Identification of gene expression signatures in human leukemia cell lines using Nanostring gene expression profiling platform
Background Gene expression profiles have been examined extensively in diseases including hematological malignancies. Although the diagnostic tests that sub classified leukemia have been improved, leukemia patients occasionally exhibit different responses to treatment. In order to find more precis...
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Main Authors: | , , , , , |
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Format: | Conference or Workshop Item |
Language: | English |
Published: |
2018
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Subjects: | |
Online Access: | http://eprints.unisza.edu.my/1678/1/FH03-FSK-18-14422.pdf http://eprints.unisza.edu.my/1678/ |
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Summary: | Background
Gene expression profiles have been examined extensively in diseases including hematological malignancies. Although the diagnostic tests that
sub classified leukemia have been improved, leukemia patients occasionally exhibit different responses to treatment. In order to find more
precise molecular markers, we performed differential gene expression
profiling in human acute myeloid leukemia (AML) and chronic myeloid
leukemia (CML) cell lines (HL60 and K562, respectively) using Nanostring
nCounter® MAX Analysis System (Nanostring Technologies, Seattle, WA).
Materials and methods
Total RNA was extracted from HL60 and K562 cell lines using innuPREP RNA mini kit (AJ Innuscreen GmbH, Germany) according to the
manufacturer’s instructions. The RNA quality was assessed on the
2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA) and the concentration was determined with a Nanodrop spectrophotometer
(ND-1000, Thermo Scientific, Wilmington, MA, USA). We performed gene expression profiling of 230 human cancer-related genes with
six internal reference genes using nCounter GX Human Cancer Reference Kit (NanoString Technologies). A total of 100 ng of RNA for each
sample was prepared as per the manufacturer’s instructions under
the high sensitivity protocol. Normalization and subsequent data processing were performed by using the nSolverTM Analysis Software
v2.6 (Nanostring Technologies). Differentially expressed mRNAs were
identified through fold change (≥2.0) and p values < 0.05 filtering.
Results
We identified distinctive gene expression patterns in K562 and HL60
cell lines. The most significantly up regulated genes in K562 cells included FGFR3, WT1, CCNA2, FGF2 and HSP90AB1 while CSF3R, BCL2A1,
TNFSF10, AKT1 and GNAS were most significantly down regulated. In
HL 60 cells, WT1, CCNA2, PRKAR1A, MYB and CHEK1 were the most
significantly up regulated while FOS, AKT1, GNAS, TP53 and IL1B were
the most significantly down regulated genes. Several genes that
were up regulated in K562 were found to be down regulated in
HL60 such as FGF2, GATA1, IL6 and PIM1. FGFR1 and SPI1 were significantly up regulated in HL60 but were found to be significantly down
regulated in K562 cell line.
Conclusions
In conclusion, our results suggest that gene expression profiling identified FGF2, GATA1, IL6, PIM1, FGFR1 and SPI1 to be differentially
expressed between AML and CML cells. These findings may also help
to assess future markers in developing therapies targeting mRNA. |
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