The discovery of a novel eight-mRNA-lncRNA signature predicting survival of hepatocellular carcinoma patients

Increasing evidence indicates that the expressions of messenger RNAs (mRNAs) and long non-coding RNAs (lncRNAs) undergo a frequent and aberrant change in carcinogenesis and cancer development. But some research was carried out on mRNA-lncRNA signatures for prediction of hepatocellular carcinoma (HCC) prognosis. We aimed to establish an mRNA-lncRNA signature to improve the ability to predict HCC patients’ survival. The subjects from the cancer genome atlas (TCGA) data set were randomly divided into two parts: training data set (n = 246) and testing data set (n = 124). Using computational methods, we selected eight gene signatures (five mRNAs and three lncRNAs) to generate the risk score model, which were significantly correlated with overall survival of patients with HCC in both training and testing data set. The signature had the ability to classify the patients in training data set into a high-risk group and low-risk group with significantly different overall survival (hazard ratio = 4.157, 95% confidence interval = 2.648-6.526, P < 0.001). The prognostic value was further validated in testing data set and the entire data set. Further analysis revealed that this signature was independent of tumor stage. In addition, Gene Set Enrichment Analysis suggested that high risk score group was associated with cell proliferation and division related pathways. Finally, we developed a well-performed nomogram integrating the prognostic signature and other clinical information to predict 3- and 5-year overall survival. In conclusion, the prognostic mRNAs and lncRNAs identified in our study indicate their potential role in HCC biogenesis. The risk score model based on the mRNA-lncRNA may be an efficient classification tool to evaluate the prognosis of patients’ with HCC.     

Prediction of platinum-resistance patients of gastric cancer using bioinformatics

Lack of guidelines for personalized chemotherapy treatment after surgery has caused gastric cancer (GC) patients' unnecessary exposure to toxicity and the financial burden of chemotherapy treatments. In our study, we aimed to identify potential biomarkers to predict GC patients' susceptibility to platinum-based on Gene Expression Omnibus (GEO) data sets. A total of 603 differentially expressed genes (DEGs) were identified between platinum-resistant cell lines and platinum-sensitive cell lines based on the Cancer Cell Line Encyclopedia (CCLE) data sets. A total of 253 patients who had accepted radical gastrectomy were recruited, of which 97 received platinum-based chemotherapy and 156 were untreated. Three biomarkers (BRMS1, ND6, SRXN1) were then selected by univariate and multivariate Cox regression analysis to establish the predictive models using nomogram. Then this model was further validated through the GEO data set (GSE62254) which showed that this model could precisely predict the disease-free survival and overall survival of patients treated with platinum-based chemotherapy after surgery compared with untreated GC patients (P < 0.0001). This predictive model might provide helpful messages about the patients' susceptibility to platinum to guide personalized chemotherapy.     

Prediction analysis for microbiome sequencing data

One goal of human microbiome studies is to relate host traits with human microbiome compositions. The analysis of microbial community sequencing data presents great statistical challenges, especially when the samples have different library sizes and the data are overdispersed with many zeros. To address these challenges, we introduce a new statistical framework, called predictive analysis in metagenomics via inverse regression (PAMIR), to analyze microbiome sequencing data. Within this framework, an inverse regression model is developed for overdispersed microbiota counts given the trait, and then a prediction rule is constructed by taking advantage of the dimension‐reduction structure in the model. An efficient Monte Carlo expectation‐maximization algorithm is proposed for maximum likelihood estimation. The method is further generalized to accommodate other types of covariates. We demonstrate the advantages of PAMIR through simulations and two real data examples.     

Choosing the right sigmoid growth function using the unified‐models approach

Growth of the young is an important part of the life history in birds. However, modelling methods have paid little attention to the choice of regression model used to describe its pattern. The aim of this study was to evaluate whether a single sigmoid model with an upper asymptote could describe avian growth adequately. We compared unified versions of five growth models of the Richards family (the four‐parameter U‐Richards and the three‐parameter U‐logistic, U‐Gompertz, U‐Bertalanffy and U4‐models) for three traits (body mass, tarsus‐length and wing‐length) for 50 passerine species, including species with varied morphologies and life histories. The U‐family models exhibit a unified set of parameters for all models. The four‐parameter U‐Richards model proved a good choice for fitting growth curves to various traits – its extra d‐parameter allows for a flexible placement of the inflection point. Which of the three‐parameter U‐models was the best performing varied greatly between species and between traits, as each three‐parameter model had a different fixed relative inflection value (fraction of the upper asymptote), implying a different growth pattern. Fixing the asymptotes to averages for adult trait value generally shifted the model preference towards one with lower relative inflection values. Our results illustrate an overlooked difficulty in the analysis of organismal growth, namely, that a single traditional three‐parameter model does not suit all growth data. This is mostly due to differences in inflection placement. Moreover, some biometric traits require more attention when estimating growth rates and other growth‐curve characteristics. We recommend fitting either several three‐parameter models from the U‐family, where the parameters are comparable between models, or only the U‐Richards model.     

Managing for multiple species: greater sage-grouse and sagebrush songbirds

Human activity has altered 33–50% of Earth's surface, including temperate grasslands and sagebrush rangelands, resulting in a loss of biodiversity. By promoting habitat for sensitive or wide-ranging species, less exigent species may be protected in an umbrella effect. The greater sage-grouse (Centrocercus urophasianus; sage-grouse) has been proposed as an umbrella for other sagebrush-obligate species because it has an extensive range that overlaps with many other species, it is sensitive to anthropogenic activity, it requires resources over large landscapes, and its habitat needs are known. The efficacy of the umbrella concept, however, is often assumed and rarely tested. Therefore, we surveyed sage-grouse pellet occurrence and sagebrush-associated songbird abundance in northwest Colorado, USA, to determine the amount of habitat overlap between sage-grouse and 4 songbirds (Brewer's sparrow [Spizella breweri], sage thrasher [Oreoscoptes montanus], sagebrush sparrow [Artemisiospiza nevadensis]), and green-tailed towhee [Pipilo chlorurus]). During May and June 2013–2015, we conducted standard point count breeding surveys for songbirds and counted sage-grouse pellets within 300 10-m radius plots. We modeled songbird abundance and sage-grouse pellet occurrence with multi-scaled environmental features, such as sagebrush cover and bare ground. To evaluate sage-grouse as an umbrella for sagebrush-associated passerines, we determined the correlation between probability of sage-grouse pellet occurrence and model-predicted songbird densities per sampling plot. We then classified the sage-grouse probability of occurrence as high (probability >0.5) and low (probability ≤0.5) and mapped model-predicted surfaces for each species in our study area. We determined average songbird density in areas of high and low probability of sage-grouse occurrence. Sagebrush cover at intermediate scales was an important predictor for all species, and ground cover was important for all species except sage thrashers. Areas with a higher probability of sage-grouse occurrence also contained higher densities of Brewer's sparrows, green-tailed towhees, and sage thrashers, but predicted sagebrush sparrow densities were lower in these areas. In northwest Colorado, sage-grouse may be an effective umbrella for Brewer's sparrows, green-tailed towhees, and sage thrashers, but sage-grouse habitat does not appear to capture areas that support high sagebrush sparrow densities. A multi-species focus may be the best management and conservation strategy for several species of concern, especially those with conflicting habitat requirements. © The Wildlife Society, 2019     

Evaluation of regression models in metabolic physiology: predicting fluxes from isotopic data without knowledge of the pathway

This study explores the ability of regression models, with no knowledge of the underlying physiology, to estimate physiological parameters relevant for metabolism and endocrinology. Four regression models were compared: multiple linear regression (MLR), principal component regression (PCR), partial least-squares regression (PLS) and regression using artificial neural networks (ANN). The pathway of mammalian gluconeogenesis was analyzed using [U−¹³C]glucose as tracer. A set of data was simulated by randomly selecting physiologically appropriate metabolic fluxes for the 9 steps of this pathway as independent variables. The isotope labeling patterns of key intermediates in the pathway were then calculated for each set of fluxes, yielding 29 dependent variables. Two thousand sets were created, allowing independent training and test data. Regression models were asked to predict the nine fluxes, given only the 29 isotopomers. For large training sets (>50) the artificial neural network model was superior, capturing 95% of the variability in the gluconeogenic flux, whereas the three linear models captured only 75%. This reflects the ability of neural networks to capture the inherent non-linearities of the metabolic system. The effect of error in the variables and the addition of random variables to the data set was considered. Model sensitivities were used to find the isotopomers that most influenced the predicted flux values. These studies provide the first test of multivariate regression models for the analysis of isotopomer flux data. They provide insight for metabolomics and the future of isotopic tracers in metabolic research where the underlying physiology is complex or unknown.We acknowledge the support of NIH Grant DK58533 and the DuPont-MIT Alliance.     

黄土高原洛河流域蝗虫群落排序及环境因素分析

应用相似性分析、主成分(PCA)分析及多元逐步回归等方法比较了洛河流域黄土高原不同地区蝗虫的群落结构。结果显示:吴旗和志丹、富县和黄陵的相似性均很高,甘泉则更加靠近富县和黄陵,相似性分析的结果反映了该地区蝗虫群落在地理上的分布规律。对各地点气候因素和蝗虫群落所进行的主成分分析,结果非常理想,三维主成分的累计方差贡献率分别达到了96.22%和92.06%。通过多元逐步回归发现,促进蝗虫发生的气候因子有5~9月平均气温、5~9月平均总降水、年平均总降水和年平均日照时数,抑止因子有5~9月最低降水等;促进蝗虫发生的植被因素有草本植物高度多样性、树木盖度、豆科优势度和其他科优势度等几个因素,抑止因素有禾本科优势度、莎草科优势度等。     

Logistic模型预测东北越冬代水稻二化螟发生期

2001~2003年在吉林省柳河县绿色大米生产稻区,采用Logistic模型拟合越冬代二化螟Chilosuppressalis(Walker),有效积温和诱捕器诱蛾百分率的关系。结果表明logistic模型有较好的拟合性。由模型拟合结果预测当地越冬代二化螟发蛾始盛期、高峰期和盛末期所需有效积温分别为275.9,358.4和440.8日.度,可以适时指导大田防治。     

玉米幼苗抗氧化酶活性、脯氨酸含量变化及与其耐寒性的关系

研究了黄C（耐寒型）和Mo17（低温敏感型）两种耐寒性不同的玉米自交系幼苗在低温逆境（5 ℃,3 d）下,根、中胚轴和胚芽鞘3个部位过氧化氢酶（CAT）、过氧化物酶（POD）活性和脯氨酸（Pro）含量变化及与其耐寒性的关系.结果表明,低温期间,黄C的中胚轴、胚芽鞘相对生长率显著高于Mo17;黄C的根、中胚轴、胚芽鞘3个部位的CAT、POD活性和Pro含量变化率（其中,中胚轴分别为39.02%、-31.35%和86.86%）均高于Mo17（中胚轴变化率分别为1.86%、-48.67%和12.01%）;黄C 3个部位的质膜透性（中胚轴为22.05%）均低于Mo17（中胚轴为29.11%）.逐步回归分析表明黄C低温期间积累的干物质主要与Pro含量有关,而Mo17主要与POD活性有关,玉米幼苗中胚轴对低温反应最敏感,中胚轴Pro含量变化率与玉米耐寒性关系最密切.     

小麦冠层叶片性状基因效应的岭估计

选用6套具有P_1、P_2、B_1、B_2、F_1和F_2的六世代遗传材料,采用K．Mather提出的六参数遗传模型,用岭回归方法估算了小麦冠层叶片性状的基因效应．结果表明：各冠层叶片性状都存在1～3种上位性效应,遗传机制较为复杂,其遗传模式可分为加性—显性—上位性、以加性为主的加性—显性—上位性和显性—上位性3种,还讨论了现有几种常用遗传模式的复共线性,以及对冠层叶片性状的选择．