One-step targeted maximum likelihood estimation for time-to-event outcomes

Researchers in observational survival analysis are interested in not only estimating survival curve nonparametrically but also having statistical inference for the parameter. We consider right-censored failure time data where we observe n independent and identically distributed observations of a vector random variable consisting of baseline covariates, a binary treatment at baseline, a survival time subject to right censoring, and the censoring indicator. We assume the baseline covariates are allowed to affect the treatment and censoring so that an estimator that ignores covariate information would be inconsistent. The goal is to use these data to estimate the counterfactual average survival curve of the population if all subjects are assigned the same treatment at baseline. Existing observational survival analysis methods do not result in monotone survival curve estimators, which is undesirable and may lose efficiency by not constraining the shape of the estimator using the prior knowledge of the estimand. In this paper, we present a one-step Targeted Maximum Likelihood Estimator (TMLE) for estimating the counterfactual average survival curve. We show that this new TMLE can be executed via recursion in small local updates. We demonstrate the finite sample performance of this one-step TMLE in simulations and an application to a monoclonal gammopathy data.     

Partial least squares for functional joint models with applications to the Alzheimer's disease neuroimaging initiative study

Many biomedical studies have identified important imaging biomarkers that are associated with both repeated clinical measures and a survival outcome. The functional joint model (FJM) framework, proposed by Li and Luo in 2017, investigates the association between repeated clinical measures and survival data, while adjusting for both high-dimensional images and low-dimensional covariates based on the functional principal component analysis (FPCA). In this paper, we propose a novel algorithm for the estimation of FJM based on the functional partial least squares (FPLS). Our numerical studies demonstrate that, compared to FPCA, the proposed FPLS algorithm can yield more accurate and robust estimation and prediction performance in many important scenarios. We apply the proposed FPLS algorithm to a neuroimaging study. Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.     

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.     

Disturbance does not have a significant impact on waders in an estuary close to conurbations: importance of overlap between birds and people in time and space

Disturbance of wildlife is a potential cause of conservation concern, not least to overwintering waders Charadrii inhabiting estuaries close to conurbations where human recreational and economic activities are often concentrated. Disturbance from people on and alongside intertidal foraging areas could make it more difficult for birds to survive until spring in good condition by reducing the time available for foraging, increasing energy requirements and displacing birds to poorer foraging areas. We adopted a two-part approach to testing whether such significant impacts occurred in a Special Protection Area where disturbance risk was high because of its small size and close proximity to conurbations. In part one, we recorded over the whole estuary during stages of the tidal cycle when part or all of the intertidal zone was exposed and so accessible to waders (i.e. on receding, low and advancing tides): (1) the numbers and activities of people on the intertidal flats and on the adjacent land in those places where people were visible to waders in the intertidal zone and (2) the numbers of waders present and disturbed into flight, the flight distance and flight duration in the ‘overlap’ areas where people did disturb waders. People occurred on < 25% of the 938 ha of intertidal flats, but most waders foraged on mudflats, whereas most people were on sandflats. People on land were visible to foraging waders along < 35% of the 16.5 km of shoreline. Waders and people were therefore substantially separated in space. Within overlap areas, people and waders were often frequently separated in time: for example, people on land mostly disturbed waders when only the upper shore levels were exposed. The average overwintering wader spent < 0.1% of its foraging time during daylight flying away from people and the additional energy expenditure was equivalent to < 0.02% of its daily requirements. The comparison made in part two between our study area and two comparable estuaries showed that the number of visits each day to the overlap areas would need to be 29 or 43 times greater for disturbance to have lowered the birds’ body condition and winter survival. Both parts of the study therefore suggested strongly that the amount of disturbance was too trivial to have a significant impact on waders. It is concluded that: (1) to properly assess disturbance risk to waders, both extensive and intensive observations must be made on the behaviour of people and birds to quantify the extent to which they overlap in space and time, and (2) it should not be assumed that an estuary's close proximity to conurbations, and the presence of large numbers of people in the vicinity of the SPA, necessarily implies a significant disturbance risk to waders.     

Expression scoring of a small-nucleolar-RNA signature identified by machine learning serves as a prognostic predictor for head and neck cancer

Head and neck squamous cell carcinoma (HNSCC) is a common malignancy with high mortality and poor prognosis due to a lack of predictive markers. Increasing evidence has demonstrated small nucleolar RNAs (snoRNAs) play an important role in tumorigenesis. The aim of this study was to identify a prognostic snoRNA signature of HNSCC. Survival-related snoRNAs were screened by Cox regression analysis (univariate, least absolute shrinkage and selection operator, and multivariate). The predictive value was validated in different subgroups. The biological functions were explored by coexpression analysis and gene set enrichment analysis (GSEA). One hundred and thirteen survival-related snoRNAs were identified, and a five-snoRNA signature predicted prognosis with high sensitivity and specificity. Furthermore, the signature was applicable to patients of different sexes, ages, stages, grades, and anatomic subdivisions. Coexpression analysis and GSEA revealed the five-snoRNA are involved in regulating malignant phenotype and DNA/RNA editing. This five-snoRNA signature is not only a promising predictor of prognosis and survival but also a potential biomarker for patient stratification management.     

Survival analysis for long noncoding RNAs identifies TP53TG1 as an antioncogenic target for the breast cancer

Breast carcinoma is one of the most commonly diagnosed tumors and also one of the deadliest cancers in the female. Long noncoding RNAs (lncRNAs) are emerging as novel targets and biomarkers for breast cancer diagnosis and treatment. In this study, we aimed to study the lncRNAs associated with the outcomes in patients using the breast invasive carcinoma datasets from The Cancer Genome Atlas. The Cox proportional hazards regression model was fitted to each lncRNA. Hierarchy clustering was carried out using these survival-related lncRNAs and the log-rank test was carried out for the clustered groups. DNA methylation status was utilized to identify the lncRNAs regulated by epigenetics. Finally, the coexpressed messenger RNA with the potential lncRNAs were utilized to study the possible functions and mechanisms of lncRNAs. In total, 182 lncRNAs had an impact on the survival time of the patients with a cutoff <0.01. The patients were clustered into three groups using these survival-related genes, which performed significantly different prognosis. Two lncRNAs, which were significantly correlated with the outcomes of breast cancer and were regulated by methylation status, were obtained. These two lncRNAs were TP53TG1 and RP5-1061H20.4. We proposed that TP53TG1 was activated by the wild-type TP53 and performed an impact on the PI3Ks family by binding YBX2 in breast cancer.     

A robust six-miRNA prognostic signature for head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) remains a major health problem worldwide. We aimed to identify a robust microRNA (miRNA)-based signature for predicting HNSCC prognosis. The miRNA expression profiles of HNSCC were obtained from The Cancer Genome Atlas (TCGA) database. The TCGA HNSCC cohort was randomly divided into the discovery and validation cohort. A miRNA-based prognostic signature was built up based on TGCA discovery cohort, and then further validated. The downstream targets of prognostic miRNAs were subjected to functional enrichment analyses. The role of miR-1229-3p, a prognosis-related miRNA, in tumorigenesis of HNSCC was further evaluated. A total of 305 significantly differentially expressed miRNAs were found between HNSCC samples and normal tissues. A six-miRNA prognostic signature was constructed, which exhibited a strong association with overall survival (OS) in the TCGA discovery cohort. In addition, these findings were successfully confirmed in TCGA validation cohort and our own independent cohort. The miRNA-based signature was demonstrated as an independent prognostic indicator for HNSCC. A risk signature-based nomogram model was constructed and showed good performance for predicting the OS for HNSCC. The functional analyses revealed that the downstream targets of these prognostic miRNAs were closely linked to cancer progression. Mechanistically, in vitro analysis revealed that miR-1229-3p played a tumor promoting role in HNSCC. In conclusion, our study has developed a robust miRNA-based signature for predicting the prognosis of HNSCC with high accuracy, which will contribute to improve the therapeutic outcome.     

Honokiol: A non-adipogenic PPARγ agonist from nature

Background

Peroxisome proliferator-activated receptor gamma (PPARγ) agonists are clinically used to counteract hyperglycemia. However, so far experienced unwanted side effects, such as weight gain, promote the search for new PPARγ activators.

Methods

We used a combination of in silico, in vitro, cell-based and in vivo models to identify and validate natural products as promising leads for partial novel PPARγ agonists.

Results

The natural product honokiol from the traditional Chinese herbal drug Magnolia bark was in silico predicted to bind into the PPARγ ligand binding pocket as dimer. Honokiol indeed directly bound to purified PPARγ ligand-binding domain (LBD) and acted as partial agonist in a PPARγ-mediated luciferase reporter assay. Honokiol was then directly compared to the clinically used full agonist pioglitazone with regard to stimulation of glucose uptake in adipocytes as well as adipogenic differentiation in 3T3-L1 pre-adipocytes and mouse embryonic fibroblasts. While honokiol stimulated basal glucose uptake to a similar extent as pioglitazone, it did not induce adipogenesis in contrast to pioglitazone. In diabetic KKAy mice oral application of honokiol prevented hyperglycemia and suppressed weight gain.

Conclusion

We identified honokiol as a partial non-adipogenic PPARγ agonist in vitro which prevented hyperglycemia and weight gain in vivo.

General significance

This observed activity profile suggests honokiol as promising new pharmaceutical lead or dietary supplement to combat metabolic disease, and provides a molecular explanation for the use of Magnolia in traditional medicine.