ArticleScientific reports2017
Identification of oral cancer related candidate genes by integrating protein-protein interactions, gene ontology, pathway analysis and immunohistochemistry.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 42 citations in OpenAlex.
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- Integrated multiplex network based approach for hub gene identification in oral cancer.Heliyon · 2021Article
- An integrated approach for identification of a panel of candidate genes arbitrated for invasion and metastasis in oral squamous cell carcinoma.Scientific reports · 2021Article
- Novel Quantitative Analysis Using Optical Imaging (VELscope) and Spectroscopy (Raman) Techniques for Oral Cancer Detection.Cancers · 2020Article
- Genetic alterations and clinical dimensions of oral cancer: a review.Molecular biology reports · 2020Review
- Network-based identification of signature genes KLF6 and SPOCK1 associated with oral submucous fibrosis.Molecular and clinical oncology · 2020Article
- Oral lichen planus interactome reveals CXCR4 and CXCL12 as candidate therapeutic targets.Scientific reports · 2020Article
- Molecular docking studies of a-mangostin with oral cancer targets ARRB1, FLNA, CALM3 and HTT.Bioinformation · 2020Article
- miR-146a Overexpression in Oral Squamous Cell Carcinoma Potentiates Cancer Cell Migration and Invasion PossiblyFrontiers in oncology · 2020Article
- Multiclass classification of autofluorescence images of oral cavity lesions based on quantitative analysis.PloS one · 2020Article
- Raman Spectroscopy Analysis for Optical Diagnosis of Oral Cancer Detection.Journal of clinical medicine · 2019Article
Corrections and comments
- Erratum issued
- Erratum issued
Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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