Newly Constructed Network Models of Different WNT Signaling Cascades Applied to Breast Cancer Expression Data

Abstract

IntroductionWNT signaling is a complex process comprising multiple pathways: the canonical β-catenin-dependent pathway and several alternative non-canonical pathways that act in a β-catenin-independent manner. Representing these intricate signaling mechanisms through bioinformatic approaches is challenging. Nevertheless, a simplified but reliable bioinformatic WNT pathway model is needed, which can be further utilized to decipher specific WNT activation states within e.g. high-throughput data.ResultsIn order to build such a model, we collected, parsed, and curated available WNT signaling knowledge from different pathway databases. The data were assembled to construct computationally suitable models of different WNT signaling cascades in the form of directed signaling graphs. This resulted in four networks representing canonical WNT signaling, non-canonical WNT signaling, the inhibition of canonical WNT signaling and the regulation of WNT signaling pathways, respectively. Furthermore, these networks were integrated with microarray and RNA sequencing data to gain deeper insight into the underlying biology of gene expression differences between MCF-7 and MDA-MB-231 breast cancer cell lines, representing weakly and highly invasive breast carcinomas, respectively. Differential genes up-regulated in the MDA-MB-231 compared to the MCF-7 cell line were found to display enrichment in the gene set originating from the non-canonical network. Moreover, we identified and validated differentially regulated modules representing canonical and non-canonical WNT pathway components specific for the aggressive basal-like breast cancer subtype.ConclusionsIn conclusion, we demonstrated that these newly constructed WNT networks reliably reflect distinct WNT signaling processes. Using transcriptomic data, we shaped these networks into comprehensive modules of the genes implicated in the aggressive basal-like breast cancer subtype and demonstrated that non-canonical WNT signaling is important in this context. The topology of these networks can be further refined in the future by integration with complementary data such as protein-protein interactions, in order to gain greater insight into signaling processes.

Ror2 Signaling and Its Relevance in Breast Cancer Progression

“…(30). In brief, the common targets were first mapped onto the nodes of the non-canonical Wnt signaling network model, and the nodes induced by the mapped targets were used as terminal nodes for the Steiner tree analysis as implemented in the SteinerNet R-package (31).…”

“…The authors acknowledge Lena Ries, Matthias Schulz, and Meike Schaffrinski (University Medical Center Göttingen, Germany) for their excellent technical assistance. A part of this study was published in the dissertation thesis of Bayerlová (30). …”

Role of Wnt/β-catenin, Wnt/c-Jun N-terminal kinase and Wnt/Ca2+ pathways in cisplatin-induced chemoresistance in ovarian cancer

“…Previous studies have identified that abnormal Wnt signaling serves a role in the development of breast (11), gastric (12), lung (13), prostate (14) and endometrial (15) cancers. In ovarian cancer, persistent activation of Wnt signaling was observed to increase cell survival, and several studies have concluded that aberrant regulation of Wnt signaling induces tumor cell chemoresistance (16–18).…”

Network analysis allows to unravel breast cancer molecular features and to identify novel targets

“…A set of STAT3 and WNT signatures was collected (Alvarez et al, 2005;Azare et al, 2007;Bayerlová, Klemm, Kramer, & Pukrop, 2015;Dauer et al, 2005;Labbe et al, 2007;Sandsmark et al, 2017;Sonnenblick et al, 2015;Robert W Tell & Horvath, 2014;Willert et al, 2002;Ziegler et al, 2005) and individual enrichments of modules for each signature were calculated with a Fisher's exact test using as using as background the list of genes not falling in the "unconnected" compartment. P-values were adjusted with the p.adjust function and the Holm method considering all the tests performed.…”

“…The most strongly connected nodes (Topological Overlap ≥ 0.02) were retained for individual modules' visualization in Cytoscape 3.7.0 (Shannon et al, 2003). The whole network was filtered for connections with Topological Overlap ≥ 0.05 and represented with Gephi 0.9.2 (Bastian & Heymann, 2009).…”