It is currently unclear whether parenteral selenium supplementation should be recommended in the management of critically ill patients. Here we conducted a systematic review and meta-analysis to assess the efficacy of parenteral selenium supplementation on clinical outcomes. Randomized trials investigating parenteral selenium supplementation administered in addition to standard of care to critically ill patients were included. CENTRAL, Medline, EMBASE, the Science Citation Index, and CINAHL were searched with complementary manual searches. The primary outcome was all-cause mortality. Trials published in any language were included. Two authors independently extracted data and assessed trial quality. A third author was consulted to resolve disagreements and for quality assurance. Twelve trials were included and meta-analysis was performed on nine trials that recruited critically ill septic patients. These comprised 965 participants in total. Of these, 148 patients (30.7%) in the treatment groups, and 180 patients (37.3%) in control groups died. Parenteral selenium treatment significantly reduced all-cause mortality in critically ill patients with sepsis (relative risk [RR] 0.83, 95% CI 0.70–0.99, p = 0.04, I² = 0%). Subgroup analyses demonstrated that the administration schedule employing longer duration (RR 0.77, 95% CI 0.63–0.94, p = 0.01, I² = 0%), loading boluses (RR 0.73, 95% CI 0.58–0.94, p = 0.01, I² = 0%) or high-dose selenium treatment (RR 0.77, 95% CI 0.61–0.99, p = 0.04, I² = 0%) might be associated with a lower mortality risk. There was no evidence of adverse events. Parenteral selenium supplementation reduces risk of mortality among critically ill patients with sepsis. Owing to the varied methodological quality of the studies, future high-quality randomized trials that directly focus on the effect of adequate-duration of parenteral selenium supplementation for severe septic patients are needed to confirm our results. Clinicians should consider these findings when treating this high-risk population. PROSPERO 2011; CRD42011001768

This article proposes a Bayesian approach for the sparse group selection problem in the regression model. In this problem, the variables are partitioned into different groups. It is assumed that only a small number of groups are active for explaining the response variable, and it is further assumed that within each active group only a small number of variables are active. We adopt a Bayesian hierarchical formulation, where each candidate group is associated with a binary variable indicating whether the group is active or not. Within each group, each candidate variable is also associated with a binary indicator, too. Thus, the sparse group selection problem can be solved by sampling from the posterior distribution of the two layers of indicator variables. We adopt a group-wise Gibbs sampler for posterior sampling. We demonstrate the proposed method by simulation studies as well as real examples. The simulation results show that the proposed method performs better than the sparse group Lasso in terms of selecting the active groups as well as identifying the active variables within the selected groups. Supplementary materials for this article are available online.

Paget¹ proposed the seed-and-soil hypothesis in 1889 through observing the distribution of disseminated cancer from 735 necropsies after breast cancer. Owing to research largely conducted in the 1970s and 1980s, we now know that disseminated tumor cells travel around the whole body but few can survive and form metastatic lesions. Therefore, to date, the best hypothesis to explain the phenomenon of organ-specific metastases is still the seed-and-soil theory.^2-5

A novel approach to finding candidate genes by using gene expression data through liquid association is developed and used to identify multiple sclerosis susceptibility candidate genes.

Background: Chemosensitivity and tumor metastasis are two primary issues in cancer management. Cancer cells often exhibit a wide range of sensitivity to anti-cancer compounds. To gain insight on the genetic mechanism of drug sensitivity, one powerful approach is to employ the panel of 60 human cancer cell lines developed by the National Cancer Institute (NCI). Cancer cells also show a broad range of invasion ability. However, a genome-wide portrait on the contributing molecular factors to invasion heterogeneity is lacking. Methods: Our lab performed an invasion assay on the NCI-60 panel. We identified invasion-associated (IA) genes by correlating our invasion profiling data with the Affymetrix gene expression data on NCI-60. We then employed the recently released chemosensitivity data of 99 anti-cancer drugs of known mechanism to investigate the gene-drug correlation, focusing on the IA genes. Afterwards, we collected data from four independent drug-testing experiments to validate our findings on compound response prediction. Finally, we obtained published clinical and molecular data from two recent adjuvant chemotherapy cohorts, one on lung cancer and one on breast cancer, to test the performance of our gene signature for patient outcome prediction. Results: First, we found 633 IA genes from the invasion-gene expression correlation study. Then, for each of the 99 drugs, we obtained a subset of IA genes whose expression levels correlated with drug-sensitivity profiles. We identified a set of eight genes (EGFR, ITGA3, MYLK, RAI14, AHNAK, GLS, IL32 and NNMT) showing significant gene-drug correlation with paclitaxel, docetaxel, erlotinib, everolimus and dasatinib. This eight-gene signature (derived from NCI-60) for chemosensitivity prediction was validated by a total of 107 independent drug tests on 78 tumor cell lines, most of which were outside of the NCI-60 panel. The eight-gene signature predicted relapse-free survival for the lung and breast cancer patients (log-rank P = 0.0263; 0.00021). Multivariate Cox regression yielded a hazard ratio of our signature of 5.33 (95% CI = 1.76 to 16.1) and 1.81 (95% CI = 1.19 to 2.76) respectively. The eight-gene signature features the cancer hallmark epidermal growth factor receptor (EGFR) and genes involved in cell adhesion, migration, invasion, tumor growth and progression. Conclusions: Our study sheds light on the intricate three-way interplay among gene expression, invasion and compound-sensitivity. We report the finding of a unique signature that predicts chemotherapy survival for both lung and breast cancer. Augmenting the NCI-60 model with in vitro characterization of important phenotype-like invasion potential is a cost-effective approach to power the genomic chemosensitivity analysis.

Motivation: Protein–protein interaction (PPI) plays an important role in understanding gene functions, and many computational PPI prediction methods have been proposed in recent years. Despite the extensive efforts, PPI prediction still has much room to improve. Sequence-based co-evolution methods include the substitution rate method and the mirror tree method, which compare sequence substitution rates and topological similarity of phylogenetic trees, respectively. Although they have been used to predict PPI in species with small genomes like Escherichia coli, such methods have not been tested in large scale proteome like Homo sapiens. Result: In this study, we propose a novel sequence-based co-evolution method, co-evolutionary divergence (CD), for human PPI prediction. Built on the basic assumption that protein pairs with similar substitution rates are likely to interact with each other, the CD method converts the evolutionary information from 14 species of vertebrates into likelihood ratios and combined them together to infer PPI. We showed that the CD method outperformed the mirror tree method in three independent human PPI datasets by a large margin. With the arrival of more species genome information generated by next generation sequencing, the performance of the CD method can be further improved. Availability: Source code and support are available at http://mib.stat.sinica.edu.tw/LAP/tmp/CD.rar. Contact: syuan@stat.sinica.edu.tw Supplementary information: Supplementary data are available at Bioinformatics online.