Heat shock protein 72 supports extracellular matrix production in metastatic mammary tumors.

This study identified tumorigenic processes most dependent on murine heat shock protein 72 (HSP72) in the mouse mammary tumor virus-PyMT mammary tumor model, which give rise to spontaneous mammary tumors that exhibit HSP72-dependent metastasis to the lung. RNA-seq expression profiling of Hspa1a/Hspa1b (Hsp72) WT and Hsp72-/- primary mammary tumors discovered significantly lower expression of genes encoding components of the extracellular matrix (ECM) in Hsp72 knockout mammary tumors compared to WT controls. In vitro studies found that genetic or chemical inhibition of HSP72 activity in cultured collagen-expressing human or murine cells also reduces mRNA and protein levels of COL1A1 and several other ECM-encoding genes. In search of a possible mechanistic basis for this relationship, we found HSP72 to support the activation of the tumor growth factor-ß-suppressor of mothers against decapentaplegic-3 signaling pathway and evidence of suppressor of mothers against decapentaplegic-3 and HSP72 coprecipitation, suggesting potential complex formation. Human COL1A1 mRNA expression was found to have prognostic value for HER2+ breast tumors over other breast cancer subtypes, suggesting a possible human disease context where targeting HSP72 may have a therapeutic rationale. Analysis of human HER2+ breast tumor gene expression data using a gene set comprising ECM-related gene and protein folding-related gene as an input to the statistical learning algorithm, Galgo, found a subset of these genes that can collectively stratify patients by relapse-free survival, further suggesting a potential interplay between the ECM and protein-folding genes may contribute to tumor progression.

Bayesian meta-analysis using SAS PROC BGLIMM.

Meta-analysis is commonly used to compare two treatments. Network meta-analysis (NMA) is a powerful extension for comparing and contrasting multiple treatments simultaneously in a systematic review of multiple clinical trials. Although the practical utility of meta-analysis is apparent, it is not always straightforward to implement, especially for those interested in a Bayesian approach. This paper demonstrates that the recently-developed SAS procedure BGLIMM provides an intuitive and computationally efficient means for conducting Bayesian meta-analysis in SAS, using a worked example of a smoking cessation NMA data set. BGLIMM gives practitioners an effective and simple way to implement Bayesian meta-analysis (pairwise and network, either contrast-based or arm-based) without requiring significant background in coding or statistical modeling. Those familiar with generalized linear mixed models, and especially the SAS procedure GLIMMIX, will find this tutorial a useful introduction to Bayesian meta-analysis in SAS.

Validation and psychometrics for the Health Skills Profile.

INTRODUCTION: Few measures can comprehensively explore the extent to which individuals are able to effectively identify areas of concern, create a personalized health action plan and target these areas for improvement. Thus, the aim of this paper was to validate the Health Skills Profile (HSP©) as a measure for assessing health-related skills and explore the relationship between the HSP skills with existing validated measures. METHOD: Participants completed a battery of self-report measures (including validated measures and visual analogue scales [VAS] that relate to each of the health-related skills) and the HSP measure online. RESULTS: We explored the association between each skill within the HSP with their corresponding validated measure. We found a significant positive relationship between the HSP skills and the validated measures. Further, we found a significant positive relationship between the HSP skills and the corresponding VAS. CONCLUSION: These findings suggest that the HSP can be combined with other assessment data to develop more complete personalized profiles of individual and organizational health and health behaviors.