Approximation of bias and mean-squared error in two-sample Mendelian randomization analyses

Mendelian randomization (MR) is a type of instrumental variable (IV) analysis that uses genetic variants as IVs for a risk factor to study its causal effect on an outcome. Extensive investigations on the performance of IV analysis procedures, such as the one based on the two-stage least squares (2SLS) procedure, have been conducted under the one-sample scenario, where measures on IVs, the risk factor, and the outcome are assumed to be available for each study participant. Recent MR analysis usually is performed with data from two independent or partially overlapping genetic association studies (two-sample setting), with one providing information on the association between the IVs and the outcome, and the other on the association between the IVs and the risk factor. We investigate the performance of 2SLS in the two-sample–based MR when the IVs are weakly associated with the risk factor. We derive closed form formulas for the bias and mean squared error of the 2SLS estimate and verify them with numeric simulations under realistic circumstances. Using these analytic formulas, we can study the pros and cons of conducting MR analysis under one-sample and two-sample settings and assess the impact of having overlapping samples. We also propose and validate a bias-corrected estimator for the causal effect.     

ROC-guided survival trees and ensembles

Tree-based methods are popular nonparametric tools in studying time-to-event outcomes. In this article, we introduce a novel framework for survival trees and ensembles, where the trees partition the dynamic survivor population and can handle time-dependent covariates. Using the idea of randomized tests, we develop generalized time-dependent receiver operating characteristic (ROC) curves for evaluating the performance of survival trees. The tree-building algorithm is guided by decision-theoretic criteria based on ROC, targeting specifically for prediction accuracy. To address the instability issue of a single tree, we propose a novel ensemble procedure based on averaging martingale estimating equations, which is different from existing methods that average the predicted survival or cumulative hazard functions from individual trees. Extensive simulation studies are conducted to examine the performance of the proposed methods. We apply the methods to a study on AIDS for illustration.     

Variable selection in joint frailty models of recurrent and terminal events

Recurrent event data are commonly encountered in biomedical studies. In many situations, they are subject to an informative terminal event, for example, death. Joint modeling of recurrent and terminal events has attracted substantial recent research interests. On the other hand, there may exist a large number of covariates in such data. How to conduct variable selection for joint frailty proportional hazards models has become a challenge in practical data analysis. We tackle this issue on the basis of the “minimum approximated information criterion” method. The proposed method can be conveniently implemented in SAS Proc NLMIXED for commonly used frailty distributions. Its finite-sample behavior is evaluated through simulation studies. We apply the proposed method to model recurrent opportunistic diseases in the presence of death in an AIDS study.     

High-efficiency protein delivery into transfection-recalcitrant cell types

Intracellular delivery of functional proteins is of great interest for basic biological research as well as for clinical applications. Transfection is the most commonly used method, however, it is not applicable to large-scale manipulation and inefficient in important cell types implicated in biomedical applications, such as epithelial, immune and pluripotent stem cells. In this study, we explored a bacterial type III secretion system (Bac-T3SS)-mediated proteofection method to overcome these limitations. An attenuated Pseudomonas aeruginosa vector was constructed, which has features of low toxicity, high T3SS activity, and self-limiting growth. Compared to the method of transfection, the Bac-T3SS showed significantly higher efficiencies of Cre recombinase translocation and target site recombination for hard-to-transfect human cell lines. Furthermore, through the delivery of β-lactamase in live animals, we demonstrated the feasibility and biosafety of in vivo application of the Bac-T3SS. This study provided an efficient and low-cost proteofection strategy for laboratory use as well as for application in large-scale cell manipulations.     

Identification of novel LncRNA targeting Smad2/PKCα signal pathway to negatively regulate malignant progression of glioblastoma

Glioblastoma multiforme (GBM) is a highly proliferative cancer with generally poor prognosis and accumulating evidence has highlighted the potential of long noncoding RNAs (lncRNAs) in the biological behaviors of glioma cells. This study focused on the identification of lncRNAs to identify targets for possible GBM prognosis. Microarray expression profiling found that 1,759 lncRNAs and 3,026 messenger RNAs (mRNAs) were upregulated, and 1932s lncRNA and 2,979 mRNAs were downregulated in GBM. Bioinformatics analysis and experimental verification identified TCONS_00020456 (TCON) for further analysis. In situ hybridization, along with immunohistochemical and receiver operating characteristic analysis determined TCON (truncation value = 3.5) as highly sensitive and specific in GBM. Grade IV patients with glioma life span with different lncRNA staining scores were analyzed. TCON staining scores below 3.5 indicated poor prognosis (life span ranging from 0.25 to 7 months), even if the glioma was surgically removed. TCON decreased significantly in GBM, and showed a coexpressional relationship with Smad2 and protein kinase C α (PKCα). Overexpression of TCON reduced the proliferation on one hand and migration, invasion on the other. TCON also inhibited epithelial–mesenchymal transformation and glioma progression in vivo, based on a nude mouse tumorigenicity assay. In addition, we predicted a potential binding site and intersection that microRNAs targeting Smad2, PKCα, and TCON through RACE pretest and bioinformatics analysis. Taken together, TCON, regarded as oncosuppressor, targeting the Smad2/PKCα axis plays a novel role in inhibiting the malignant progression of glioma. Moreover, it also demonstrates that the level of TCON can be used as a prognostic and diagnostic biomarker for GBM.     

Species-specific abundant retrotransposons elucidate the genomic composition of modern sugarcane cultivars

Chromosoma - Modern sugarcane cultivars are highly polyploid and derived from the hybridization of Saccharum officinarum and S. spontaneum, thus leading to singularly complex genomes. The complex...     

Identification of methylation-driven genes related to prognosis in clear-cell renal cell carcinoma

With the participation of the existing treatment methods, the prognosis of advanced clear-cell renal cell carcinoma (ccRCC) is poor. More evidence indicates the presence of methylation in ccRCC cancer cells, but there is a lack of studies on methylation-driven genes in ccRCC. We analyzed the open data of ccRCC in The Cancer Genome Atlas database to obtain ccRCC-related methylation-driven genes, and then carried out pathway enrichment, survival, and joint survival analyses. More important, we deeply explored the correlation between differential methylation sites and the expression of these driving genes. Finally, we screened 29 methylation-driven genes via MethylMix, of which six were significantly associated with the survival of ccRCC patients. This study demonstrated that the effect of hypermethylation or hypomethylation on prognosis is different, and the level of methylation of key methylation sites is associated with gene expression. We identified methylation-driven genes independently predicting prognosis in ccRCC, which offers theoretical support in bioinformatics for the study of methylation in ccRCC and a new perspective for the epigenetic study of ccRCC.