Relationship between intratumoral expression of genes coding for xenobiotic-metabolizing enzymes and benefit from adjuvant tamoxifen in estrogen receptor alpha-positive postmenopausal breast carcinoma

INTRODUCTION: Little is known of the function and clinical significance of intratumoral dysregulation of xenobiotic-metabolizing enzyme expression in breast cancer. One molecular mechanism proposed to explain tamoxifen resistance is altered tamoxifen metabolism and bioavailability. METHODS: To test this hypothesis, we used real-time quantitative RT-PCR to quantify the mRNA expression of a large panel of genes coding for the major xenobiotic-metabolizing enzymes (12 phase I enzymes, 12 phase II enzymes and three members of the ABC transporter family) in a small series of normal breast (and liver) tissues, and in estrogen receptor alpha (ERα)-negative and ERα-positive breast tumors. Relevant genes were further investigated in a well-defined cohort of 97 ERα-positive postmenopausal breast cancer patients treated with primary surgery followed by adjuvant tamoxifen alone. RESULTS: Seven of the 27 genes showed very weak or undetectable expression in both normal and tumoral breast tissues. Among the 20 remaining genes, seven genes (CYP2A6, CYP2B6, FMO5, NAT1, SULT2B1, GSTM3 and ABCC11) showed significantly higher mRNA levels in ERα-positive breast tumors than in normal breast tissue, or showed higher mRNA levels in ERα-positive breast tumors than in ERα-negative breast tumors. In the 97 ERα-positive breast tumor series, most alterations of these seven genes corresponded to upregulations as compared with normal breast tissue, with an incidence ranging from 25% (CYP2A6) to 79% (NAT1). Downregulation was rare. CYP2A6, CYP2B6, FMO5 and NAT1 emerged as new putative ERα-responsive genes in human breast cancer. Relapse-free survival was longer among patients with FMO5-overexpressing tumors or NAT1-overexpressing tumors (P = 0.0066 and P = 0.000052, respectively), but only NAT1 status retained prognostic significance in Cox multivariate regression analysis (P = 0.0013). CONCLUSIONS: Taken together, these data point to a role of genes coding for xenobiotic-metabolizing enzymes in breast tumorigenesis, NAT1 being an attractive candidate molecular predictor of antiestrogen responsiveness

Shortening of 3′UTRs Correlates with Poor Prognosis in Breast and Lung Cancer

A major part of the post-transcriptional regulation of gene expression is affected by trans-acting elements, such as microRNAs, binding the 3′ untraslated region (UTR) of their target mRNAs. Proliferating cells partly escape this type of negative regulation by expressing shorter 3′ UTRs, depleted of microRNA binding sites, compared to non-proliferating cells. Using large-scale gene expression datasets, we show that a similar phenomenon takes place in breast and lung cancer: tumors expressing shorter 3′ UTRs tend to be more aggressive and to result in shorter patient survival. Moreover, we show that a gene expression signature based only on the expression ratio of alternative 3′ UTRs is a strong predictor of survival in both tumors. Genes undergoing 3′UTR shortening in aggressive tumors of the two tissues significantly overlap, and several of them are known to be involved in tumor progression. However the pattern of 3′ UTR shortening in aggressive tumors in vivo is clearly distinct from analogous patterns involved in proliferation and transformation

Gene expression meta-analysis supports existence of molecular apocrine breast cancer with a role for androgen receptor and implies interactions with ErbB family

<p>Abstract</p> <p>Background</p> <p>Pathway discovery from gene expression data can provide important insight into the relationship between signaling networks and cancer biology. Oncogenic signaling pathways are commonly inferred by comparison with signatures derived from cell lines. We use the Molecular Apocrine subtype of breast cancer to demonstrate our ability to infer pathways directly from patients' gene expression data with pattern analysis algorithms.</p> <p>Methods</p> <p>We combine data from two studies that propose the existence of the Molecular Apocrine phenotype. We use quantile normalization and XPN to minimize institutional bias in the data. We use hierarchical clustering, principal components analysis, and comparison of gene signatures derived from Significance Analysis of Microarrays to establish the existence of the Molecular Apocrine subtype and the equivalence of its molecular phenotype across both institutions. Statistical significance was computed using the Fasano & Franceschini test for separation of principal components and the hypergeometric probability formula for significance of overlap in gene signatures. We perform pathway analysis using LeFEminer and Backward Chaining Rule Induction to identify a signaling network that differentiates the subset. We identify a larger cohort of samples in the public domain, and use Gene Shaving and Robust Bayesian Network Analysis to detect pathways that interact with the defining signal.</p> <p>Results</p> <p>We demonstrate that the two separately introduced ER^-breast cancer subsets represent the same tumor type, called Molecular Apocrine breast cancer. LeFEminer and Backward Chaining Rule Induction support a role for AR signaling as a pathway that differentiates this subset from others. Gene Shaving and Robust Bayesian Network Analysis detect interactions between the AR pathway, EGFR trafficking signals, and ErbB2.</p> <p>Conclusion</p> <p>We propose criteria for meta-analysis that are able to demonstrate statistical significance in establishing molecular equivalence of subsets across institutions. Data mining strategies used here provide an alternative method to comparison with cell lines for discovering seminal pathways and interactions between signaling networks. Analysis of Molecular Apocrine breast cancer implies that therapies targeting AR might be hampered if interactions with ErbB family members are not addressed.</p

Genome-Wide Profile of Pleural Mesothelioma versus Parietal and Visceral Pleura: The Emerging Gene Portrait of the Mesothelioma Phenotype

Malignant pleural mesothelioma is considered an almost incurable tumour with increasing incidence worldwide. It usually develops in the parietal pleura, from mesothelial lining or submesothelial cells, subsequently invading the visceral pleura. Chromosomal and genomic aberrations of mesothelioma are diverse and heterogenous. Genome-wide profiling of mesothelioma versus parietal and visceral normal pleural tissue could thus reveal novel genes and pathways explaining its aggressive phenotype.Well-characterised tissue from five mesothelioma patients and normal parietal and visceral pleural samples from six non-cancer patients were profiled by Affymetrix oligoarray of 38 500 genes. The lists of differentially expressed genes tested for overrepresentation in KEGG PATHWAYS (Kyoto Encyclopedia of Genes and Genomes) and GO (gene ontology) terms revealed large differences of expression between visceral and parietal pleura, and both tissues differed from mesothelioma. Cell growth and intrinsic resistance in tumour versus parietal pleura was reflected in highly overexpressed cell cycle, mitosis, replication, DNA repair and anti-apoptosis genes. Several genes of the “salvage pathway” that recycle nucleobases were overexpressed, among them TYMS, encoding thymidylate synthase, the main target of the antifolate drug pemetrexed that is active in mesothelioma. Circadian rhythm genes were expressed in favour of tumour growth. The local invasive, non-metastatic phenotype of mesothelioma, could partly be due to overexpression of the known metastasis suppressors NME1 and NME2. Down-regulation of several tumour suppressor genes could contribute to mesothelioma progression. Genes involved in cell communication were down-regulated, indicating that mesothelioma may shield itself from the immune system. Similarly, in non-cancer parietal versus visceral pleura signal transduction, soluble transporter and adhesion genes were down-regulated. This could represent a genetical platform of the parietal pleura propensity to develop mesothelioma.Genome-wide microarray approach using complex human tissue samples revealed novel expression patterns, reflecting some important features of mesothelioma biology that should be further explored

Hypericum sp.: essential oil composition and biological activities

Phytochemical composition of Hypericum genus has been investigated for many years. In the recent past, studies on the essential oils (EO) of this genus have been progressing and many of them have reported interesting biological activities. Variations in the EO composition of Hypericum species influenced by seasonal variation, geographic distribution, phenological cycle and type of the organ in which EO are produced and/or accumulated have also been reported. Although many reviews attributed to the characterization as well as biological activities of H. perforatum crude extracts have been published, no review has been published on the EO composition and biological activities of Hypericum species until recently (Crockett in Nat Prod Commun 5(9):1493–1506, 2010; Bertoli et al. in Global Sci Books 5:29–47, 2011). In this article, we summarize and update information regarding the composition and biological activities of Hypericum species EO. Based on experimental work carried out in our laboratory we also mention possible biotechnology approaches envisaging EO improvement of some species of the genus.Fundação para a Ciência e a Tecnologia (FCT) - project PTDC/AGR AAM/70418/2006, SFRH/BD/ 13283/2003

Thermoeconomic analysis and optimization of a Re-compression supercritical CO2 cycle using waste heat of Gaziantep Municipal Solid Waste Power Plant

This paper presents thermodynamic and thermoeconomic analyses as well as optimization of a re-compression supercritical CO2 cycle. A gas turbine cycle (GT) is adapted as a model to an existing plant to generate additional power in Gaziantep Municipal Solid Waste Power Plant (GMSWPP). The total capital cost rate and total cost rate of the GT cycle are found to be 20.47 $/h and 77.14$/h, respectively utilizing SPECO by using the exhaust gas of 16 kg/s with 1.9 bar and 566.7 °c. The net power, the energy and exergy efficiencies, the total cost and the total capital cost rates of the GT cycle are optimized by +1.73%, +3.21%, +2.45%, ?1.11% and ?1.64%, respectively using NSGA-II in MATLAB in the range of 2.5?PR?4, 200?P6?216, 16?T0?23 and 9.1?LMTD?12.9. This paper provides an originality such that optimization as well as thermodynamic and thermoeconomic analyses is performed simultaneously for an existing MSW power plant, which can be stressed that there are scarce amounts of studies related on this field. Moreover, as another novelty, it can be emphasized that net power output of such like plants which have similar capacity can be improved using the developed model and NSGA-II optimization method. © 2017 Elsevier Lt

Thermoeconomic multi-objective optimization of an organic Rankine cycle (ORC) adapted to an existing solid waste power plant

In this paper, thermodynamic and thermoeconomic analyses, and also optimization of an organic Rankine cycle (ORC) were performed. The system was adapted to an existing solid waste power plant with a 5.66 MW installed power capacity in order to produce additional power from the exhaust gas. The actual operating data of the plant were utilized during all stages of the analyses. The originality of this paper is based on the analysis of the possibility of the energy conversion of an exhaust gas with a temperature of 566 & #x000B0;C into the electricity by utilizing an ORC system in the concept of waste-to-energy. Four different working fluids: toluene, octamethyltrisiloxane (MDM), octamethyl cyclotetrasiloxane (D4) and n-decane were considered and analyzed for the current system. This is also another novelty of this study due to lack of such a study, in the open literature, that deals with an ORC utilized for a typical municipal solid waste power plant. According to the thermoeconomic analyses, toluene was found to be the optimum working fluid with the maximum power output of 584.6 kW and the exergy efficiency of 15.69%. The optimization of the cycle was performed by using the non-dominated sorting genetic algorithm method (NSGA-II) in MATLAB software environment. The optimization results were compared and the deviations of the net power output and the total cost rate were evaluated as & #x2212;5.89%, & #x2212;3.51 & #x00024;/h for toluene; 0.96%, & #x2212;3.60 & #x00024;/h for MDM; 8.45%, & #x2212;2.04 & #x00024;/h for D4 and 2.00%, & #x2212;5.54 & #x00024;/h for n-decane, respectively. © 2018 Elsevier Lt