Genome-wide search algorithms for identifying dynamic gene co-expression via Bayesian variable selection.

A wealth of gene expression data generated by high-throughput techniques provides exciting opportunities for studying gene-gene interactions systematically. Gene-gene interactions in a biological system are tightly regulated and are often highly dynamic. The interactions can change flexibly under various internal cellular signals or external stimuli. Previous studies have developed statistical methods to examine these dynamic changes in gene-gene interactions. However, due to the massive number of possible gene combinations that need to be considered in a typical genomic dataset, intensive computation is a common challenge for exploring gene-gene interactions. On the other hand, oftentimes only a small proportion of gene combinations exhibit dynamic co-expression changes. To solve this problem, we propose Bayesian variable selection approaches based on spike-and-slab priors. The proposed algorithms reduce the computational intensity by focusing on identifying subsets of promising gene combinations in the search space. We also adopt a Bayesian multiple hypothesis testing procedure to identify strong dynamic gene co-expression changes. Simulation studies are performed to compare the proposed approaches with existing exhaustive search heuristics. We demonstrate the implementation of our proposed approach to study the association between gene co-expression patterns and overall survival using the RNA-sequencing dataset from The Cancer Genome Atlas breast cancer BRCA-US project.

A flexible parametric approach for analyzing arbitrarily censored data that are potentially subject to left truncation under the proportional hazards model.

The proportional hazards (PH) model is, arguably, the most popular model for the analysis of lifetime data arising from epidemiological studies, among many others. In such applications, analysts may be faced with censored outcomes and/or studies which institute enrollment criterion leading to left truncation. Censored outcomes arise when the event of interest is not observed but rather is known relevant to an observation time(s). Left truncated data occur in studies that exclude participants who have experienced the event prior to being enrolled in the study. If not accounted for, both of these features can lead to inaccurate inferences about the population under study. Thus, to overcome this challenge, herein we propose a novel unified PH model that can be used to accommodate both of these features. In particular, our approach can seamlessly analyze exactly observed failure times along with interval-censored observations, while aptly accounting for left truncation. To facilitate model fitting, an expectation-maximization algorithm is developed through the introduction of carefully structured latent random variables. To provide modeling flexibility, a monotone spline representation is used to approximate the cumulative baseline hazard function. The performance of our methodology is evaluated through a simulation study and is further illustrated through the analysis of two motivating data sets; one that involves child mortality in Nigeria and the other prostate cancer.

Simultaneous variable selection in regression analysis of multivariate interval-censored data.

Multivariate interval-censored data arise when each subject under study can potentially experience multiple events and the onset time of each event is not observed exactly but is known to lie in a certain time interval formed by adjacent examination times with changed statuses of the event. This type of incomplete and complex data structure poses a substantial challenge in practical data analysis. In addition, many potential risk factors exist in numerous studies. Thus, conducting variable selection for event-specific covariates simultaneously becomes useful in identifying important variables and assessing their effects on the events of interest. In this paper, we develop a variable selection technique for multivariate interval-censored data under a general class of semiparametric transformation frailty models. The minimum information criterion (MIC) method is embedded in the optimization step of the proposed expectation-maximization (EM) algorithm to obtain the parameter estimator. The proposed EM algorithm greatly reduces the computational burden in maximizing the observed likelihood function, and the MIC naturally avoids selecting the optimal tuning parameter as needed in many other popular penalties, making the proposed algorithm promising and reliable. The proposed method is evaluated through extensive simulation studies and illustrated by an analysis of patient data from the Aerobics Center Longitudinal Study.

Regression analysis of arbitrarily censored survival data under the proportional odds model.

Arbitrarily censored data are referred to as the survival data that contain a mixture of exactly observed, left-censored, interval-censored, and right-censored observations. Existing research work on regression analysis on arbitrarily censored data is relatively sparse and mainly focused on the proportional hazards model and the accelerated failure time model. This article studies the proportional odds (PO) model and proposes a novel estimation approach through an expectation-maximization (EM) algorithm for analyzing such data. The proposed EM algorithm has many appealing properties such as being robust to initial values, easy to implement, converging fast, and providing the variance estimate of the regression parameter estimate in closed form. An informal diagnosis plot is developed for checking the PO model assumption. Our method has shown excellent performance in estimating the regression parameters as well as the baseline survival function in a simulation study. A real-life dataset about metastatic colorectal cancer is analyzed for illustration. An R package regPO has been created for practitioners to implement our method.

Semiparametric estimation of the cure fraction in population-based cancer survival analysis.

With rapid development in medical research, the treatment of diseases including cancer has progressed dramatically and those survivors may die from causes other than the one under study, especially among elderly patients. Motivated by the Surveillance, Epidemiology, and End Results (SEER) female breast cancer study, background mortality is incorporated into the mixture cure proportional hazards (MCPH) model to improve the cure fraction estimation in population-based cancer studies. Here, that patients are "cured" is defined as when the mortality rate of the individuals in diseased group returns to the same level as that expected in the general population, where the population level mortality is presented by the mortality table of the United States. The semiparametric estimation method based on the EM algorithm for the MCPH model with background mortality (MCPH+BM) is further developed and validated via comprehensive simulation studies. Real data analysis shows that the proposed semiparametric MCPH+BM model may provide more accurate estimation in population-level cancer study.

Variable selection in semiparametric nonmixture cure model with interval-censored failure time data: An application to the prostate cancer screening study.

Censored failure time data with a cured subgroup is frequently encountered in many scientific areas including the cancer screening research, tumorigenicity studies, and sociological surveys. Meanwhile, one may also encounter an extraordinary large number of risk factors in practice, such as patient's demographic characteristics, clinical measurements, and medical history, which makes variable selection an emerging need in the data analysis. Motivated by a medical study on prostate cancer screening, we develop a variable selection method in the semiparametric nonmixture or promotion time cure model when interval-censored data with a cured subgroup are present. Specifically, we propose a penalized likelihood approach with the use of the least absolute shrinkage and selection operator, adaptive least absolute shrinkage and selection operator, or smoothly clipped absolute deviation penalties, which can be easily accomplished via a novel penalized expectation-maximization algorithm. We assess the finite-sample performance of the proposed methodology through extensive simulations and analyze the prostate cancer screening data for illustration.

Normal frailty probit model for clustered interval-censored failure time data.

Clustered interval-censored data commonly arise in many studies of biomedical research where the failure time of interest is subject to interval-censoring and subjects are correlated for being in the same cluster. A new semiparametric frailty probit regression model is proposed to study covariate effects on the failure time by accounting for the intracluster dependence. Under the proposed normal frailty probit model, the marginal distribution of the failure time is a semiparametric probit model, the regression parameters can be interpreted as both the conditional covariate effects given frailty and the marginal covariate effects up to a multiplicative constant, and the intracluster association can be summarized by two nonparametric measures in simple and explicit form. A fully Bayesian estimation approach is developed based on the use of monotone splines for the unknown nondecreasing function and a data augmentation using normal latent variables. The proposed Gibbs sampler is straightforward to implement since all unknowns have standard form in their full conditional distributions. The proposed method performs very well in estimating the regression parameters as well as the intracluster association, and the method is robust to frailty distribution misspecifications as shown in our simulation studies. Two real-life data sets are analyzed for illustration.

A Gamma-frailty proportional hazards model for bivariate interval-censored data.

Correlated survival data naturally arise from many clinical and epidemiological studies. For the analysis of such data, the Gamma-frailty proportional hazards (PH) model is a popular choice because the regression parameters have marginal interpretations and the statistical association between the failure times can be explicitly quantified via Kendall's tau. Despite their popularity, Gamma-frailty PH models for correlated interval-censored data have not received as much attention as analogous models for right-censored data. In this work, a Gamma-frailty PH model for bivariate interval-censored data is presented and an easy to implement expectation-maximization (EM) algorithm for model fitting is developed. The proposed model adopts a monotone spline representation for the purposes of approximating the unknown conditional cumulative baseline hazard functions, significantly reducing the number of unknown parameters while retaining modeling flexibility. The EM algorithm was derived from a data augmentation procedure involving latent Poisson random variables. Extensive numerical studies illustrate that the proposed method can provide reliable estimation and valid inference, and is moreover robust to the misspecification of the frailty distribution. To further illustrate its use, the proposed method is used to analyze data from an epidemiological study of sexually transmitted infections.

A flexible, computationally efficient method for fitting the proportional hazards model to interval-censored data.

The proportional hazards model (PH) is currently the most popular regression model for analyzing time-to-event data. Despite its popularity, the analysis of interval-censored data under the PH model can be challenging using many available techniques. This article presents a new method for analyzing interval-censored data under the PH model. The proposed approach uses a monotone spline representation to approximate the unknown nondecreasing cumulative baseline hazard function. Formulating the PH model in this fashion results in a finite number of parameters to estimate while maintaining substantial modeling flexibility. A novel expectation-maximization (EM) algorithm is developed for finding the maximum likelihood estimates of the parameters. The derivation of the EM algorithm relies on a two-stage data augmentation involving latent Poisson random variables. The resulting algorithm is easy to implement, robust to initialization, enjoys quick convergence, and provides closed-form variance estimates. The performance of the proposed regression methodology is evaluated through a simulation study, and is further illustrated using data from a large population-based randomized trial designed and sponsored by the United States National Cancer Institute.

A Bayesian proportional hazards model for general interval-censored data.

The proportional hazards (PH) model is the most widely used semiparametric regression model for analyzing right-censored survival data based on the partial likelihood method. However, the partial likelihood does not exist for interval-censored data due to the complexity of the data structure. In this paper, we focus on general interval-censored data, which is a mixture of left-, right-, and interval-censored observations. We propose an efficient and easy-to-implement Bayesian estimation approach for analyzing such data under the PH model. The proposed approach adopts monotone splines to model the baseline cumulative hazard function and allows to estimate the regression parameters and the baseline survival function simultaneously. A novel two-stage data augmentation with Poisson latent variables is developed for the efficient computation. The developed Gibbs sampler is easy to execute as it does not require imputing any unobserved failure times or contain any complicated Metropolis-Hastings steps. Our approach is evaluated through extensive simulation studies and illustrated with two real-life data sets.

Bayesian variable selection in joint modeling of longitudinal data and interval-censored failure time data.

Joint modeling of longitudinal data and survival data has gained great attention in the last two decades. However, most of the existing studies have focused on right-censored survival data. In this article, we study joint analysis of longitudinal data and interval-censored survival data and conduct Bayesian variable selection in this framework. A new joint model is proposed with a shared frailty to characterize the dependence between the two types of responses, where the longitudinal response is modeled with a semiparametric linear mixed-effects submodel and the survival time is modeled by a semiparametric normal fraility probit sub-model. Several Bayesian variable selection approaches are developed by adopting Bayesian Lasso, adaptive Lasso, and spike-and-slab priors in order to simultaneously select significant covariates and estimate their effects on the two types of responses. Efficient Gibbs samplers are proposed with all unknown parameters and latent variables being sampled directly from well recognized full conditional distributions. Our simulation study shows that these methods perform well in both variable selection and parameter estimation. A real-life data application to joint analysis of blood cholesterol level and hypertension is provided as an illustration.

Regression analysis for current status data using the EM algorithm.

We propose new expectation-maximization algorithms to analyze current status data under two popular semiparametric regression models: the proportional hazards (PH) model and the proportional odds (PO) model. Monotone splines are used to model the baseline cumulative hazard function in the PH model and the baseline odds function in the PO model. The proposed algorithms are derived by exploiting a data augmentation based on Poisson latent variables. Unlike previous regression work with current status data, our PH and PO model fitting methods are fast, flexible, easy to implement, and provide variance estimates in closed form. These techniques are evaluated using simulation and are illustrated using uterine fibroid data from a prospective cohort study on early pregnancy.

Pan-cancer analysis of the oncogenic effects of G-protein-coupled receptor kinase-interacting protein-1 and validation on liver hepatocellular carcinoma.

BACKGROUND: Despite G-protein-coupled receptor kinase-interacting protein-1 (GIT1) being recognized as a new promoter gene in some types of cancer, its effect on human pan-cancers and liver hepatocellular carcinoma (LIHC) remains unclear. OBJECTIVES: To elucidate the molecular mechanisms of GIT1 in pan-cancer and LIHC. MATERIAL AND METHODS: Various bioinformatics approaches were utilized to elucidate the oncogenic effects of GIT1 on human pan-cancers. RESULTS: The GIT1 was aberrantly expressed in pan-cancers and associated with the clinical stage. Moreover, the upregulation of GIT1 expression was indicative of poor overall survival (OS) in patients with LIHC, skin cutaneous melanoma (SKCM) and uterine corpus endometrial carcinoma (UCEC), as well as of poor disease-free survival (DFS) in patients with LIHC and UCEC. Furthermore, GIT1 levels were correlated with cancer-associated fibroblasts (CAFs) in adrenocortical carcinoma (ACC), cervical squamous cell carcinoma (CESC) and LIHC. The analysis of single-cell sequencing data revealed an association of GIT1 levels with apoptosis, cell cycle and DNA damage. In addition, multivariate Cox analysis indicated that high GIT1 levels were an independent risk factor for shorter OS in patients with LIHC. Finally, the gene set enrichment analysis revealed INFLAMMATORY_RESPONSE pathway and IL2_STAT5_SIGNALING to be the most enriched in LIHC. CONCLUSIONS: Our data demonstrate the oncogenic effects of GIT1 on various cancers. We believe that GIT1 can serve as a biomarker for LIHC.

Hippo Signaling Mediates TGFß-Dependent Transcriptional Inputs in Cardiac Cushion Mesenchymal Cells to Regulate Extracellular Matrix Remodeling.

The transforming growth factor beta (TGFß) and Hippo signaling pathways are evolutionarily conserved pathways that play a critical role in cardiac fibroblasts during embryonic development, tissue repair, and fibrosis. TGFß signaling and Hippo signaling are also important for cardiac cushion remodeling and septation during embryonic development. Loss of TGFß2 in mice causes cardiac cushion remodeling defects resulting in congenital heart disease. In this study, we used in vitro molecular and pharmacologic approaches in the cushion mesenchymal cell line (tsA58-AVM) and investigated if the Hippo pathway acts as a mediator of TGFß2 signaling. Immunofluorescence staining showed that TGFß2 induced nuclear translocation of activated SMAD3 in the cushion mesenchymal cells. In addition, the results indicate increased nuclear localization of Yes-associated protein 1 (YAP1) following a similar treatment of TGFß2. In collagen lattice formation assays, the TGFß2 treatment of cushion cells resulted in an enhanced collagen contraction compared to the untreated cushion cells. Interestingly, verteporfin, a YAP1 inhibitor, significantly blocked the ability of cushion cells to contract collagen gel in the absence or presence of exogenously added TGFß2. To confirm the molecular mechanisms of the verteporfin-induced inhibition of TGFß2-dependent extracellular matrix (ECM) reorganization, we performed a gene expression analysis of key mesenchymal genes involved in ECM remodeling in heart development and disease. Our results confirm that verteporfin significantly decreased the expression of α-smooth muscle actin (Acta2), collagen 1a1 (Col1a1), Ccn1 (i.e., Cyr61), and Ccn2 (i.e., Ctgf). Western blot analysis indicated that verteporfin treatment significantly blocked the TGFß2-induced activation of SMAD2/3 in cushion mesenchymal cells. Collectively, these results indicate that TGFß2 regulation of cushion mesenchymal cell behavior and ECM remodeling is mediated by YAP1. Thus, the TGFß2 and Hippo pathway integration represents an important step in understanding the etiology of congenital heart disease.

Annexin 1-derived peptide Ac2-26 inhibits eosinophil recruitment in vivo via decreasing prostaglandin D2.

BACKGROUND: Asthma is a chronic inflammatory disease of the mucosa and is associated with excess TH2 cytokines, eotaxin, prostaglandin D2 (PGD2) and eosinophilia in the lungs. Previous studies have emphasized that the N-terminal peptide of annexin 1 (peptide Ac2-26) can inhibit mast cell degranulation, antigen-induced eotaxin release as well as the accumulation of both neutrophils and eosinophils in a model of rat pleurisy. The purpose of this study was to demonstrate anti-asthmatic effects of Ac2-26 in an asthma model and to explore possible mechanisms involved. METHODS: The effect of Ac2-26 on TH2 cytokine release, eotaxin production, PGD2 levels and the development of pulmonary eosinophilic inflammation was compared with glucocorticoids in an asthmatic rat model. The study was conducted on rats sensitized and challenged with ovalbumin and plethysmography measured airway responsiveness. Bronchoalveolar lavage (BAL) histopathology and the levels of cytokines, chemokines as well as PGD2 were examined. RESULTS: Our results showed that Ac2-26 suppressed the accumulation of eosinophils in airways, reduced IL-4, IL-5, IL-13, PGD2 and eotaxin levels in the BAL fluid, and lowered the expression of CRTH2. Exogenous PGD2 significantly attenuated the biological effects of Ac2-26. CONCLUSION: These results indicated that Ac2-26 exerted broad inhibitory effects on airway inflammation and hyperresponsiveness in a rat model of asthma. Exogenous PGD2 reversed the inhibitory effects of AC2-26 on eosinophil recruitment. Ac2-26 exhibited anti-asthmatic, immunomodulatory activity that was substantially mediated by decreasing PGD2 production and its CRTH2 receptor expression in vivo.

Semiparametric bayes' proportional odds models for current status data with underreporting.

Current status data are a type of interval-censored event time data in which all the individuals are either left or right censored. For example, our motivation is drawn from a cross-sectional study, which measured whether or not fibroid onset had occurred by the age of an ultrasound exam for each woman. We propose a semiparametric Bayesian proportional odds model in which the baseline event time distribution is estimated nonparametrically by using adaptive monotone splines in a logistic regression model and the potential risk factors are included in the parametric part of the mean structure. The proposed approach has the advantage of being straightforward to implement using a simple and efficient Gibbs sampler, whereas alternative semiparametric Bayes' event time models encounter problems for current status data. The model is generalized to allow systematic underreporting in a subset of the data, and the methods are applied to an epidemiologic study of uterine fibroids.

[Exploration of the broken parts of a brake hose by ATR-FTIR and SEM].

ATR-FTIR and SEM were respectively utilized to analyze the chemical components and to observe the micromorphology of a broken brake hose from a traffic case, which could be a supplementary for the traditional microscopic examination. The instrumental analysis results indicated that the rubber from the brake hose had already aged; the rubber from external side had experienced brittle fracture and there were original hollows in the rubber from internal side. The breaking of the brake hose resulted from all these reasons. The experimental results also demonstrated that the fact could be reflected efficiently, accurately and objectively by the application of ATR-FTIR and SEM to the physical evidence from a case. Therefore, it could be an effective complement for traditional traffic trace examination.

A semiparametric mixture model approach for regression analysis of partly interval-censored data with a cured subgroup.

Failure time data with a cured subgroup are frequently confronted in various scientific fields and many methods have been proposed for their analysis under right or interval censoring. However, a cure model approach does not seem to exist in the analysis of partly interval-censored data, which consist of both exactly observed and interval-censored observations on the failure time of interest. In this article, we propose a two-component mixture cure model approach for analyzing such type of data. We employ a logistic model to describe the cured probability and a proportional hazards model to model the latent failure time distribution for uncured subjects. We consider maximum likelihood estimation and develop a new expectation-maximization algorithm for its implementation. The asymptotic properties of the resulting estimators are established and the finite sample performance of the proposed method is examined through simulation studies. An application to a set of real data on childhood mortality in Nigeria is provided.

Semiparametric bayes multiple testing: applications to tumor data.

In National Toxicology Program (NTP) studies, investigators want to assess whether a test agent is carcinogenic overall and specific to certain tumor types, while estimating the dose-response profiles. Because there are potentially correlations among the tumors, a joint inference is preferred to separate univariate analyses for each tumor type. In this regard, we propose a random effect logistic model with a matrix of coefficients representing log-odds ratios for the adjacent dose groups for tumors at different sites. We propose appropriate nonparametric priors for these coefficients to characterize the correlations and to allow borrowing of information across different dose groups and tumor types. Global and local hypotheses can be easily evaluated by summarizing the output of a single Monte Carlo Markov chain (MCMC). Two multiple testing procedures are applied for testing local hypotheses based on the posterior probabilities of local alternatives. Simulation studies are conducted and an NTP tumor data set is analyzed illustrating the proposed approach.

A semiparametric probit model for case 2 interval-censored failure time data.

Interval-censored data occur naturally in many fields and the main feature is that the failure time of interest is not observed exactly, but is known to fall within some interval. In this paper, we propose a semiparametric probit model for analyzing case 2 interval-censored data as an alternative to the existing semiparametric models in the literature. Specifically, we propose to approximate the unknown nonparametric nondecreasing function in the probit model with a linear combination of monotone splines, leading to only a finite number of parameters to estimate. Both the maximum likelihood and the Bayesian estimation methods are proposed. For each method, regression parameters and the baseline survival function are estimated jointly. The proposed methods make no assumptions about the observation process and can be applicable to any interval-censored data with easy implementation. The methods are evaluated by simulation studies and are illustrated by two real-life interval-censored data applications.