The Role of Glutathione Transferase Omega-Class Variant Alleles in Individual Susceptibility to Ovarian Cancer.

The tumor microenvironment is affected by reactive oxygen species and has been suggested to have an important role in ovarian cancer (OC) tumorigenesis. The role of glutathione transferases (GSTs) in the maintenance of redox balance is considered as an important contributing factor in cancer, including OC. Furthermore, GSTs are mostly encoded by highly polymorphic genes, which further highlights their potential role in OC, known to originate from accumulated genetic changes. Since the potential relevance of genetic variations in omega-class GSTs (GSTO1 and GSTO2), with somewhat different activities such as thioltransferase and dehydroascorbate reductase activity, has not been clarified as yet in terms of susceptibility to OC, we aimed to investigate whether the presence of different GSTO1 and GSTO2 genetic variants, individually or combined, might represent determinants of risk for OC development. Genotyping was performed in 110 OC patients and 129 matched controls using a PCR-based assay for genotyping single nucleotide polymorphisms. The results of our study show that homozygous carriers of the GSTO2 variant G allele are at an increased risk of OC development in comparison to the carriers of the referent genotype (OR1 = 2.16, 95% CI: 0.88-5.26, p = 0.08; OR2 = 2.49, 95% CI: 0.93-6.61, p = 0.06). Furthermore, individuals with GST omega haplotype H2, meaning the concomitant presence of the GSTO1*A and GSTO2*G alleles, are more susceptible to OC development, while carriers of the H4 (*A*A) haplotype exhibited lower risk of OC when crude and adjusted haplotype analysis was performed (OR1 = 0.29; 95% CI: 0.12-0.70; p = 0.007 and OR2 = 0.27; 95% CI: 0.11-0.67; p = 0.0054). Overall, our results suggest that GSTO locus variants may confer OC risk.

Glutathione Transferase P1: Potential Therapeutic Target in Ovarian Cancer.

Chemotherapy resistance of ovarian cancer, regarded as the most lethal malignant gynecological disease, can be explained by several mechanisms, including increased activity of efflux transporters leading to decreased intracellular drug accumulation, increased efflux of the therapeutic agents from the cell by multidrug-resistance-associated protein (MRP1), enhanced DNA repair, altered apoptotic pathways, silencing of a number of genes, as well as drug inactivation, especially by glutathione transferase P1 (GSTP1). Indeed, GSTP1 has been recognized as the major enzyme responsible for the conversion of drugs most commonly used to treat metastatic ovarian cancer into less effective forms. Furthermore, GSTP1 may even be responsible for chemoresistance of non-GST substrate drugs by mechanisms such as interaction with efflux transporters or different signaling molecules involved in regulation of apoptosis. Recently, microRNAs (miRNAs) have been identified as important gene regulators in ovarian cancer, which are able to target GST-mediated drug metabolism in order to regulate drug resistance. So far, miR-186 and miR-133b have been associated with reduced ovarian cancer drug resistance by silencing the expression of the drug-resistance-related proteins, GSTP1 and MDR1. Unfortunately, sometimes miRNAs might even enhance the drug resistance in ovarian cancer, as shown for miR-130b. Therefore, chemoresistance in ovarian cancer treatment represents a very complex process, but strategies that influence GSTP1 expression in ovarian cancer as a therapeutic target, as well as miRNAs affecting GSTP1 expression, seem to represent promising predictors of chemotherapeutic response in ovarian cancer, while at the same time represent potential targets to overcome chemoresistance in the future.

The association of hs-CRP and fibrinogen with anthropometric and lipid parameters in non-obese adolescent girls with polycystic ovary syndrome.

Background The aim of the study was to evaluate high-sensitivity C-reactive protein (hs-CRP) and fibrinogen in non-obese normoinsulinemic adolescent girls with polycystic ovary syndrome (PCOS) and their relationship with anthropometric and lipid parameters. Methods The study comprised a total of 26 adolescent girls newly diagnosed with PCOS and 12 healthy controls with regular ovulatory menstrual cycles. The concentration of hs-CRP, fibrinogen, anthropometric measurements, and biochemical and hormonal testing were assessed. Results PCOS adolescent girls had significantly higher levels of hs-CRP and fibrinogen compared to healthy controls. In univariate regression analysis, statistically significant associations of hs-CRP and fibrinogen levels of PCOS patients have been shown with body mass index (BMI), waist circumference (WC), hip circumference (HC) and low-density lipoprotein (LDL)/high-density lipoprotein (HDL) ratio, while hs-CRP levels were also associated with cholesterol and LDL. In the multivariate regression model, we found that hs-CRP levels were predicted by BMI (ß=0.541, p<0.001) and LDL (ß=0.507, p=0.014), while fibrinogen levels were predicted by BMI (ß=0.449, p=0.004). Conclusions We have shown an association of proinflammatory indices hs-CRP and fibrinogen with anthropometric and lipid parameters of adolescent women with PCOS. The inflammatory markers might be useful in monitoring normal-weight adolescent women with PCOS in an effort to timely prevent unfavorable changes in body mass and lipid profile.

Constrained Nets for Graph Matching and Other Quadratic Assignment Problems.

Some time ago Durbin and Willshaw proposed an interesting parallel algorithm (the "elastic net") for approximately solving some geometric optimization problems, such as the Traveling Salesman Problem. Recently it has been shown that their algorithm is related to neural networks of Hopfield and Tank, and that they both can be understood as the semiclassical approximation to statistical mechanics of related physical models. The main point of the elastic net algorithm is seen to be in the way one deals with the constraints when evaluating the effective cost function (free energy in the thermodynamic analogy), and not in its geometric foundation emphasized originally by Durbin and Willshaw. As a consequence, the elastic net algorithm is a special case of the more general physically based computations and can be generalized to a large class of nongeometric problems. In this paper we further elaborate on this observation, and generalize the elastic net to the quadratic assignment problem. We work out in detail its special case, the graph matching problem, because it is an important problem with many applications in computational vision and neural modeling. Simulation results on random graphs, and on structured (hand-designed) graphs of moderate size (20-100 nodes) are discussed.