract

Despite a dramatic reduction in incidence and mortality rates, gastric cancer still remains one of the most common malignant tumors worldwide, especially in China. We sought to identify a set of discriminating genes that could be used for characterization and prediction of response to gastric cancer. Using bioinformatics analysis, two gastric cancer datasets, GSE19826 and GSE2685, were merged to find novel target genes and domains to explain pathogenesis; we selected differentially expressed genes in these two datasets and analyzed their correlation in order to construct a network. This network was examined to find graph clusters and related significant pathways. We found that ALDH2 and CCNB1 were associated with gastric cancer. We also mined for the underlying molecular mechanisms involving these differently expressed genes. We found that ECM-receptor interaction, focal adhesion, and cell cycle were among the significantly associated pathways. We were able to detect genes and pathways that were not considered in previous research on gastric cancer, indicating that this approach could be an improvement on the investigative mechanisms for finding genetic associations with disease.

Despite a dramatic reduction in incidence and mortality rates, gastric cancer still remains one of the most common malignant tumors worldwide, especially in China. We sought to identify a set of discriminating genes that could be used for characterization and prediction of response to gastric cancer. Using bioinformatics analysis, two gastric cancer datasets, GSE19826 and GSE2685, were merged to find novel target genes and domains to explain pathogenesis; we selected differentially expressed genes in these two datasets and analyzed their correlation in order to construct a network. This network was examined to find graph clusters and related significant pathways. We found that ALDH2 and CCNB1 were associated with gastric cancer. We also mined for the underlying molecular mechanisms involving these differently expressed genes. We found that ECM-receptor interaction, focal adhesion, and cell cycle were among the significantly associated pathways. We were able to detect genes and pathways that were not considered in previous research on gastric cancer, indicating that this approach could be an improvement on the investigative mechanisms for finding genetic associations with disease.

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