Identification of an Efficient Gene Expression Panel for Glioblastoma Classification.

Autor: Crisman TJ; Semel Institute for Neuroscience & Human Behavior and Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California 90095, United States of America., Zelaya I; Semel Institute for Neuroscience & Human Behavior and Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California 90095, United States of America., Laks DR; Department of Biological Chemistry, University of California Los Angeles, Los Angeles, California 90095, United States of America., Zhao Y; Semel Institute for Neuroscience & Human Behavior and Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California 90095, United States of America., Kawaguchi R; Semel Institute for Neuroscience & Human Behavior and Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California 90095, United States of America., Gao F; Semel Institute for Neuroscience & Human Behavior and Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California 90095, United States of America., Kornblum HI; Semel Institute for Neuroscience & Human Behavior and Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California 90095, United States of America.; Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California Los Angeles, Los Angeles, California 90095, United States of America.; Jonsson Comprehensive Cancer Center, University of California Los Angeles, Los Angeles, California 90095, United States of America., Coppola G; Semel Institute for Neuroscience & Human Behavior and Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, California 90095, United States of America.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2016 Nov 17; Vol. 11 (11), pp. e0164649. Date of Electronic Publication: 2016 Nov 17 (Print Publication: 2016).
DOI: 10.1371/journal.pone.0164649
Abstrakt: We present here a novel genetic algorithm-based random forest (GARF) modeling technique that enables a reduction in the complexity of large gene disease signatures to highly accurate, greatly simplified gene panels. When applied to 803 glioblastoma multiforme samples, this method allowed the 840-gene Verhaak et al. gene panel (the standard in the field) to be reduced to a 48-gene classifier, while retaining 90.91% classification accuracy, and outperforming the best available alternative methods. Additionally, using this approach we produced a 32-gene panel which allows for better consistency between RNA-seq and microarray-based classifications, improving cross-platform classification retention from 69.67% to 86.07%. A webpage producing these classifications is available at http://simplegbm.semel.ucla.edu.
Competing Interests: The authors have declared that no competing interests exist.
Databáze: MEDLINE
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