TY - JOUR
T1 - The proteomic landscape of glioma stem-like cells
AU - Lichti, Cheryl F.
AU - Wildburger, Norelle C.
AU - Shavkunov, Alexander S.
AU - Mostovenko, Ekaterina
AU - Liu, Huiling
AU - Sulman, Erik P.
AU - Nilsson, Carol L.
N1 - Funding Information:
Support from the Cancer Prevention and Research Institute of Texas (CLN) and the University of Texas Medical Branch (CLN) is gratefully acknowledged, as are the Biomolecular Resource Facility Mass Spectrometry Lab (CFL) and the Department of Pharmacology & Toxicology (CFL). EPS was supported by the National Institutes of Health / National Cancer Institute (SPORE Grant No. P50CA127001 , R01CA190121 ). The DanTE software was written by Tom Taverner ( t.taverner@gmail.com ) and Ashoka Polpitiya for the U.S. Department of Energy (PNNL, Richland, WA, USA, http://omics.pnl.gov/software ). Mark R. Emmett, Frederick F. Lang, and Charles A. Conrad are gratefully acknowledged for helpful discussions (CFL).
Publisher Copyright:
© 2015 The Authors.
PY - 2015/9/1
Y1 - 2015/9/1
N2 - Glioma stem-like cells (GSCs) are hypothesized to provide a repository of cells in tumors that can self-replicate and are radio- and chemo-resistant. GSC lines, representing several glioma subtypes, have been isolated and characterized at the transcript level. We sought to characterize 35 GSC lines at the protein level using label-free quantitative proteomics. Resulting relative fold changes were used to drive unsupervised hierarchical clustering for the purpose of classifying the cell lines based on proteomic profiles. Bioinformatics analysis identified synoviolin, serine/arginine-rich splicing factor 2, symplekin, and IL-5 as molecules of interest in progression and/or treatment of glioma.
AB - Glioma stem-like cells (GSCs) are hypothesized to provide a repository of cells in tumors that can self-replicate and are radio- and chemo-resistant. GSC lines, representing several glioma subtypes, have been isolated and characterized at the transcript level. We sought to characterize 35 GSC lines at the protein level using label-free quantitative proteomics. Resulting relative fold changes were used to drive unsupervised hierarchical clustering for the purpose of classifying the cell lines based on proteomic profiles. Bioinformatics analysis identified synoviolin, serine/arginine-rich splicing factor 2, symplekin, and IL-5 as molecules of interest in progression and/or treatment of glioma.
KW - DAVID
KW - Glioblastoma
KW - Glioma stem cells
KW - Ingenuity Pathway Analysis
KW - Label-free quantitative proteomics
KW - Unsupervised hierarchical clustering
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U2 - 10.1016/j.euprot.2015.06.008
DO - 10.1016/j.euprot.2015.06.008
M3 - Article
AN - SCOPUS:84940957712
VL - 8
SP - 85
EP - 93
JO - EuPA Open Proteomics
JF - EuPA Open Proteomics
SN - 2212-9685
ER -