The importance of the tumor microenvironment in targeted anticancer therapies has been well recognized. Various protein factors participate in the cross-talk between tumor cells and non-malignant cells. Anterior gradient-2 (AGR2) is overexpressed in diverse human adenocarcinomas and it exists in both intracellular and extracellular spaces. Although intracellular AGR2 has been intensively investigated, the function of secreted AGR2, especially its exact mechanism of action is still poorly understood. Here we report that the secreted AGR2 promotes the angiogenesis and the invasion of vascular endothelial cells and fibroblasts by enhancing the activities of vascular endothelial growth factor (VEGF) and fibroblast growth factor 2 (FGF2). Further study indicated that AGR2 directly binds to these extracellular signaling molecules, and enhances their homodimerization. The extracellular AGR2 activity can be blocked to reduce angiogenesis and inhibit tumor growth in vitro and in vivo by a monoclonal antibody targeting the AGR2 self-dimerization region, and combined treatment with bevacizumab produced maximum inhibition effect. In conclusion, our investigation reveals a mechanism that directly links the secreted AGR2 with extracellular signaling networks, and we propose that the secreted AGR2 is a blockable molecular target, which acts as a chaperon-like enhancer to VEGF and FGF2.
The use of bifactor models has increased substantially in the past decade. However, bifactor models are prone to a nonidentification problem in the context of prediction that is not well recognized in the general research community. Moreover, the practical consequences of adopting different conceptualizations of hierarchical constructs when examining their predictive validity has received little attention. Therefore, Study 1 examined the statistical performance of bifactor models and investigated the effectiveness of an augmentation strategy to remedy the nonidentification problem. Monte Carlo simulations showed that the augmentation strategy is effective. The second simulation study demonstrated that researchers may arrive at different conclusions regarding the predictive validity of hierarchical constructs depending on their choice of models. In general, augmented bifactor models, which are restricted variants of the more general bifactor-(S·I-1) model, reasonably recovered the overall predictive validity ( R2) of hierarchical constructs and led to correct substantive conclusions regarding the incremental validity of facets regardless of the true data-generation model given a sufficiently large sample ( n ≥ 600). The authors discussed implications of those findings and made practical recommendations for further users of bifactor models.
Forced choice (FC) measures are gaining popularity as an alternative assessment format to single statement (SS) measures due to their potential in reducing the impact of various response styles and faking. However, a fundamental question remains to be answered: do FC and SS instruments measure the same underlying constructs? In addition, FC measures are theorized to be more cognitively challenging, so how would this feature influence respondents' reactions to FC measures compared to SS? Two studies were designed to answer these questions. Study 1 results showed that FC measures scored by the Multi-unidimensional Pairwise Preference Model (MUPP) and SS measures scored with an ideal point model yielded similar factor structures and almost identical criterion-related validity across 12 criteria. Both formats also had similar pattern of marginal reliabilities and test-retest reliabilities. Study 1 findings were replicated in Study 2.In addition, we found strong evidence for convergent validity between the two formats. Though the FC format was perceived to be more difficult, respondents showed no differential preference and expressed similar level of emotional and cognitive reactions to the two formats.
Item response theory (IRT) models have a number of advantages for developing and evaluating scales in organizational research. However, these advantages can be obtained only when the IRT model used to estimate the parameters fits the data well. Therefore, examining IRT model fit is important before drawing conclusions from the data. To test model fit, a wide range of indices are available in the IRT literature and have demonstrated utility in past research. Nevertheless, the performance of many of these indices for detecting misfit has not been directly compared in simulations. The current study evaluates a number of these indices to determine their utility for detecting various types of misfit in both dominance and ideal point IRT models. Results indicate that some indices are more effective than others but that none of the indices accurately detected misfit due to multidimensionality in the data. The implications of these results for future organizational research are discussed.
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