Previous reports have suggested that the drug inhibits the proliferation of some HCC cell lines [37,38,39,40]. phosphorylation in the EGFR and EphA2 signaling pathways. The CD90+ cells exhibited higher abundance of AKT, EphA2 and its phosphorylated form at Ser897, whereas the EpCAM+ cells exhibited higher abundance of ERK, RSK and its phosphorylated form. This demonstrates that pro-oncogenic, ligand-independent EphA2 signaling plays a dominant role in CD90+ cells with higher motility and metastatic activity than EpCAM+ cells. We also showed that an AKT inhibitor reduced the proliferation and survival of CD90+ cells but did not affect those of EpCAM+ cells. Taken together, our results suggest that AKT activation may be a key pro-oncogenic regulator in HCC. 0.05, Welchs ANOVA with multiple correction). For Bay 59-3074 the significant proteins and phosphoproteins, a Games-Howell post-hoc test was used for pairwise Bay 59-3074 comparisons. * 0.05, ** 0.01, *** 0.001. 2.3. Partial Least Squares Analysis Bay 59-3074 Reveals Anti- and Pro-Oncogenic Activity of Epcam+ and Cd90+ Cells We further examined the differential expression of proteins and phosphoproteins among the GMM clusters using partial least squares (PLS) analysis [33,34,35]. PLS is a multivariate technique similar to principal component analysis (PCA), with the exception that PLS extracts latent variables (LVs) that maximize the covariance between independent and dependent datasets using singular value decomposition. Thus, when applied Rabbit Polyclonal to KSR2 to the current data, it seeks to find LVs that maximize the covariance between the RPPA data and the GMM clusters. These LVs are likened to the factor scores in the PCA. The LVs also have a counterpart of factor loadings in PCA, a pair of singular vectors, or 0.0001). Figure 3A shows the cluster salience, which corresponds to the optimal contrast between GMM Bay 59-3074 clusters that accounts for variation in the RPPA data. This indicates that the current data are best characterized by contrasting activation between CD90+ and EpCAM+ cells. In addition, the fact the salience of the Neutral cell cluster is almost zero shows that Neutral cells do not have a common pattern of protein manifestation and phosphorylation into which they are clustered. Bay 59-3074 This means that they may be heterogeneous in their protein manifestation and phosphorylation, which explains why hierarchical clustering did not reveal a distinct cluster for Neutral cells (Number 1B). Open in a separate window Number 3 Mean-centered PLS analysis of the RPPA data. (A) Pub graph of cluster salience for the significant latent variable (LV1). The significance was assessed using 1000-fold permutation checks. (B) Bootstrap percentage (BSR) of the protein salience for LV1. The BSR is the percentage of salience to its bootstrap estimated SE, which approximates a z-score. The depicted BSRs are based on 1000 bootstrap resampling. * |BSR| 1.96 ( 0.05), ** |BSR| 2.58 ( 0.01), *** |BSR| 3.03 ( 0.001). (C) Score storyline of LVs for the RPPA data. Number 3B depicts the protein salience, which illustrates the pattern of protein manifestation and phosphorylation that optimally differentiates the GMM clusters as recognized in the cluster salience (i.e., between the CD90+ and EpCAM+ clusters). Bad salience shows the proteins and phosphoproteins that are more abundant in CD90+ cells than in EpCAM+ cells; positive protein salience shows those that are more abundant in EpCAM+ cells than in CD90+ cells. The results are in agreement with those from the univariate analysis (Number 2), excepting that pERK level was not statistically significant. This indicates that when modified by multivariate analysis, the contribution of pERK was not large plenty of to distinguish between the CD90+ and EpCAM+ clusters. Thus, we found that CD90+ cells exhibited significantly higher levels of AKT, EphA2 and pEphA2-Ser897, which is definitely consistent with the upregulation of pro-oncogenic, ligand-independent.