Identifying reproducible cancer-associated highly expressed genes with important functional significances using multiple datasets

Identifying differentially expressed (DE) genes between cancer and normal tissues is of basic importance for studying cancer mechanisms. However, current methods, such as the commonly used Significance Analysis of Microarrays (SAM), are biased to genes with low expression levels. Recently, we proposed an algorithm, named the pairwise difference (PD) algorithm, to identify highly expressed DE genes based on reproducibility evaluation of top-ranked expression differences between paired technical replicates of cells under two experimental conditions. In this study, we extended the application of the algorithm to the identification of DE genes between two types of tissue samples (biological replicates) based on several independent datasets or sub-datasets of a dataset, by constructing multiple paired average gene expression profiles for the two types of samples. Using multiple datasets for lung and esophageal cancers, we demonstrated that PD could identify many DE genes highly expressed in both cancer and normal tissues that tended to be missed by the commonly used SAM. These highly expressed DE genes, including many housekeeping genes, were significantly enriched in many conservative pathways, such as ribosome, proteasome, phagosome and TNF signaling pathways with important functional significances in oncogenesis.

Extracting a few functionally reproducible biomarkers to build robust subnetwork-based classifiers for the diagnosis of cancer

In microarray-based case-control studies of a disease, people often attempt to identify a few diagnostic or prognostic markers amongst the most significant differentially expressed (DE) genes. However, the reproducibility of DE genes identified in different studies for a disease is typically very low. To tackle the problem, we could evaluate the reproducibility of DE genes across studies and define robust markers for disease diagnosis using disease-associated protein-protein interaction (PPI) subnetwork. Using datasets for four cancer types, we found that the most significant DE genes in cancer exhibit consistent up- or down-regulation in different datasets. For each cancer type, the 5 (or 10) most significant DE genes separately extracted from different datasets tend to be significantly coexpressed and closely connected in the PPI subnetwork, thereby indicating that they are highly reproducible at the PPI level. Consequently, we were able to build robust subnetwork-based classifiers for cancer diagnosis.