Multi-dimensional discovery of biomarker and phenotype complexes.

BACKGROUND: Given the rapid growth of translational research and personalized healthcare paradigms, the ability to relate and reason upon networks of bio-molecular and phenotypic variables at various levels of granularity in order to diagnose, stage and plan treatments for disease states is highly desirable. Numerous techniques exist that can be used to develop networks of co-expressed or otherwise related genes and clinical features. Such techniques can also be used to create formalized knowledge collections based upon the information incumbent to ontologies and domain literature. However, reports of integrative approaches that bridge such networks to create systems-level models of disease or wellness are notably lacking in the contemporary literature. RESULTS: In response to the preceding gap in knowledge and practice, we report upon a prototypical series of experiments that utilize multi-modal approaches to network induction. These experiments are intended to elicit meaningful and significant biomarker-phenotype complexes spanning multiple levels of granularity. This work has been performed in the experimental context of a large-scale clinical and basic science data repository maintained by the National Cancer Institute (NCI) funded Chronic Lymphocytic Leukemia Research Consortium. CONCLUSIONS: Our results indicate that it is computationally tractable to link orthogonal networks of genes, clinical features, and conceptual knowledge to create multi-dimensional models of interrelated biomarkers and phenotypes. Further, our results indicate that such systems-level models contain interrelated bio-molecular and clinical markers capable of supporting hypothesis discovery and testing. Based on such findings, we propose a conceptual model intended to inform the cross-linkage of the results of such methods. This model has as its aim the identification of novel and knowledge-anchored biomarker-phenotype complexes.

Using gene co-expression network analysis to predict biomarkers for chronic lymphocytic leukemia.

BACKGROUND: Chronic lymphocytic leukemia (CLL) is the most common adult leukemia. It is a highly heterogeneous disease, and can be divided roughly into indolent and progressive stages based on classic clinical markers. Immunoglobin heavy chain variable region (IgVH) mutational status was found to be associated with patient survival outcome, and biomarkers linked to the IgVH status has been a focus in the CLL prognosis research field. However, biomarkers highly correlated with IgVH mutational status which can accurately predict the survival outcome are yet to be discovered. RESULTS: In this paper, we investigate the use of gene co-expression network analysis to identify potential biomarkers for CLL. Specifically we focused on the co-expression network involving ZAP70, a well characterized biomarker for CLL. We selected 23 microarray datasets corresponding to multiple types of cancer from the Gene Expression Omnibus (GEO) and used the frequent network mining algorithm CODENSE to identify highly connected gene co-expression networks spanning the entire genome, then evaluated the genes in the co-expression network in which ZAP70 is involved. We then applied a set of feature selection methods to further select genes which are capable of predicting IgVH mutation status from the ZAP70 co-expression network. CONCLUSIONS: We have identified a set of genes that are potential CLL prognostic biomarkers IL2RB, CD8A, CD247, LAG3 and KLRK1, which can predict CLL patient IgVH mutational status with high accuracies. Their prognostic capabilities were cross-validated by applying these biomarker candidates to classify patients into different outcome groups using a CLL microarray datasets with clinical information.

New bioinformatics approach to analyze gene expressions and signaling pathways reveals unique purine gene dysregulation profiles that distinguish between CD and UC.

BACKGROUND: Expression of purine genes is modulated by inflammation or experimental colitis and altered expression leads to disrupted gut function. We studied purine gene dysregulation profiles in inflammatory bowel disease (IBD) and determined whether they can distinguish between Crohn's disease (CD) and ulcerative colitis (UC) using Pathway Analysis and a new Comparative Analysis of Gene Expression and Selection (CAGES) method. METHODS: Raw datasets for 22 purine genes and 36 probe-sets from National Center for Biotechnology Information (NCBI) GEO (Gene Expression Omnibus) (http://www.ncbi.nlm.nih.gov/projects/geo/) were analyzed by National Cancer Institute (NCI) Biological Resources Branch (BRB) array tools for random-variance of multiple/36 t-tests in colonic mucosal biopsies or peripheral blood mononuclear cells (PBMCs) of CD, UC or control subjects. Dysregulation occurs in 59% of purine genes in IBD including ADORA3, CD73, ADORA2A, ADORA2B, ADAR, AMPD2, AMPD3, DPP4, P2RY5, P2RY6, P2RY13, P2RY14, and P2RX5. RESULTS: In CD biopsies, expression of ADORA3, AMPD3, P2RY13, and P2RY5 were negatively correlated with acute inflammatory score, Crohn's Disease Activity Index (CDAI) or disease chronicity; P2RY14 was positively correlated in UC. In mucosal biopsies or PBMCs, CD and UC were distinguished by unique patterns of dysregulation (up- or downregulation) in purine genes. Purine gene dysregulation differs between PBMCs and biopsies and possibly between sexes for each disease. Ingenuity Pathway Analysis (IPA) revealed significant associations between alterations in the expression of CD73 (upregulation) or ADORA3 (downregulation) and inflammatory or purine genes (