ORAOV1 is amplified in oral squamous cell carcinoma

BACKGROUND: Oral cancer overexpressed 1 (ORAOV1) was found as a candidate oncogene in the 11q13 chromosomal region, based on its amplification and overexpression in oral cancer cell lines. Because gene amplification often leads to increased levels of gene expression, we aimed to verify the relationship between ORAOV1 gene status and mRNA expression primarily in oral squamous cell carcinoma (OSCC) by quantitative assay, correlating with clinical and pathological characteristics in patients. METHODS: Levels of ORAOV1 amplification and expression were evaluated by qPCR and RT-qPCR in OSCC cell lines and in tumor and non-tumoral surgical margins from 33 patients with OSCC. All subjects were smokers and habitual alcohol drinkers, mostly men above 40 years of age and with a single primary tumor. RESULTS: ORAOV1 exhibited increased gene expression levels as well as higher copy number in three OSCC cell lines with 11q13 amplified chromosomal region when compared with the OSCC cell line without the amplification (one-way ANOVA, P < 0.05). Weak correlation between ORAOV1 mRNA levels and DNA copy number was seen in tumor samples (Spearman, P = 0.07). Although ORAOV1 was amplified in tumor (Wilcoxon, P < 0.01), high levels of transcripts in margin did not reveal differences in comparison with tumor (Wilcoxon, P = 0.85). Aggressiveness and survival rate did not demonstrate statistical difference for both events in OSCC. CONCLUSION: The overexpression of ORAOV1 in non-tumoral margin samples can occur in the absence of amplification. The weak correlation between ORAOV1 amplification and expression in OSSC suggests that ORAOV1 expression can be regulated by mechanisms other than gene amplification. J Oral Pathol Med (2012) 41: 5460

Predicting gene ontology annotations of orphan GWAS genes using protein-protein interactions

Background: The number of genome-wide association studies (GWAS) has increased rapidly in the past couple of years, resulting in the identification of genes associated with different diseases. The next step in translating these findings into biomedically useful information is to find out the mechanism of the action of these genes. However, GWAS studies often implicate genes whose functions are currently unknown; for example, MYEOV, ANKLE1, TMEM45B and ORAOV1 are found to be associated with breast cancer, but their molecular function is unknown. Results: We carried out Bayesian inference of Gene Ontology (GO) term annotations of genes by employing the directed acyclic graph structure of GO and the network of protein-protein interactions (PPIs). The approach is designed based on the fact that two proteins that interact biophysically would be in physical proximity of each other, would possess complementary molecular function, and play role in related biological processes. Predicted GO terms were ranked according to their relative association scores and the approach was evaluated quantitatively by plotting the precision versus recall values and F-scores (the harmonic mean of precision and recall) versus varying thresholds. Precisions of similar to 58% and similar to 40% for localization and functions respectively of proteins were determined at a threshold of similar to 30 (top 30 GO terms in the ranked list). Comparison with function prediction based on semantic similarity among nodes in an ontology and incorporation of those similarities in a k nearest neighbor classifier confirmed that our results compared favorably. Conclusions: This approach was applied to predict the cellular component and molecular function GO terms of all human proteins that have interacting partners possessing at least one known GO annotation. The list of predictions is available at http://severus.dbmi.pitt.edu/engo/GOPRED.html. We present the algorithm, evaluations and the results of the computational predictions, especially for genes identified in GWAS studies to be associated with diseases, which are of translational interest.