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Pancreatic cancer (PC) is a malignancy with little/no warning signs before the disease reaches its ultimate stages. Currently early detection of PC is very difficult because most patients have non-specific symptoms leading to postponing the correct diagnosis. In this study, using multiple bioinformatics tools, we integrated various serum expression profiles of miRNAs to find the most significant miRNA signatures helpful in diagnosis of PC and constructed novel miRNA diagnosis models for PC. Altogether, 27 differentially expressed miRNAs (DEMs) showed area under curve (AUC) score >80%. The most promising miRNAs, miR-1469 and miR-4530, were individually able to distinguish two groups with the highest specificity and sensitivity. By using multivariate cox regression analyses, 5 diagnostic models consisting of different combinations of miRNAs, based on their significant expression algorithms and functional properties were introduced. The correlation model consisting of miR-125a-3p, miR-5100 and miR-642b-3p was the most promising model in the diagnosis of PC patients from healthy controls with an AUC of 0.95, Sensitivity 0.98 and Specificity 0.97. Validation analysis was conducted for considered miRNAs on a final cohort consist of the microarray data from two other datasets (GSE112264 & GSE124158) . These results provide some potential biomarkers for PC diagnosis after testing in large case-control and cohort studies.

Identification of potential microRNA panels for pancreatic cancer diagnosis using microarray datasets and bioinformatics methods