Gene Expression Music Algorithm-Based Characterization of the Ewing Sarcoma Stem Cell Signature.

Martin Sebastian Staege

doi:10.1155/2016/7674824

Abstract

Gene Expression Music Algorithm (GEMusicA) is a method for the transformation of DNA microarray data into melodies that can be used for the characterization of differentially expressed genes. Using this method we compared gene expression profiles from endothelial cells (EC), hematopoietic stem cells, neuronal stem cells, embryonic stem cells (ESC), and mesenchymal stem cells (MSC) and defined a set of genes that can discriminate between the different stem cell types. We analyzed the behavior of public microarray data sets from Ewing sarcoma (“Ewing family tumors,” EFT) cell lines and biopsies in GEMusicA after prefiltering DNA microarray data for the probe sets from the stem cell signature. Our results demonstrate that individual Ewing sarcoma cell lines have a high similarity to ESC or EC. Ewing sarcoma cell lines with inhibited Ewing sarcoma breakpoint region 1-Friend leukemia virus integration 1 (EWSR1-FLI1) oncogene retained the similarity to ESC and EC. However, correlation coefficients between GEMusicA-processed expression data between EFT and ESC decreased whereas correlation coefficients between EFT and EC as well as between EFT and MSC increased after knockdown of EWSR1-FLI1. Our data support the concept of EFT being derived from cells with features of embryonic and endothelial cells.

Highlights

The stem cell phenotype of cancer cells can be the consequence of the malignant transformation that led to de novo acquisition of a stem cell-like phenotype or this phenotype can be reminiscent of a normal stem cell that serves as the cell of origin for the cancer cells
The algorithm includes a procedure that filters probe sets with high variance of the signal intensities. These probe sets are likely to have a higher information content than probe sets with low variability [14]. We used this approach for the characterization of tumor specific gene expression profiles and demonstrated that the generated melodies can be used for discrimination between different tumor entities, for example, neuroblastoma and Ewing sarcoma cell lines [14]
We asked whether this method can be used for the definition of gene expression signatures that are specific for certain stem cell populations

Summary

Introduction

The stem cell phenotype of cancer cells can be the consequence of the malignant transformation that led to de novo acquisition of a stem cell-like phenotype or this phenotype can be reminiscent of a normal stem cell that serves as the cell of origin for the cancer cells In both cases the gene expression profile of the cancer cells will show similarities to the gene expression profile of stem cells. Gene expression data suggest a relationship between EFT and endothelial cells, neuroectodermal cells, or mesenchymal stem cells [1,2,3,4]. Expression of the EFT specific EWSR1-FLI1 oncogene in neuroblastoma cells can induce an EFT-like phenotype [7]. Stem Cells International neuronal phenotype might be partially a consequence of oncogene expression [8]. In the present paper we used this approach for the definition of a stem cell signature and tested the behavior of this signature in EFT microarray data

Materials and Methods

Results and Discussion

Conclusions