Identification of gene expression signatures in human leukemia cell lines using Nanostring gene expression profiling platform

Imilia, Ismail and Mohd Nizam, Zahary and Wan Rohani, Wan Taib and Syed Ahmad Tajudin, Tuan Johari and Rosline, Hassan and Keng, W.K. (2018) Identification of gene expression signatures in human leukemia cell lines using Nanostring gene expression profiling platform. In: Human genome meeting 2018, 12-15 Mac 2018, Yokohama, Japan.

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Abstract

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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