Opitz, Lennart

[["dc.bibliographiccitation.artnumber","36"],["dc.bibliographiccitation.journal","BMC Medical Genomics"],["dc.bibliographiccitation.volume","3"],["dc.contributor.author","Opitz, Lennart"],["dc.contributor.author","Salinas-Riester, Gabriela"],["dc.contributor.author","Grade, Marian"],["dc.contributor.author","Jung, Klaus"],["dc.contributor.author","Jo, Peter"],["dc.contributor.author","Emons, Georg"],["dc.contributor.author","Ghadimi, Michael B."],["dc.contributor.author","Beißbarth, Tim"],["dc.contributor.author","Gaedcke, Jochen"],["dc.date.accessioned","2018-11-07T08:40:26Z"],["dc.date.available","2018-11-07T08:40:26Z"],["dc.date.issued","2010"],["dc.description.abstract","Background: Gene expression profiling is a highly sensitive technique which is used for profiling tumor samples for medical prognosis. RNA quality and degradation influence the analysis results of gene expression profiles. The impact of this influence on the profiles and its medical impact is not fully understood. As patient samples are very valuable for clinical studies, it is necessary to establish criteria for the RNA quality to be able to use these samples in later analysis. Methods: To investigate the effects of RNA integrity on gene expression profiling, whole genome expression arrays were used. We used tumor biopsies from patients diagnosed with locally advanced rectal cancer. To simulate degradation, the isolated total RNA of all patients was subjected to heat-induced degradation in a time-dependent manner. Expression profiling was then performed and data were analyzed bioinformatically to assess the differences. Results: The differences introduced by RNA degradation were largely outweighed by the biological differences between the patients. Only a relatively small number of probes (275 out of 41,000) show a significant effect due to degradation. The genes that show the strongest effect due to RNA degradation were, especially, those with short mRNAs and probe positions near the 5' end. Conclusions: Degraded RNA from tumor samples (RIN > 5) can still be used to perform gene expression analysis. A much higher biological variance between patients is observed compared to the effect that is imposed by degradation of RNA. Nevertheless there are genes, very short ones and those with the probe binding side close to the 5' end that should be excluded from gene expression analysis when working with degraded RNA. These results are limited to the Agilent 44 k microarray platform and should be carefully interpreted when transferring to other settings."],["dc.description.sponsorship","Deutsche Forschungsgemeinschaft [KFO 179]; BMBF [01GS0890]; BreastSys"],["dc.identifier.doi","10.1186/1755-8794-3-36"],["dc.identifier.isi","000283179600001"],["dc.identifier.pmid","20696062"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/5664"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/19230"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Biomed Central Ltd"],["dc.relation.issn","1755-8794"],["dc.rights","CC BY 2.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/2.0"],["dc.title","Impact of RNA degradation on gene expression profiling"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","86"],["dc.bibliographiccitation.journal","Nutrition & Metabolism"],["dc.bibliographiccitation.volume","9"],["dc.contributor.author","Fromm-Dornieden, Carolin"],["dc.contributor.author","Lytovchenko, Oleksandr"],["dc.contributor.author","Heyde, Silvia von der"],["dc.contributor.author","Behnke, Nina"],["dc.contributor.author","Hogl, Sebastian"],["dc.contributor.author","Berghoff, Janina"],["dc.contributor.author","Koepper, Frederik"],["dc.contributor.author","Opitz, Lennart"],["dc.contributor.author","Renne, Ulla"],["dc.contributor.author","Hoeflich, Andreas"],["dc.contributor.author","Beißbarth, Tim"],["dc.contributor.author","Brenig, Bertram B."],["dc.contributor.author","Baumgartner, Bernhard G."],["dc.date.accessioned","2018-11-07T09:05:46Z"],["dc.date.available","2018-11-07T09:05:46Z"],["dc.date.issued","2012"],["dc.description.abstract","Background: DOR/TP53INP2 acts both at the chromosomal level as a nuclear co-factor e.g. for the thyroid hormone receptor and at the extrachromosomal level as an organizing factor of the autophagosome. In a previous study, DOR was shown to be down-regulated in skeletal muscle of obese diabetic Zucker fa/fa rats. Methods: To identify sites of differential DOR expression in metabolically active tissues, we measured differences in DOR expression in white adipose tissue (WAT), brown adipose tissue (BAT), skeletal muscle (SM) and heart muscle (HM) by qPCR. To assess whether DOR expression is influenced in the short term by nutritional factors, NMRI mice were fed different fat rich diets (fat diet, FD: 18% or high fat diet, HFD: 80% fat) for one week and DOR expression was compared to NMRI mice fed a control diet (normal diet, ND: 3.3% fat). Additionally, DOR expression was measured in young (45 days old) and adult (100 days old) genetically obese (DU6/DU6i) mice and compared to control (DUKs/DUKsi) animals. Results: ANOVA results demonstrate a significant influence of diet, tissue type and sex on DOR expression in adipose and muscle tissues of FD and HFD mice. In SM, DOR expression was higher in HFD than in FD male mice. In WAT, DOR expression was increased compared to BAT in male FD and HFD mice. In contrast, expression levels in female mice were higher in BAT for both dietary conditions. DOR expression levels in all tissues of 100 days old genetically obese animals were mainly influenced by sex. In HM, DOR expression was higher in male than female animals. Conclusions: DOR expression varies under the influence of dietary fat content, tissue type and sex. We identified target tissues for further studies to analyze the specific function of DOR in obesity. DOR might be part of a defense mechanism against fat storage in high fat diets or obesity."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2012"],["dc.identifier.doi","10.1186/1743-7075-9-86"],["dc.identifier.isi","000311434000001"],["dc.identifier.pmid","22995226"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/8284"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/25404"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Biomed Central Ltd"],["dc.relation.issn","1743-7075"],["dc.rights","CC BY 2.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/2.0"],["dc.title","Extrinsic and intrinsic regulation of DOR/TP53INP2 expression in mice: effects of dietary fat content, tissue type and sex in adipose and muscle tissues"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","132"],["dc.bibliographiccitation.issue","2"],["dc.bibliographiccitation.journal","Computer Methods and Programs in Biomedicine"],["dc.bibliographiccitation.lastpage","139"],["dc.bibliographiccitation.volume","100"],["dc.contributor.author","Jung, Klaus"],["dc.contributor.author","Grade, Marian"],["dc.contributor.author","Gaedcke, Jochen"],["dc.contributor.author","Jo, Peter"],["dc.contributor.author","Opitz, Lennart"],["dc.contributor.author","Becker, Heinz"],["dc.contributor.author","Ghadimi, Michael B."],["dc.contributor.author","Beißbarth, Tim"],["dc.date.accessioned","2018-11-07T08:37:24Z"],["dc.date.available","2018-11-07T08:37:24Z"],["dc.date.issued","2010"],["dc.description.abstract","Pre-therapeutic prediction of therapy response and clinical outcome is an important field in medicine and poses new challenges to statisticians. Statistical prediction rules for two-class problems are generally designated to maximize the overall correct classification rate and to reflect an optimal balance of sensitivity and specificity. In some clinical situations, however, correct prediction of one particular class is more important than of the other class. We therefore propose a new strategy of building prediction rules, which are designed to increase the sensitivity, while losing some specificity. This strategy is applied to artificial simulation data and to gene expression data from primary colon cancers. Our concept is generally applicable to most common classification methods. (C) 2010 Elsevier Ireland Ltd. All rights reserved."],["dc.identifier.doi","10.1016/j.cmpb.2010.03.016"],["dc.identifier.isi","000283040000004"],["dc.identifier.pmid","20382441"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/18524"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Elsevier Ireland Ltd"],["dc.relation.issn","0169-2607"],["dc.title","A new sensitivity-preferred strategy to build prediction rules for therapy response of cancer patients using gene expression data"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dspace.entity.type","Publication"]]