Predicting novel salivary biomarkers for the detection of pancreatic cancer using biological feature-based classification.

AIM: The use of saliva as a diagnostic fluid enables non-invasive sampling and thus is a prospective sample for disease tests. This study fully utilized the information from the salivary transcriptome to characterize pancreatic cancer related genes and predict novel salivary biomarkers. METHODS: We calculated the enrichment scores of gene ontology (GO) and pathways annotated in Kyoto Encyclopedia of Genes and Genomes database (KEGG) for pancreatic cancer-related genes. Annotation of GO and KEGG pathway characterize the molecular features of genes. We employed Random Forest classification and incremental feature selection to identify the optimal features among them and predicted novel pancreatic cancer-related genes. RESULTS: A total of 2175 gene ontology and 79 KEGG pathway terms were identified as the optimal features to identify pancreatic cancer-related genes. A total of 516 novel genes were predicted using these features. We discovered 29 novel biomarkers based on the expression of these 516 genes in saliva. Using our new biomarkers, we achieved a higher accuracy (92%) for the detection of pancreatic cancer. Another independent expression dataset confirmed that these novel biomarkers performed better than the previously described markers alone. CONCLUSION: By analyzing the information of the salivary transcriptome, we predict pancreatic cancer-related genes and novel salivary gene markers for detection.