Abstract
Deciphering the network of signaling pathways in cancer via protein-protein interactions (PPIs) at the cellular level is a promising approach but remains incomplete. We used an in situ proximity ligation assay to identify and quantify 67 endogenous PPIs among 21 interlinked pathways in two hepatocellular carcinoma (HCC) cells, Huh7 (minimally migratory cells) and Mahlavu (highly migratory cells). We then applied a differential network biology analysis and determined that the novel interaction, CRKL-FLT1, has a high centrality ranking, and the expression of this interaction is strongly correlated with the migratory ability of HCC and other cancer cell lines. Knockdown of CRKL and FLT1 in HCC cells leads to a decrease in cell migration via ERK signaling and the epithelial-mesenchymal transition process. Our immunohistochemical analysis shows high expression levels of the CRKL and CRKL-FLT1 pair that strongly correlate with reduced disease-free and overall survival in HCC patient samples, and a multivariate analysis further established CRKL and the CRKL-FLT1 as novel prognosis markers. This study demonstrated that functional exploration of a disease network with interlinked pathways via PPIs can be used to discover novel biomarkers.
Highlights
We present a systems approach that computationally infers the interlinked pathways from numerous protein-protein interactions (PPIs) in Hepatocellular carcinoma (HCC) up-regulated genes and empirically detects endogenous PPIs using an in situ proximity ligation assay (PLA), which allows quantitative and localized detection of endogenous PPIs in cells [11]
We demonstrate that CRKL and FLT1 was involved in the ERK pathway and regulated the epithelial-mesenchymal transition (EMT) process in migration of HCC
The proteins belonging to the HCC-related pathways were detected to determine whether collected PPIs (POINeT) [21] or predicted PPIs (PIPS) [22] with overexpressed patterns can link each pathway and enable potential interlinking
Summary
Identification of HCC-related Pathways—In this over-representation analysis, N represents the total number of genes in the background population; n represents the number of HCC-related genes; and m denotes the number of genes within the given pathways. We represent the interlinked PPIs among M HCC-related pathways as an undirected simple graph G ϭ (VG, EG), where VG ϭ (v1, v2, 1⁄7 1⁄7 1⁄7, vM) a finite set of M vertices representing the M pathways, and EG ʚ VG ϫ VG is the set of edges representing the pairs of interlinked pathways Each such graph can equivalently be represented by a symmetric M ϫ M adjacency matrix AG, if pathway Vi has at least interlinked PPIs with pathway Vj and 0 and otherwise by the following Equations 2 and 3. Two proximity matrices C (Fig. 2B) and R (Fig. 2C) were calculated using the modified simple matching coefficient representing the between rows (PPIs) and between columns (pathways) association structure as shown in Equation 4,.
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