Breast cancer prognosis risk estimation using integrated gene expression and clinical data

Novel prognostic markers are needed so newly diagnosed breast cancer patients do not undergo any unnecessary therapy. Various microarray gene expression datasets based studies have generated gene signatures to predict the prognosis outcomes, while ignoring the large amount of information contained in established clinical markers. Nevertheless, small sample sizes in individual microarray datasets remain a bottleneck in generating robust gene signatures that show limited predictive power. The aim of this study is to achieve high classification accuracy for the good prognosis group and then achieve high classification accuracy for the poor prognosis group.

Sub-lethal UV-C radiation induces callose, hydrogen peroxide and defence-related gene expression in Arabidopsis thaliana

Exposure of plants to UV-C irradiation induces gene expression and cellular responses that are commonly associated with wounding and pathogen defence, and in some cases can lead to increased resistance against pathogen infection. We examined, at a physiological, molecular and biochemical level, the effects of and responses to, sub-lethal UV-C exposure on Arabidopsis plants when irradiated with increasing dosages of UV-C radiation. Following UV-C exposure plants had reduced leaf areas over time, with the severity of reduction increasing with dosage. Severe morphological changes that included leaf glazing, bronzing and curling were found to occur in plants treated with the 1000 J·m(-2) dosage. Extensive damage to the mesophyll was observed, and cell death occurred in both a dosage- and time-dependent manner. Analysis of H2 O2 activity and the pathogen defence marker genes PR1 and PDF1.2 demonstrated induction of these defence-related responses at each UV-C dosage tested. Interestingly, in response to UV-C irradiation the production of callose (β-1,3-glucan) was identified at all dosages examined. Together, these results show plant responses to UV-C irradiation at much lower doses than have previously been reported, and that there is potential for the use of UV-C as an inducer of plant defence.

Fatty acid-specific alterations in leptin, PPARα, and CPT-1 gene expression in the rainbow trout

It is known that fatty acids (FA) regulate lipid metabolism by modulating the expression of numerous genes. In order to gain a better understanding of the effect of individual FA on lipid metabolism related genes in rainbow trout (Oncorhynchus mykiss), an in vitro time-course study was implemented where twelve individual FA (butyric 4:0; caprylic 8:0; palmitic (PAM) 16:0; stearic (STA) 18:0; palmitoleic16:1n-7; oleic 18:1n-9; 11-cis-eicosenoic 20:1n-9; linoleic (LNA) 18:2n-6; α-linolenic (ALA) 18:3n-3; eicosapentenoic (EPA) 20:5n-3; docosahexaenoic (DHA) 22:6n-3; arachidonic (ARA) 20:4n-6) were incubated in rainbow trout liver slices. The effect of FA administration over time was evaluated on the expression of leptin, PPARα and CPT-1 (lipid oxidative related genes). Leptin mRNA expression was down regulated by saturated fatty acids (SFA) and LNA, and was up regulated by monounsaturated fatty acids (MUFA) and long chain PUFA, whilst STA and ALA had no effect. PPARα and CPT-1mRNA expression were up regulated by SFA, MUFA, ALA, ARA and DHA; and down regulated by LNA and EPA. These results suggest that there are individual and specific FA induced modifications of leptin, PPARα and CPT-1 gene expression in rainbow trout, and it is envisaged that such results may provide highly valuable information for future practical applications in fish nutrition.

Postexercise muscle cooling enhances gene expression of PGC-1α

This study aimed to investigate the influence of localized muscle cooling on postexercise vascular, metabolic, and mitochondrial-related gene expression.

IPA: Integrated predictive gene signature from gene expression based breast cancer patient samples

Background: Novel predictive markers are needed to accurately diagnose the breast cancer patients so they do not need to undergo any unnecessary aggressive therapies. Various gene expression studies based predictive gene signatureshave generated in the recent past to predict the binary estrogen-receptor subclass or to predict the therapy response subclass. However, the existing algorithms comes with many limitations, including low predictive performances over multiple cohorts of patients and non-significant or limited biological roles associated with thepredictive gene signatures. Therefore, the aim of this study is to develop novel predictive markers with improved performances.Methods: We propose a novel prediction algorithm called IPA to construct a predictive gene signature for performing multiple prediction tasks of predicting estrogen-receptor based binary subclass and predicting chemotherapy response (neoadjuvantly) based binary subclass. The constructed gene signature with considering multiple classification techniques was used to evaluate the algorithm performance on multiple cohorts of breast cancer patients.Results: The evaluation on multiple validation cohorts demonstrated that proposed algorithm achieved stable and high performance to perform prediction tasks, with consideration given to any classification techniques. We show that the predictive gene signature of our proposed algorithm reflects the mechanisms underlying the estrogen-receptors or response to therapy with significant greater biological interpretations, compared with the other existing algorithm.

Hidden Markov models for cancer classification using gene expression profiles

This paper introduces an approach to cancer classification through gene expression profiles by designing supervised learning hidden Markov models (HMMs). Gene expression of each tumor type is modelled by an HMM, which maximizes the likelihood of the data. Prominent discriminant genes are selected by a novel method based on a modification of the analytic hierarchy process (AHP). Unlike conventional AHP, the modified AHP allows to process quantitative factors that are ranking outcomes of individual gene selection methods including t-test, entropy, receiver operating characteristic curve, Wilcoxon test and signal to noise ratio. The modified AHP aggregates ranking results of individual gene selection methods to form stable and robust gene subsets. Experimental results demonstrate the performance dominance of the HMM approach against six comparable classifiers. Results also show that gene subsets generated by modified AHP lead to greater accuracy and stability compared to competing gene selection methods, i.e. information gain, symmetrical uncertainty, Bhattacharyya distance, and ReliefF. The modified AHP improves the classification performance not only of the HMM but also of all other classifiers. Accordingly, the proposed combination between the modified AHP and HMM is a powerful tool for cancer classification and useful as a real clinical decision support system for medical practitioners.

Statin-induced increases in atrophy gene expression occur independently of changes in PGC1α protein and mitochondrial content

One serious side effect of statin drugs is skeletal muscle myopathy. Although the mechanism(s) responsible for statin myopathy remains to be fully determined, an increase in muscle atrophy gene expression and changes in mitochondrial content and/or function have been proposed to play a role. In this study, we examined the relationship between statin-induced expression of muscle atrophy genes, regulators of mitochondrial biogenesis, and markers of mitochondrial content in slow- (ST) and fast-twitch (FT) rat skeletal muscles. Male Sprague Dawley rats were treated with simvastatin (60 or 80 mg·kg(-1)·day(-1)) or vehicle control via oral gavage for 14 days. In the absence of overt muscle damage, simvastatin treatment induced an increase in atrogin-1, MuRF1 and myostatin mRNA expression; however, these were not associated with changes in peroxisome proliferator gamma co-activator 1 alpha (PGC-1α) protein or markers of mitochondrial content. Simvastatin did, however, increase neuronal nitric oxide synthase (nNOS), endothelial NOS (eNOS) and AMPK α-subunit protein expression, and tended to increase total NOS activity, in FT but not ST muscles. Furthermore, simvastatin induced a decrease in β-hydroxyacyl CoA dehydrogenase (β-HAD) activity only in FT muscles. These findings suggest that the statin-induced activation of muscle atrophy genes occurs independent of changes in PGC-1α protein and mitochondrial content. Moreover, muscle-specific increases in NOS expression and possibly NO production, and decreases in fatty acid oxidation, could contribute to the previously reported development of overt statin-induced muscle damage in FT muscles.

Maternal prenatal mental health and placental 11β-HSD2 gene expression: initial findings from the mercy pregnancy and emotional wellbeing study

High intrauterine cortisol exposure can inhibit fetal growth and have programming effects for the child's subsequent stress reactivity. Placental 11beta-hydroxysteroid dehydrogenase (11β-HSD2) limits the amount of maternal cortisol transferred to the fetus. However, the relationship between maternal psychopathology and 11β-HSD2 remains poorly defined. This study examined the effect of maternal depressive disorder, antidepressant use and symptoms of depression and anxiety in pregnancy on placental 11β-HSD2 gene (HSD11B2) expression. Drawing on data from the Mercy Pregnancy and Emotional Wellbeing Study, placental HSD11B2 expression was compared among 33 pregnant women, who were selected based on membership of three groups; depressed (untreated), taking antidepressants and controls. Furthermore, associations between placental HSD11B2 and scores on the State-Trait Anxiety Inventory (STAI) and Edinburgh Postnatal Depression Scale (EPDS) during 12-18 and 28-34 weeks gestation were examined. Findings revealed negative correlations between HSD11B2 and both the EPDS and STAI (r = -0.11 to -0.28), with associations being particularly prominent during late gestation. Depressed and antidepressant exposed groups also displayed markedly lower placental HSD11B2 expression levels than controls. These findings suggest that maternal depression and anxiety may impact on fetal programming by down-regulating HSD11B2, and antidepressant treatment alone is unlikely to protect against this effect.