Primary extraskeletal osteosarcoma of omentum majus

Extraskeletal osteosarcoma is a rare malignant soft tissue tumor. Here we present a case of a primary extraskeletal osteosarcoma arising from omentum majus in a 40-year-old Chinese woman. Ultrasonography of the pelvic cavity showed a large soft tissue mass with marked calcification. Complete surgical resection of the primary tumor was performed and the histopathological diagnosis was extraskeletal osteosarcoma of omentum majus. She was followed up without adjuvant radiotherapy and chemotherapy, and died from widespread intra-abdominal, lung and liver metastases 7 months postoperatively

Accelerating Haplotype-Based Genome-Wide Association Study Using Perfect Phylogeny and Phase-Known Reference Data

The genome-wide association study (GWAS) has become a routine approach for mapping disease risk loci with the advent of large-scale genotyping technologies. Multi-allelic haplotype markers can provide superior power compared with single-SNP markers in mapping disease loci. However, the application of haplotype-based analysis to GWAS is usually bottlenecked by prohibitive time cost for haplotype inference, also known as phasing. In this study, we developed an efficient approach to haplotype-based analysis in GWAS. By using a reference panel, our method accelerated the phasing process and reduced the potential bias generated by unrealistic assumptions in phasing process. The haplotype-based approach delivers great power and no type I error inflation for association studies. With only a medium-size reference panel, phasing error in our method is comparable to the genotyping error afforded by commercial genotyping solutions

Fifteen new risk loci for coronary artery disease highlight arterial-wall-specific mechanisms

Coronary artery disease (CAD) is a leading cause of morbidity and mortality worldwide. Although 58 genomic regions have been associated with CAD thus far, most of the heritability is unexplained, indicating that additional susceptibility loci await identification. An efficient discovery strategy may be larger-scale evaluation of promising associations suggested by genome-wide association studies (GWAS). Hence, we genotyped 56,309 participants using a targeted gene array derived from earlier GWAS results and performed meta-analysis of results with 194,427 participants previously genotyped, totaling 88,192 CAD cases and 162,544 controls. We identified 25 new SNP-CAD associations (P < 5 × 10(-8), in fixed-effects meta-analysis) from 15 genomic regions, including SNPs in or near genes involved in cellular adhesion, leukocyte migration and atherosclerosis (PECAM1, rs1867624), coagulation and inflammation (PROCR, rs867186 (p.Ser219Gly)) and vascular smooth muscle cell differentiation (LMOD1, rs2820315). Correlation of these regions with cell-type-specific gene expression and plasma protein levels sheds light on potential disease mechanisms

Logical Analysis of Data (LAD) model for the early diagnosis of acute ischemic stroke

<p>Abstract</p> <p>Background</p> <p>Strokes are a leading cause of morbidity and the first cause of adult disability in the United States. Currently, no biomarkers are being used clinically to diagnose acute ischemic stroke. A diagnostic test using a blood sample from a patient would potentially be beneficial in treating the disease.</p> <p>Results</p> <p>A classification approach is described for differentiating between proteomic samples of stroke patients and controls, and a second novel predictive model is developed for predicting the severity of stroke as measured by the National Institutes of Health Stroke Scale (NIHSS). The models were constructed by applying the Logical Analysis of Data (LAD) methodology to the mass peak profiles of 48 stroke patients and 32 controls. The classification model was shown to have an accuracy of 75% when tested on an independent validation set of 35 stroke patients and 25 controls, while the predictive model exhibited superior performance when compared to alternative algorithms. In spite of their high accuracy, both models are extremely simple and were developed using a common set consisting of only 3 peaks.</p> <p>Conclusion</p> <p>We have successfully identified 3 biomarkers that can detect ischemic stroke with an accuracy of 75%. The performance of the classification model on the validation set and on cross-validation does not deteriorate significantly when compared to that on the training set, indicating the robustness of the model. As in the case of the LAD classification model, the results of the predictive model validate the function constructed on our support-set for approximating the severity scores of stroke patients. The correlation and root mean absolute error of the LAD predictive model are consistently superior to those of the other algorithms used (Support vector machines, C4.5 decision trees, Logistic regression and Multilayer perceptron).</p

The Intracellular Localization of ID2 Expression Has a Predictive Value in Non Small Cell Lung Cancer

ID2 is a member of a subclass of transcription regulators belonging to the general bHLH (basic-helix-loophelix) family of transcription factors. In normal cells, ID2 is responsible for regulating the balance between proliferation and differentiation. More recent studies have demonstrated that ID2 is involved in tumor progression in several cancer types such as prostate or breast

Systemic properties of metabolic networks lead to an epistasis-based model for heterosis

The genetic and molecular approaches to heterosis usually do not rely on any model of the genotype–phenotype relationship. From the generalization of Kacser and Burns’ biochemical model for dominance and epistasis to networks with several variable enzymes, we hypothesized that metabolic heterosis could be observed because the response of the flux towards enzyme activities and/or concentrations follows a multi-dimensional hyperbolic-like relationship. To corroborate this, we used the values of systemic parameters accounting for the kinetic behaviour of four enzymes of the upstream part of glycolysis, and simulated genetic variability by varying in silico enzyme concentrations. Then we “crossed” virtual parents to get 1,000 hybrids, and showed that best-parent heterosis was frequently observed. The decomposition of the flux value into genetic effects, with the help of a novel multilocus epistasis index, revealed that antagonistic additive-by-additive epistasis effects play the major role in this framework of the genotype–phenotype relationship. This result is consistent with various observations in quantitative and evolutionary genetics, and provides a model unifying the genetic effects underlying heterosis

The association of two single nucleotide polymorphisms (SNPs) in growth hormone (GH) gene with litter size and superovulation response in goat-breeds

Two active mutations (A 781 G and A 1575 G) in growth hormone (GH) gene, and their associations with litter size (LS), were investigated in both a high prolificacy (Matou, n = 182) and a low prolificacy breed (Boer, n = 352) by using the PCR-RFLP method. Superovulation experiments were designed in 57 dams, in order to evaluate the effect of different genotypes of the GH gene on superovulation response. Two genotypes (AA and AB, CC and CD) in each mutation were detected in these two goat breeds. Neither BB nor DD homozygous genotypes were observed. The genotypic frequencies of AB and CC were significantly higher than those of AA and CD. In the third parity, Matou dams with AB or CC genotypes had significantly larger litter sizes than those with AA and CD (p < 0.05). On combining the two loci, both Matou and Boer dams with ABCD genotype had the largest litter sizes when compared to the other genotypes (p < 0.05). When undergoing like superovulation treatments, a significantly higher number of corpora lutea and ova, with a lower incidence of ovarian cysts, were harvested in the AB and CC genotypes than in AA and CD. These results show that the two loci of GH gene are highly associated with abundant prolificacy and superovulation response in goat breeds

Inhibition of Non-Homologous End Joining Repair Impairs Pancreatic Cancer Growth and Enhances Radiation Response

Pancreatic ductal adenocarcinoma (PDAC) is amongst the deadliest of human cancers, due to its late diagnosis as well as its intense resistance to currently available therapeutics. To identify mechanisms as to why PDAC are refractory to DNA damaging cytoxic chemotherapy and radiation, we performed a global interrogation of the DNA damage response of PDAC. We find that PDAC cells generally harbor high levels of spontaneous DNA damage. Inhibition of Non-Homologous End Joining (NHEJ) repair either pharmacologically or by RNAi resulted in a further accumulation of DNA damage, inhibition of growth, and ultimately apoptosis even in the absence of exogenous DNA damaging agents. In response to radiation, PDAC cells rely on the NHEJ pathway to rapidly repair DNA double strand breaks. Mechanistically, when NHEJ is inhibited there is a compensatory increase in Homologous Recombination (HR). Despite this upregulation of HR, DNA damage persists and cells are significantly more sensitive to radiation. Together, these findings support the incorporation of NHEJ inhibition into PDAC therapeutic approaches, either alone, or in combination with DNA damaging therapies such as radiation