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PLoS One. 2016 Aug 18;11(8):e0161514. doi: 10.1371/journal.pone.0161514. eCollection 2016.

Discretization of Gene Expression Data Unmasks Molecular Subgroups Recurring in Different Human Cancer Types.

Author information

1
Qlaym Healthcare AG, Hans-Adolf-Krebs Weg 1, 37077 Goettingen, Germany.
2
Cancer Registry Zurich and Zug, University Hospital Zurich, Zurich, Switzerland.
3
Institute of Surgical Pathology, University Hospital Zurich, Schmelzbergstrasse 12, 8091 Zurich, Switzerland.

Abstract

Despite the individually different molecular alterations in tumors, the malignancy associated biological traits are strikingly similar. Results of a previous study using renal cell carcinoma (RCC) as a model pointed towards cancer-related features, which could be visualized as three groups by microarray based gene expression analysis. In this study, we used a mathematic model to verify the presence of these groups in RCC as well as in other cancer types. We developed an algorithm for gene-expression deviation profiling for analyzing gene expression data of a total of 8397 patients with 13 different cancer types and normal tissues. We revealed three common Cancer Transcriptomic Profiles (CTPs) which recurred in all investigated tumors. Additionally, CTPs remained robust regardless of the functions or numbers of genes analyzed. CTPs may represent common genetic fingerprints, which potentially reflect the closely related biological traits of human cancers.

PMID:
27537329
PMCID:
PMC4990327
DOI:
10.1371/journal.pone.0161514
[Indexed for MEDLINE]
Free PMC Article

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