Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: EC:2.7.10.1 (ERK)
 95,504 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

BACKGROUND: Spontaneous premature ovarian failure presents most commonly with secondary amenorrhea. Young women with the disorder are infertile and experience the symptoms and sequelae of estrogen deficiency. The mechanisms that give rise to spontaneous premature ovarian failure are largely unknown, but many reports suggest a genetic mechanism in some cases. The small family size associated with infertility makes genetic linkage analysis studies extremely difficult. Another approach that has proven successful has been to examine candidate genes based on known genetic phenotypes in other species. Studies in mice have demonstrated that c-kit, a transmembrane tyrosine kinase receptor, plays a critical role in gametogenesis. Here we test the hypothesis that human KIT mutations might be a cause of spontaneous premature ovarian failure. METHODS AND RESULTS: We examined 42 women with spontaneous premature ovarian failure and found partial X monosomy in two of them. In the remaining 40 women with known 46,XX spontaneous premature ovarian failure we evaluated the entire coding region of the KIT gene. We did this using polymerase chain reaction based single-stranded conformational polymorphism analysis and DNA sequencing. We did not identify a single mutation that would alter the amino acid sequence of the c-KIT protein in any of 40 patients (upper 95% confidence limit is 7.2%). We found one silent mutation at codon 798 and two intronic polymorphisms. CONCLUSION: Mutations in the coding regions of the KIT gene appear not to be a common cause of 46,XX spontaneous premature ovarian failure in North American women.
BMC Womens Health 2002 Aug 02
PMID:Investigation of KIT gene mutations in women with 46,XX spontaneous premature ovarian failure. 1215 2

During the past decade, progress in endocrine therapy and the use of trastuzumab has significantly contributed to the decline in breast cancer mortality for hormone receptor-positive and ERBB2 (HER2)-positive cases, respectively. As a result of these advances, a breast cancer cluster with poor prognosis that is negative for the estrogen receptor (ESR1), the progesterone receptor (PRGR) and ERBB2 (triple negative) has come to the forefront of medical therapeutic attention. DNA microarray analyses have revealed that this cluster is phenotypically most like the basal-like breast cancer that is caused by deficiencies in the BRCA1 pathways. To gain further improvements in breast cancer survival, new types of drugs might be required, and small molecules targeting the ubiquitin proteasome system have moved into the spotlight. The success of bortezomib in the treatment of multiple myeloma has sent encouraging signals that proteasome inhibitors could be used to treat other types of cancers. In addition, ubiquitin E3s involved in ESR1, ERBB2 or BRCA1 pathways could be ideal targets for therapeutic intervention. This review summarizes the ubiquitin proteasome pathways related to these proteins and discusses the possibility of new drugs for the treatment of breast cancers. PUBLICATION HISTORY : Republished from Current BioData's Targeted Proteins database (TPdb; http://www.targetedproteinsdb.com).
BMC Biochem 2008 Oct 21
PMID:The UPS: a promising target for breast cancer treatment. 1900 32

The approval of sorafenib and active development of many other molecularly targeted agents in hepatocellular carcinoma (HCC) have presented a challenge to understand the mechanism of action of sorafenib and identify predictive biomarkers to select patients more likely to benefit from sorafenib. The preclinical study by Zhang and celleagues published this month in BMC Medicine provides preliminary evidence that baseline phosphorylated extracellular signaling-regulated kinase (pERK) may be a relevant marker to reflect the level of constitutive activation of the RAF/mitogen-activated protein kinase kinase (MEK)/ERK signaling pathway and has the potential value in predicting response to sorafenib. The clinical data from the initial single arm phase II study and preliminary report from the randomized phase III study also suggest the correlation of baseline archived tumor pERK levels and time to tumor progression in HCC patients. Whether baseline pERK will prove to be a useful predictive biomarker of response and clinical benefits for sorafenib in HCC will need to be validated in future large prospective studies.
BMC Med 2009 Aug 24
PMID:Predicting the response to sorafenib in hepatocellular carcinoma: where is the evidence for phosphorylated extracellular signaling-regulated kinase (pERK)? 1970 70

The associations existing among different biomarkers are important in clinical settings because they contribute to the characterisation of specific pathways related to the natural history of the disease, genetic and environmental determinants. Despite the availability of binary/linear (or at least monotonic) correlation indices, the full exploitation of molecular information depends on the knowledge of direct/indirect conditional independence (and eventually causal) relationships among biomarkers, and with target variables in the population of interest. In other words, that depends on inferences which are performed on the joint multivariate distribution of markers and target variables. Graphical models, such as Bayesian Networks, are well suited to this purpose. Therefore, we reconsidered a previously published case study on classical biomarkers in breast cancer, namely estrogen receptor (ER), progesterone receptor (PR), a proliferative index (Ki67/MIB-1) and to protein HER2/neu (NEU) and p53, to infer conditional independence relations existing in the joint distribution by inferring (learning) the structure of graphs entailing those relations of independence. We also examined the conditional distribution of a special molecular phenotype, called triple-negative, in which ER, PR and NEU were absent. We confirmed that ER is a key marker and we found that it was able to define subpopulations of patients characterized by different conditional independence relations among biomarkers. We also found a preliminary evidence that, given a triple-negative profile, the distribution of p53 protein is mostly supported in 'zero' and 'high' states providing useful information in selecting patients that could benefit from an adjuvant anthracyclines/alkylating agent-based chemotherapy.
BMC Bioinformatics 2009 Oct 15
PMID:Conditional independence relations among biological markers may improve clinical decision as in the case of triple negative breast cancers. 1982 73

A new approach is described here to predict ubiquitinated substrates of the E3 ubiquitin ligase, TRAF6, which takes into account its interaction with the scaffold protein SQSTM1/p62. A novel TRAF6 ubiquitination motif defined as [-(hydrophobic)-k-(hydrophobic)-x-x-(hydrophobic)- (polar)-(hydrophobic)-(polar)-(hydrophobic)] was identified and used to screen the TRAF6/p62 interactome composed of 155 proteins, that were either TRAF6 or p62 interactors, or a negative dataset, composed of 54 proteins with no known association to either TRAF6 or p62. NRIF (K19), TrkA (K485), TrkB (K811), TrkC (K602 and K815), NTRK2 (K828), NTRK3 (K829) and MBP (K169) were found to possess a perfect match for the amino acid consensus motif for TRAF6/p62 ubiquitination. Subsequent analyses revealed that this motif was biased to the C-terminal regions of the protein (nearly 50% the sites), and had preference for loops (~50%) and helices (~37%) over beta-strands (15% or less). In addition, the motif was observed to be in regions that were highly solvent accessible (nearly 90%). Our findings suggest that specific Lysines may be selected for ubiquitination based upon an embedded code defined by a specific amino acid motif with structural determinants. Collectively, our results reveal an unappreciated role for the scaffold protein in targeting ubiquitination. The findings described herein could be used to aid in identification of other E3/scaffold ubiquitination sites.
BMC Proc 2011 May 28
PMID:Mining the TRAF6/p62 interactome for a selective ubiquitination motif. 2155 62

Aitkin recently proposed an integrated Bayesian/likelihood approach that he claims is general and simple. We have applied this method, which does not rely on informative prior probabilities or large-sample results, to investigate the evidence of association between disease and the 16 variants in the KDR gene provided by Genetic Analysis Workshop 17. Based on the likelihood of logistic regression models and considering noninformative uniform prior probabilities on the coefficients of the explanatory variables, we used a random walk Metropolis algorithm to simulate the distributions of deviance and deviance difference. The distribution of probability values and the distribution of the proportions of positive deviance differences showed different locations, but the direction of the shift depended on the genetic factor. For the variant with the highest minor allele frequency and for any rare variant, standard logistic regression showed a higher power than the novel approach. For the two variants with the strongest effects on Q1 under a type I error rate of 1%, the integrated approach showed a higher power than standard logistic regression. The advantages and limitations of the integrated Bayesian/likelihood approach should be investigated using additional regions and considering alternative regression models and collapsing methods.
BMC Proc 2011 Nov 29
PMID:Using the posterior distribution of deviance to measure evidence of association for rare susceptibility variants. 2237 31

Rare variants are believed to play an important role in disease etiology. Recent advances in high-throughput sequencing technology enable investigators to systematically characterize the genetic effects of both common and rare variants. We introduce several approaches that simultaneously test the effects of common and rare variants within a single-nucleotide polymorphism (SNP) set based on logistic regression models and logistic kernel machine models. Gene-environment interactions and SNP-SNP interactions are also considered in some of these models. We illustrate the performance of these methods using the unrelated individuals data from Genetic Analysis Workshop 17. Three true disease genes (FLT1, PIK3C3, and KDR) were consistently selected using the proposed methods. In addition, compared to logistic regression models, the logistic kernel machine models were more powerful, presumably because they reduced the effective number of parameters through regularization. Our results also suggest that a screening step is effective in decreasing the number of false-positive findings, which is often a big concern for association studies.
BMC Proc 2011 Nov 29
PMID:SNP set analysis for detecting disease association using exon sequence data. 2237 33

Use of trait-dependent sampling designs in whole-genome association studies of sequence data can reduce total sequencing costs with modest losses of statistical efficiency. In a quantitative trait (QT) analysis of data from the Genetic Analysis Workshop 17 mini-exome for unrelated individuals in the Asian subpopulation, we investigate alternative designs that sequence only 50% of the entire cohort. In addition to a simple random sampling design, we consider extreme-phenotype designs that are of increasing interest in genetic association analysis of QTs, especially in studies concerned with the detection of rare genetic variants. We also evaluate a novel sampling design in which all individuals have a nonzero probability of being selected into the sample but in which individuals with extreme phenotypes have a proportionately larger probability. We take differential sampling of individuals with informative trait values into account by inverse probability weighting using standard survey methods which thus generalizes to the source population. In replicate 1 data, we applied the designs in association analysis of Q1 with both rare and common variants in the FLT1 gene, based on knowledge of the generating model. Using all 200 replicate data sets, we similarly analyzed Q1 and Q4 (which is known to be free of association with FLT1) to evaluate relative efficiency, type I error, and power. Simulation study results suggest that the QT-dependent selection designs generally yield greater than 50% relative efficiency compared to using the entire cohort, implying cost-effectiveness of 50% sample selection and worthwhile reduction of sequencing costs.
BMC Proc 2011 Nov 29
PMID:Are quantitative trait-dependent sampling designs cost-effective for analysis of rare and common variants? 2237 46

We propose a factor-screening method based on a Bayesian model selection framework and apply it to Genetic Analysis Workshop 17 simulated data with unrelated individuals to identify genes and SNP variants associated with the quantitative trait Q1. A Metropolis-Hasting algorithm is implemented to generate a posterior distribution in a restricted model space and thus the marginal posterior distribution of each variant. Our framework provides flexibility to make inferences on either individual variants or genes. We obtained results for 10 simulated data sets. Our methods are able to identify FTP1 and KDR, two genes that are associated with Q1 in a majority of replicates.
BMC Proc 2011 Nov 29
PMID:Identification of genes and variants associated with quantitative traits using Bayesian factor screening. 2237 83

We develop statistical methods for detecting rare variants that are associated with quantitative traits. We propose two strategies and their combination for this purpose: the iterative regression strategy and the extreme values strategy. In the iterative regression strategy, we use iterative regression on residuals and a multimarker association test to identify a group of significant variants. In the extreme values strategy, we use individuals with extreme trait values to select candidate genes and then test only these candidate genes. These two strategies are integrated into a hybrid approach through a weighting technology. We apply the proposed methods to analyze the Genetic Analysis Workshop 17 data set. The results show that the hybrid approach is the most powerful approach. Using the hybrid approach, the average power to detect causal genes for Q1 is about 40% and the powers to detect FLT1 and KDR are 100% and 68% for Q1, respectively. The powers to detect VNN3 and BCHE are 34% and 30% for Q2, respectively.
BMC Proc 2011 Nov 29
PMID:Detection of rare variant effects in association studies: extreme values, iterative regression, and a hybrid approach. 2237 88


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