An integrated approach of gene expression and DNA-methylation profiles of WNT signaling genes uncovers novel prognostic markers in Acute Myeloid Leukemia. Issue 4 (December 2015)

• Record Type: Journal Article
• Authors: Taskesen, Erdogan; Staal, Frank; Reinders, Marcel
• Is Part Of: BMC bioinformatics. Volume 16:Issue 4(2015)
• Page Start: 1
• Page End: 13
• Publication Date: 2015-12
• Subjects: Data integration -- Gene expression -- DNA-methylation -- Prognostic markers -- Acute Myeloid Leukemia -- Wingless-Int (WNT); Bioinformatics -- Periodicals; Computational biology -- Periodicals; 570.285
• DOI: 10.1186/1471-2105-16-S4-S4
• Languages: English
• ISSNs: 1471-2105

Abstract

Abstract Background The wingless-Int (WNT) pathway has an essential role in cell regulation of hematopoietic stem cells (HSC). For Acute Myeloid Leukemia (AML), the malignant counterpart of HSC, currently only a selective number of genes of the WNT pathway are analyzed by using either gene expression or DNA-methylation profiles for the identification of prognostic markers and potential candidate targets for drug therapy. It is known that mRNA expression is controlled by DNA-methylation and that specific patterns can infer the ability to differentiate biological differences, thus a combined analysis using all WNT annotated genes could provide more insight in the WNT signaling. Approach We created a computational approach that integrates gene expression and DNA promoter methylation profiles. The approach represents the continuous gene expression and promoter methylation profiles with nine discrete mutually exclusive scenarios. The scenario representation allows for a refinement of patient groups by a more powerful statistical analysis, and the construction of a co-expression network. We focused on 268 WNT annotated signaling genes that are derived from the molecular signature database. Results Using the scenarios we identified seven prognostic markers for overall survival and event-free survival. Three genes are novel prognostic markers; two with favorable outcome (PSMD2, PPARD ) and one with unfavorable outcome (XPNPEP ). The remaining four genes (LEF1, SFRP2, RUNX1, andAXIN2 ) were previously identified but we could refine the patient groups. Three AML risk groups were further analyzed and the co-expression network showed that only the good risk group harbors frequent promoter hypermethylation and significantly correlated interactions with proteasome family members. Conclusion Our results provide novel insights in WNT signaling in AML, we discovered new and previously identified prognostic markers and a refinement of the patient groups.