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Statistical detection of quantitative protein biomarkers provides insights into signaling networks deregulated in acute myeloid leukemia
Author(s) -
Elo Laura L.,
Karjalainen Riikka,
Öhman Tiina,
Hintsanen Petteri,
Nyman Tuula A.,
Heckman Caroline A.,
Aittokallio Tero
Publication year - 2014
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.201300460
Subject(s) - myeloid leukemia , transcriptome , biology , proteomics , computational biology , biomarker , gene expression profiling , biomarker discovery , proteome , quantitative proteomics , myeloid , microarray , bioinformatics , gene expression , cancer research , gene , genetics
The increasing coverage and sensitivity of LC‐MS/MS‐based proteomics have expanded its applications in systems medicine. In particular, label‐free quantitation approaches are enabling biomarker discovery in terms of statistical comparison of proteomic profiles across large numbers of clinical samples. However, it still remains poorly understood how much protein markers can add novel insights compared to markers derived from mRNA transcriptomic profiling. Using paired label‐free LC‐MS/MS and gene expression microarray measurements from primary samples of patients with acute myeloid leukemia (AML), we demonstrate here that while the quantitative proteomic and transcriptomic profiles were highly correlated, in general, the marker panels showing statistically significant expression changes across the disease and healthy groups were profoundly different between protein and mRNA levels. In particular, the proteomic assay enabled unique links to known leukemic processes, which were missed when using the transcriptomic profiling alone, as well as identified additional links to metabolic regulators and chromatin remodelers, such as GPX1, fumarate hydratase, and SET oncogene, which have subsequently been evaluated in independent AML samples. Overall, these results highlighted the complementary and informative view obtained from the quantitative LC‐MS/MS approach into the AML deregulated signaling networks.