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Segmented regression, a versatile tool to analyze mRNA levels in relation to DNA copy number aberrations.

Artikel i vetenskaplig tidskrift
Författare Szilard Nemes
Toshima Z Parris
Anna Danielsson
Marie Kannius-Janson
Junmei Miao Jonasson
Gunnar Steineck
Khalil Helou
Publicerad i Genes, chromosomes & cancer
Volym 51
Nummer/häfte 1
Sidor 77-82
ISSN 1098-2264
Publiceringsår 2012
Publicerad vid Institutionen för kliniska vetenskaper, Avdelningen för onkologi
Institutionen för kemi och molekylärbiologi
Sidor 77-82
Språk en
Länkar dx.doi.org/10.1002/gcc.20934
Ämneskategorier Cancer och onkologi

Sammanfattning

DNA copy number aberrations (CNA) and subsequent altered gene expression profiles (mRNA levels) are characteristic features of cancerous cells. Integrative genomic analysis aims to identify recurrent CNA that may have a potential role in cancer development, assuming that gene amplification is accompanied by overexpression, while deletions give rise to downregulation of gene expression. We propose a segmented regression-based approach to identify CNA-driven alteration of gene expression profiles. Segmented regression allows to fit piecewise linear models in different domains of CNA joined by a change-point, where the mRNA-CNA relationship undergoes structural changes. Here, we illustrate the implementation and applicability of the proposed model using 1,161 chromosome fragments detected as DNA CNA in primary tumors from 97 breast cancer patients. We identified significant CNA-driven changes in gene expression levels for 341 chromosome fragments, of which 72 showed a nonlinear relationship to CNA. For 59 of 72 chromosome fragments (82%), we observed an initial increase in mRNA levels due to changes in CNA. After the change-point was passed, the mRNA levels reached a plateau, and a further increase in DNA copy numbers did not induce further elevation in mRNA levels. In contrast, for 13 chromosome fragments, the change-point marked the point where mRNA production accelerated. We conclude that segmented regression modeling may provide valuable insights into the impact CNA have on gene expression in cancer cells. © 2011 Wiley Periodicals, Inc.

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