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dc.contributor.authorHolden, Marit
dc.contributor.authorHolden, Lars
dc.contributor.authorOlsen, Karina Standahl
dc.contributor.authorLund, Eiliv
dc.date.accessioned2018-06-26T12:25:09Z
dc.date.available2018-06-26T12:25:09Z
dc.date.issued2017-07-10
dc.description.abstractBackground: Functional genomics in a processual analysis cover the time-dependent changes in transcriptomics and epigenetics before diagnosis of a disease, reflecting the changes in both life style and disease processes. The aim of this paper is to explore the dynamic, time-dependent mechanisms of the metastatic processes, using blood transcriptomics and including time in a continuous manner. For achieving this goal, we have developed new statistical methods based on statistics that are local in time.<p> Methods: The new statistical method, Local in Time Statistics (LITS), is based on calculating statistics in moving windows and randomization. The method has been tested for the analysis of a dataset that collectively provides information on the blood transcriptome up to 8 years before breast cancer diagnosis. The dataset from the Norwegian Women and Cancer (NOWAC) Post-genome Cohort consists of 467 case-control pairs matched on birth year and time of blood sampling. The data for a pair are the difference in log2 gene expression between the case and control. The stratified analyses are based on important biological differences like metastatic versus non-metastatic cancer, and the mode of cancer detection, ie, screening-detected cancers versus clinically detected cancers. The dataset was used for examining whether the gene expression profile varies between cases and controls, with time, or between cases with and without metastases.<p> Results: The null hypotheses of no differences between cases and controls, no time-dependent changes, and no differences between different strata were all rejected. For screening-detected cancers, the probability of correct prediction of metastasis status was best in year 1 before diagnosis compared to year 3 and 4 before diagnosis for clinically detected cancers. The predictor was not very sensitive to the number of genes included.<p> Conclusion: Using a new statistical method, LITS, we have demonstrated time-dependent changes of the blood transcriptome up to 8 years before breast cancer diagnosis.en_US
dc.descriptionSource at <a href=https://doi.org/10.2147/AGG.S130004> https://doi.org/10.2147/AGG.S130004 </a>.en_US
dc.identifier.citationHolden, M., Holden, L., Olsen, K.S. & Lund, E. (2017). Local In Time Statistics for detecting weak gene expression signals in blood – illustrated for prediction of metastases in breast cancer in the NOWAC Post-genome Cohort. Advances in Genomics and Genetics, 7, 11-28. https://doi.org/10.2147/AGG.S130004en_US
dc.identifier.cristinIDFRIDAID 1564222
dc.identifier.doi10.2147/AGG.S130004
dc.identifier.issn1179-9870
dc.identifier.urihttps://hdl.handle.net/10037/13006
dc.language.isoengen_US
dc.publisherDove Medical Pressen_US
dc.relation.journalAdvances in Genomics and Genetics
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/FP7-IDEAS-ERC/232997/EU/TRANSCRIPTOMICS IN CANCER EPIDEMIOLOGY/TICE/en_US
dc.relation.urihttp://publications.nr.no/1518426568/LITS-Holden.pdf
dc.rights.accessRightsopenAccessen_US
dc.subjectVDP::Medisinske Fag: 700en_US
dc.subjectVDP::Medical disciplines: 700en_US
dc.subjectprocessual analysisen_US
dc.subjecttranscriptomicsen_US
dc.subjectpredictionen_US
dc.subjectbreast canceren_US
dc.subjectblooden_US
dc.subjectLocal in Time Statisticsen_US
dc.titleLocal In Time Statistics for detecting weak gene expression signals in blood – illustrated for prediction of metastases in breast cancer in the NOWAC Post-genome Cohorten_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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