Five genes as a novel signature for predicting the prognosis of patients with laryngeal cancer

In this study, we purpose to investigate a novel five-gene signature for predicting the prognosis of patients with laryngeal cancer. The laryngeal cancer datasets were obtained from The Cancer Genome Atlas (TCGA). Both univariate and multivariate Cox regression analysis was applied to screening for prognostic differential expressed genes (DEGs), and a novel gene signature was obtained. The performance of this Cox regression model was tested by receiver operating characteristic (ROC) curves and area under the curve (AUC). Further survival analysis for each of the five genes was carried out through the Kaplan-Meier curve and Log-rank test. Totally, 622 DEGs were screened from the TCGA datasets in this study. We construct a five-gene signature through Cox survival analysis. Patients were divided into low- and high-risk groups depending on the median risk score, and a significant difference of the 5-year overall survival was found between these two groups (P < .05). ROC curves verified that this five-gene signature had good performance to predict the prognosis of laryngeal cancer (AUC = 0.862, P < .05). In conclusion, the five-gene signature consist of EMP1, HOXB9, DPY19L2P1, MMP1, and KLHDC7B might be applied as an independent prognosis predictor of laryngeal cancer.     

Equivalence of regression curves sharing common parameters

In clinical trials, the comparison of two different populations is a common problem. Nonlinear (parametric) regression models are commonly used to describe the relationship between covariates, such as concentration or dose, and a response variable in the two groups. In some situations, it is reasonable to assume some model parameters to be the same, for instance, the placebo effect or the maximum treatment effect. In this paper, we develop a (parametric) bootstrap test to establish the similarity of two regression curves sharing some common parameters. We show by theoretical arguments and by means of a simulation study that the new test controls its significance level and achieves a reasonable power. Moreover, it is demonstrated that under the assumption of common parameters, a considerably more powerful test can be constructed compared with the test that does not use this assumption. Finally, we illustrate the potential applications of the new methodology by a clinical trial example.     

Bayesian data integration and variable selection for pan-cancer survival prediction using protein expression data

Accurate prognostic prediction using molecular information is a challenging area of research, which is essential to develop precision medicine. In this paper, we develop translational models to identify major actionable proteins that are associated with clinical outcomes, like the survival time of patients. There are considerable statistical and computational challenges due to the large dimension of the problems. Furthermore, data are available for different tumor types; hence data integration for various tumors is desirable. Having censored survival outcomes escalates one more level of complexity in the inferential procedure. We develop Bayesian hierarchical survival models, which accommodate all the challenges mentioned here. We use the hierarchical Bayesian accelerated failure time model for survival regression. Furthermore, we assume sparse horseshoe prior distribution for the regression coefficients to identify the major proteomic drivers. We borrow strength across tumor groups by introducing a correlation structure among the prior distributions. The proposed methods have been used to analyze data from the recently curated “The Cancer Proteome Atlas” (TCPA), which contains reverse-phase protein arrays–based high-quality protein expression data as well as detailed clinical annotation, including survival times. Our simulation and the TCPA data analysis illustrate the efficacy of the proposed integrative model, which links different tumors with the correlated prior structures.     

In vitro nemato-toxic potential of some leaf extracts on Juvenile mortality of Meloidogyne incognita race-3

Abstract

Aqueous leaf extracts of four commonly growing weeds namely Ageratum conyzoides, Elephantopus scaber, Lantana camara and Xanthium strumarium were used to evaluate their nematicidal activity on second stage juvenile of Meloidogyne incognita race-3. The juveniles were exposed to various concentration of leaf extract namely 250, 500, 1000 and 2000?ppm for 12, 24 and 48?h, respectively. All leaf extracts showed the nematicidal property in concentration and time-dependent manner. The maximum juvenile mortality was recorded in E. scaber throughout the incubation period followed by X. strumarium, L. camara and A. conyzoides. The regression and correlation of regression revealed the best concentration-dependent effect of aqueous leaf extracts on nematode mortality in E. scaber (R²?=?.751) followed by X. strumarium (R²?=?.749), A. conyzoides (R²?=?.687) and L. camara (R²?=?.756). Aqueous leaves extracts of these aforementioned weeds showed nematicidal properties, therefore, may be used as a key component of integrated disease management programme.     

Tagging SNPs in the ERCC4 gene are associated with gastric cancer risk

ERCC4 plays an essential role in the nucleotide excision repair (NER) pathway, which is involved in the removal of a wide variety of DNA lesions. To determine whether the ERCC4 tagging SNPs (tSNPs) are associated with risk of gastric cancer, we conducted a hospital-based case-control study of 350 cases and 468 cancer-free controls. In the logistic regression (LR) analysis, we found a significantly decreased risk of gastric cancer associated with the rs744154 GC/CC genotypes [adjusted odds ratio (OR) = 0.56, 95% confidence interval (CI) = 0.42–0.75, false discovery rate (FDR) P = 0.003] compared with the wild-type GG genotype. Haplotype-based association study revealed that the CGC haplotype that containing the rs744154 C allele can decrease the risk of gastric cancer compared with the most common haplotype GGT (adjusted OR = 0.61, 95% CI = 0.46–0.81). Using the multifactor dimensionality reduction (MDR) analysis, we identified that the SNP rs744154 and smoking status were the best two predictive factors for gastric cancer with a testing accuracy of 55.76% and a perfect cross-validation consistency (CVC) of 10 (P = 0.001). Furthermore, the smokers with the rs744154 GC/CC genotypes showed a decreased risk of gastric cancer (adjusted OR = 0.55, 95% CI = 0.35–0.85) compared with the smokers with the GG genotype using multivariate LR analysis. The above findings consistently suggested that genetic variants in the ERCC4 gene may play a protective role in the etiology of gastric cancer, even in the smokers.     

International Society of Chronobiology: Membership Information

Most variables of interest in laboratory medicine show predictable changes with several frequencies in the span of time investigated. The waveform of such nonsinusoidal rhythms can be well described by the use of multiple components rhythmometry, a method that allows fitting a linear model with several cosine functions. The method, originally described for analysis of longitudinal time series, is here extended to allow analysis of hybrid data (time series sampled from a group of subjects, each represented by an individual series). Given k individual series, we can fit the same linear model with m different frequencies (harmonics or not from one fundamental period) to each series. This fit will provide estimations for 2m + 1 parameters, namely, the amplitude and acrophase of each component, as well as the rhythm-adjusted mean. Assuming that the set of parameters obtained for each individual is a random sample from a multivariate normal population, the corresponding population parameter estimates can be based on the means of estimates obtained from individuals in the sample. Their confidence intervals depend on the variability among individual parameter estimates. The vari-ance-covariance matrix can then be estimated on the basis of the sample covariances. Confidence intervals for the rhythm-adjusted mean, as well as for the amplitude-acrophase pair, of each component can then be computed using the estimated covariance matrix. The p-values for testing the zero-amplitude assumption for each component, as well as for the global model, can finally be derived using those confidence intervals and the t and F distributions. The method, validated by a simulation study and illustrated by an example of modeling the circadian variation of heart rate, represents a new step in the development of statistical procedures in chronobiology.     

Dynamical behaviour of a two-predator model with prey refuge

A three-component model consisting on one-prey and two-predator populations is considered with a Holling type II response function incorporating a constant proportion of prey refuge. We also consider the competition among predators for their food (prey) and shelter. The essential mathematical features of the model have been analyzed thoroughly in terms of stability and bifurcations arising in some selected situations. Threshold values for some parameters indicating the feasibility and stability conditions of some equilibria are determined. The range of significant parameters under which the system admits different types of bifurcations is investigated. Numerical illustrations are performed in order to validate the applicability of the model under consideration.