![]() ![]() LogCPM signature of the high-value genes was significantly higher in mesenchymal samples in 9 out of 17 tested cancer types, including HCC. Samples were stratified into epithelial or mesenchymal sample types based on gene expression signatures. (D) Expression of the high-value nodes from the Huh7-Fzd2 KiRNet model in TCGA cohorts. This subnetwork serves as the final KiRNet model for our experimental system: a focused subnetwork, centered around the predicted key kinases, that predicts the proteins and relationships most critical for the phenotype of interest. We created a high-value subnetwork of 166 proteins by using an empirical closeness cutoff to maximize the enrichment of differentially phosphorylated nodes, while also minimizing the size of the subnetwork ( Figures 1D and S2B see STAR Methods). Thus, when there are no additional data present, this “refined closeness” can be used as a topological predictor of a node's differential regulation and, consequently, its functional importance for the kinase-mediated phenotype. Comparing these measures with our quantitative phosphopeptide data, we found closeness, defined as the inverse of the average distance between a node and all other nodes in the network, was the most effective at predicting differentially phosphorylated proteins ( Figures 1C and S2A). The previously defined edge weights ensure that even these local, distance-based centralities are not dominated by hub nodes. Although these two metrics are not independent, as the phosphopeptide data are biased toward the kinome- and kinase-interacting proteins, this validates the use of kinase enrichment as an a priori means of identifying a meaningful kinome-centered subnetwork and provides further support for the flexibility of KiRNet. The enrichment of differentially phosphorylated nodes (compared with Huh7 WT) also peaked at a distance cutoff of 11 (p < 0.001), reinforcing this as a meaningful cutoff ( Figure 1B). For mesenchymal cancer cells (Huh7-Fzd2), we exploited quantitative phosphopeptide data that provided additional insight into which proteins are likely to be functionally important for these cells' growth. This enrichment peaked at a cutoff of 11 (p < 0.001 Figure 1B). We then created series of hypothetical subnetworks by enforcing a functional distance cutoff at integer values between 1 and 30 (arbitrary units) and calculated the enrichment of kinases in each subnetwork (see STAR Methods). To focus on kinase-centered subnetworks, we assigned every node a “functional distance” equal to the weighted, undirected path distance to the nearest key kinase. Graphical abstractĪrmed with a contextualized PPI network and a priori edge weights to improve local network methods, we sought to identify a small, testable subnetwork that captures the differential signaling in mesenchymal HCC (Huh7-Fzd2) cells. Finally, analysis of clinical data shows that mesenchymal tumors exhibit significantly higher average expression of the 166 corresponding genes than epithelial tumors for nine different cancer types. These proteins exhibit FZD2-dependent differential phosphorylation, and genetic knockdown studies validate their role in maintaining a mesenchymal cell state. KiRNet produces a network model consisting of 166 high-value proteins. We apply KiRNet to uncover molecular regulators of mesenchymal cancer cells driven by overexpression of Frizzled 2 (FZD2). This method, dubbed KiRNet, uses an a priori edge-weighting strategy based on node degree to establish a pipeline from a kinase inhibitor screen to the generation of a predictive PPI subnetwork. We develop a network propagation method that integrates kinase-inhibitor-focused functional screens with known protein-protein interactions (PPIs). The ever-increasing size and scale of biological information have popularized network-based approaches as a means to interpret these data. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |