Characterizing the prognostic and therapeutic value of necroptosis in sarcoma based on necroptosis subtypes.

Necroptosis, a type of necrotic cell death independent of caspase regulation, is mainly mediated by receptor interacting serine/threonine kinase 1 (RIPK1), receptor interacting serine/threonine kinase 3 (RIPK3) and mixed lineage kinase domain-like (MLKL). Necroptosis plays an essential role in many tumors. However, the potential roles of necroptosis in tumor microenvironment (TME) of sarcoma (SARC) remain unknown. This study analyzed the expression, prognosis, genetic alterations of necroptosis genes in SARC. We identified two subtypes (cluster A and B) by performing unsupervised consensus clustering. Cluster A and B greatly differed in prognosis and immune infiltration, with cluster A showing more favorable prognosis, higher immune infiltration and higher expression levels of necroptosis genes than cluster B. Based on the differentially expressed genes (DEGs) between two clusters, a necroptosis scoring system was developed for predicting overall survival of SARC patients. Patients with high necroptosis score had worse survival status, with a decreased infiltration level of most immune cells. Our findings demonstrated the potential role of necroptosis in regulating tumor microenvironment and the prognostic value of necroptosis-related genes for SARC patients.

A Note on the Specification of Error Structures in Latent Interaction Models.

Latent interaction models have motivated a great deal of methodological research, mainly in the area of estimating such models. Product-indicator methods have been shown to be competitive with other methods of estimation in terms of parameter bias and standard error accuracy, and their continued popularity in empirical studies is due, in part, to their straightforward implementation and relative ease of estimation in mainstream structural equation modeling software. In recent years, the impact of different specifications of the mean structure of the structural model has been the focus of a fair amount of investigation in this area. Yet the effects of misspecification of the error structure of the observed variables implied by the model have not been investigated. In this study, the authors demonstrate algebraically the ramifications of misspecifying these error structures for the unconstrained product-indicator approach. Recommendations to practitioners based on these results are discussed.

On Latent Growth Models for Composites and Their Constituents.

Over the last decade and a half, latent growth modeling has become an extremely popular and versatile technique for evaluating longitudinal change and its determinants. Most common among the models applied are those for a single measured variable over time. This model has been extended in a variety of ways, most relevant for the current work being the multidomain and the second-order latent growth models. Whereas the former allows for growth function characteristics to be modeled for multiple outcomes simultaneously, with the degree of growth characteristics' relations assessed within the model (e.g., cross-domain slope factor correlations), the latter models growth in latent outcomes, each of which has effect indicators repeated over time. But what if one has an outcome that is believed to be formative relative to its indicator variables rather than latent? In this case, where the outcome is a composite of multiple constituents, modeling change over time is less straightforward. This article provides analytical and applied details for simultaneously modeling growth in composites and their constituent elements, including a real data example using a general computer self-efficacy questionnaire.