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2017 ; 7
(1
): 2472
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Identification of oral cancer related candidate genes by integrating
protein-protein interactions, gene ontology, pathway analysis and
immunohistochemistry
#MMPMID28559546
Kumar R
; Samal SK
; Routray S
; Dash R
; Dixit A
Sci Rep
2017[May]; 7
(1
): 2472
PMID28559546
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In the recent years, bioinformatics methods have been reported with a high degree
of success for candidate gene identification. In this milieu, we have used an
integrated bioinformatics approach assimilating information from gene ontologies
(GO), protein-protein interaction (PPI) and network analysis to predict candidate
genes related to oral squamous cell carcinoma (OSCC). A total of 40973 PPIs were
considered for 4704 cancer-related genes to construct human cancer gene network
(HCGN). The importance of each node was measured in HCGN by ten different
centrality measures. We have shown that the top ranking genes are related to a
significantly higher number of diseases as compared to other genes in HCGN. A
total of 39 candidate oral cancer target genes were predicted by combining top
ranked genes and the genes corresponding to significantly enriched oral cancer
related GO terms. Initial verification using literature and available
experimental data indicated that 29 genes were related with OSCC. A detailed
pathway analysis led us to propose a role for the selected candidate genes in the
invasion and metastasis in OSCC. We further validated our predictions using
immunohistochemistry (IHC) and found that the gene FLNA was upregulated while the
genes ARRB1 and HTT were downregulated in the OSCC tissue samples.
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