Meta-analysis of several gene lists for distinct types of cancer: A simple way to reveal common prognostic markers

BACKGROUND: Although prognostic biomarkers specific for particular cancers have been discovered, microarray analysis of gene expression profiles, supported by integrative analysis algorithms, helps to identify common factors in molecular oncology. Similarities of Ordered Gene Lists (SOGL) is a recently proposed approach to meta-analysis suitable for identifying features shared by two data sets. Here we extend the idea of SOGL to the detection of significant prognostic marker genes from microarrays of multiple data sets. Three data sets for leukemia and the other six for different solid tumors are used to demonstrate our method, using established statistical techniques. RESULTS: We describe a set of significantly similar ordered gene lists, representing outcome comparisons for distinct types of cancer. This kind of similarity could improve the diagnostic accuracies of individual studies when SOGL is incorporated into the support vector machine algorithm. In particular, we investigate the similarities among three ordered gene lists pertaining to mesothelioma survival, prostate recurrence and glioma survival. The similarity-driving genes are related to the outcomes of patients with lung cancer with a hazard ratio of 4.47 (p = 0.035). Many of these genes are involved in breakdown of EMC proteins regulating angiogenesis, and may be used for further research on prognostic markers and molecular targets of gene therapy for cancers. CONCLUSION: The proposed method and its application show the potential of such meta-analyses in clinical studies of gene expression profiles

Lower levels of maternal capital in early life predict offspring obesity in adulthood

Background: As of 2013, 65% of the world’s population lived in countries where overweight/obesity kills more people than being underweight. Evolutionary perspectives provide a holistic understanding of both how and why obesity develops and its long-term implications. Aim: To test whether the maternal capital hypothesis, an evolutionary perspective, is viable for explaining the development of obesity in adulthood. Subjects and methods: Restricted-use data from the National Longitudinal Study of Adolescent Health (Add Health; n = 11 403) was analysed using logistic regressions. The sample included adolescents and their biological mothers. Results: The odds of obesity in adulthood increased by 22% for every standard deviation increase in lack of maternal capital (Exp (B) = 1.22, p < .001). That is, individuals whose mothers were young, of an ethnic minority and had short breastfeeding durations were more likely to be obese in adulthood, even after controlling for other factors in infancy, adolescence and adulthood. The results showed that those whose mothers had lower capital were more prone to later life disease (specifically, obesity). Conclusion: The maternal capital perspective is useful for explaining how and why early life characteristics (including maternal resources) predict obesity in adulthood. Implications of the findings are discussed

Integrated proteomic analysis of post-translational modifications by serial enrichment

We report a mass spectrometry–based method for the integrated analysis of protein expression, phosphorylation, ubiquitination and acetylation by serial enrichments of different post-translational modifications (SEPTM) from the same biological sample. this technology enabled quantitative analysis of nearly 8,000 proteins and more than 20,000 phosphorylation, 15,000 ubiquitination and 3,000 acetylation sites per experiment, generating a holistic view of cellular signal transduction pathways as exemplified by analysis of bortezomib-treated human leukemia cells