Highly informative marker sets consisting of genes with low individual degree of differential expression.

Autor: Galatenko VV; Moscow State University, Leninskie Gory, 119991 Moscow, Russia.; SRC Bioclinicum, Ugreshskaya str 2/85, 115088 Moscow, Russia., Shkurnikov MY; P. Hertsen Moscow Oncology Research Institute, National Center of Medical Radiological Research, 3 Second Botkinsky Lane, Moscow, 125284, Russia., Samatov TR; SRC Bioclinicum, Ugreshskaya str 2/85, 115088 Moscow, Russia.; Moscow State University of Mechanical Engineering, Bolshaya Semenovskaya str 38, 107023 Moscow, Russia., Galatenko AV; Moscow State University, Leninskie Gory, 119991 Moscow, Russia., Mityakina IA; SRC Bioclinicum, Ugreshskaya str 2/85, 115088 Moscow, Russia., Kaprin AD; P. Hertsen Moscow Oncology Research Institute, National Center of Medical Radiological Research, 3 Second Botkinsky Lane, Moscow, 125284, Russia., Schumacher U; Department of Anatomy and Experimental Morphology, University Cancer Center, University Medical Center Hamburg-Eppendorf, Martinistr. 52, Hamburg D-20246, Germany., Tonevitsky AG; Moscow State University, Leninskie Gory, 119991 Moscow, Russia.; P. Hertsen Moscow Oncology Research Institute, National Center of Medical Radiological Research, 3 Second Botkinsky Lane, Moscow, 125284, Russia.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2015 Oct 08; Vol. 5, pp. 14967. Date of Electronic Publication: 2015 Oct 08.
DOI: 10.1038/srep14967
Abstrakt: Genes with significant differential expression are traditionally used to reveal the genetic background underlying phenotypic differences between cancer cells. We hypothesized that informative marker sets can be obtained by combining genes with a relatively low degree of individual differential expression. We developed a method for construction of highly informative gene combinations aimed at the maximization of the cumulative informative power and identified sets of 2-5 genes efficiently predicting recurrence for ER-positive breast cancer patients. The gene combinations constructed on the basis of microarray data were successfully applied to data acquired by RNA-seq. The developed method provides the basis for the generation of highly efficient prognostic and predictive gene signatures for cancer and other diseases. The identified gene sets can potentially reveal novel essential segments of gene interaction networks and pathways implied in cancer progression.
Databáze: MEDLINE
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