Bootstrapping integrative hypothesis test for identifying biomarkers that differentiates lung cancer and chronic obstructive pulmonary disease

Abstract Different from the common approaches that use either hypothesis test or classifier for biomarker discovery, we applied the integrative hypothesis test (IHT) that combined both to identifying miRNAs for differentiation between lung cancer and Chronic Obstructive Pulmonary Disease (shortly L-C differentiation) on GEO data set GSE24709, and further extended IHT implementation by bootstrapping aided ranking and mean-variance based reliability check, which outputs a list of the top-15 differentially expressed miRNAs that confirmed the previously reported 14 miRNAs for L-C differentiation from a very different perspective plus an additional one. Moreover, we conducted a literature survey for a further explanation via dividing the 15 miRNAs into subclasses based on known relevances to the two diseases. Also, every pair of 15 miRNAs is exhaustively examined on their joint effect via p -value, misclassification, and correlation, which identifies core pairs and linked cliques as joint miRNAs biomarkers.