An application of ontology-based filtering method in discovery of saliva biomarkers for gastric cancer

Saliva contains a large array of proteins and many of them can be very informative for human disease detection. Thus it has gradually emerged as a potential biofluid for the noninvasive detection of biomarkers for specific disease. In this study, we aimed to detect potential markers for gastric cancer in saliva through an integrative mining method. This strategy firstly started with the collection of salivary genes and upregulated genes relating with gastric cancer from public genomic profiles. Subsequently, a list of gene ontology were retrieved through the functional annotation enrichment analysis of the salivary proteins. Based on the ontology filtering and further assessment, a reduced list of 21 entities were harvested. For further exploration, we performed gene set pathway enrichment analysis and established protein-protein interaction network. Meanwhile, a few preferred potential markers which were much more likely discovered in saliva due to gastric cancer were proposed. Our work is an integrative and experimentalist-friendly application of the mining of public genomic profiles.