As main inflammatory mediators, the downstream targets are inhibited, thus, asthma could be controlled. Conclusion Our study indicates that dexamethasone increases the expression of PTEN in asthmatic mice and human A549 cells. This induction results from the stimulation of PTEN transcription, and may involve the increased his tone acetylation at the PTEN promoter. A new mechan ism of action is proposed for the anti inflammatory effect of glucocorticoids in asthma treatment. Specific regulation of PTEN expression in human airways may be useful for the treatment of asthma. Declaration of interests The authors declare that they have no competing inter ests. The authors alone are responsible for the content and writing of the paper.

Background The rapid progress in gene expression array technology in the past decade has greatly facilitated our understanding of the genetic aspect of various diseases. Knowledge based approaches, such as gene set or pathway analysis, have become increasingly popular. In such gene sets/pathways, groups of genes act in concert to accomplish tasks related to a cellular process and the resulting genetic pathway effects may manifest themselves through phenotypic changes, such as occurrence of disease. Thus it is poten tially more meaningful to study the overall effect of a group of genes rather than a single gene, as single gene analysis may miss important effects on pathways and dif ficult to reproduce from studies to studies. Researchers have made significant progress in identifying metabolic or signaling pathways based on expression array data.

Meanwhile, new tools for identification of pathways, such as GenMAPP, Pathway Processor, MAPPFinder, have made pathway data more widely available. However, It is a challenging task to model the pathway data and test for a potentially complex pathway effect on a disease out come. One way to model pathway data is through the linear model approach, where the pathway effect is represented by a linear combination of individual gene effects. This approach has several limitations. Activities of genes within a pathway are often complicated, thus a linear model is often insufficient to capture the relationship between these genes. Furthermore, genes within a path way tend to interact with each other. Such interactions are not taken into account by the linear model approach.

Dacomitinib In this paper we propose a nonparametric approach, the kernel machine regression, to model a pathway effect. The kernel machine method, with the support vector machine as a most popular example, has emerged in the last decade as a powerful machine learning technique in high dimensional settings. This method provides a flexi ble way to model linear and nonlinear effects of variables and gene gene interactions, unifies the model building procedure in both one and multi dimensional settings, and shows attractive performance compared to other non parametric methods such as splines. Liu et al.