β-catenin-independent regulation of Wnt target genes by RoR2 and ATF2/ATF4 in colon cancer cells

Wnt signaling is an evolutionarily conserved signaling route required for development and homeostasis. While canonical, β-catenin-dependent Wnt signaling is well studied and has been linked to many forms of cancer, much less is known about the role of non-canonical, β-catenin-independent Wnt signaling. Here, we aimed at identifying a β-catenin-independent Wnt target gene signature in order to understand the functional significance of non-canonical signaling in colon cancer cells. Gene expression profiling was performed after silencing of key components of Wnt signaling pathway and an iterative signature algorithm was applied to predict pathway-dependent gene signatures. Independent experiments confirmed several target genes, including PLOD2, HADH, LCOR and REEP1 as non-canonical target genes in various colon cancer cells. Moreover, non-canonical Wnt target genes are regulated via RoR2, Dvl2, ATF2 and ATF4. Furthermore, we show that the ligands Wnt5a/b are upstream regulators of the non-canonical signature and moreover regulate proliferation of cancer cells in a β-catenin-independent manner. Our experiments indicate that colon cancer cells are dependent on both β-catenin-dependent and –independent Wnt signaling routes for growth and proliferation.

The detailed steps of bioinformatical analyses for identification of meaningful biological clusters on the basis of RNAi RNAseq HCT116 data. (c) Clusters derived from randomized data consisted of large gene sets correlated across a few conditions. In contrast, clusters derived from the experimental data exhibited a distinct pattern with small gene sets co-regulated across several conditions. Each dot on the graphic represents one cluster. 99.7% of all clusters generated from randomized data consisted of less than 2 conditions and more than 900 genes.

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Initilize with a randomly chosen gene set G 0 siβ-catenin β-catenin-dependent gene clusters

Non-specific filtering
Next, we remove not expressed genes and genes exhibiting a low variability across all samples.

Data normalization
Before clutsering the expression matrix is scaled and centered. Two normalized matrices are generated: the gene-wise (row-wise) normalized and the sample-wise (column-wise) normalized expression matrix.

Differential expression analysis
Next we selected samples with high differences in EVI expression, which is involved in Wnt secretion. In this analysis we assume that in Samples with low EVI expression, complete Wnt secretion and hence signaling is impaired compared to samples with high EVI expression.

EVI Expression in TCGA data set
Rel. expression

Statistical test
We use a t-test statistic to identify differentially expressed genes between low EVI samples and high EVI samples. Finally p-values are corrected for multiple testing.