Gene Expression Music Algorithm-Based Characterization of the Ewing Sarcoma Stem Cell Signature
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...
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Stem Cells International |
| Online Access: | http://dx.doi.org/10.1155/2016/7674824 |
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| _version_ | 1849304776320221184 |
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| author | Martin Sebastian Staege |
| author_facet | Martin Sebastian Staege |
| author_sort | Martin Sebastian Staege |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3c3846fad4034d3b9f8cef34b0cfab36 |
| institution | Kabale University |
| issn | 1687-966X 1687-9678 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Stem Cells International |
| spelling | doaj-art-3c3846fad4034d3b9f8cef34b0cfab362025-08-20T03:55:37ZengWileyStem Cells International1687-966X1687-96782016-01-01201610.1155/2016/76748247674824Gene Expression Music Algorithm-Based Characterization of the Ewing Sarcoma Stem Cell SignatureMartin Sebastian Staege0University Clinic and Polyclinic for Child and Adolescent Medicine, Martin Luther University of Halle-Wittenberg, 06120 Halle (Saale), GermanyGene 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.http://dx.doi.org/10.1155/2016/7674824 |
| spellingShingle | Martin Sebastian Staege Gene Expression Music Algorithm-Based Characterization of the Ewing Sarcoma Stem Cell Signature Stem Cells International |
| title | Gene Expression Music Algorithm-Based Characterization of the Ewing Sarcoma Stem Cell Signature |
| title_full | Gene Expression Music Algorithm-Based Characterization of the Ewing Sarcoma Stem Cell Signature |
| title_fullStr | Gene Expression Music Algorithm-Based Characterization of the Ewing Sarcoma Stem Cell Signature |
| title_full_unstemmed | Gene Expression Music Algorithm-Based Characterization of the Ewing Sarcoma Stem Cell Signature |
| title_short | Gene Expression Music Algorithm-Based Characterization of the Ewing Sarcoma Stem Cell Signature |
| title_sort | gene expression music algorithm based characterization of the ewing sarcoma stem cell signature |
| url | http://dx.doi.org/10.1155/2016/7674824 |
| work_keys_str_mv | AT martinsebastianstaege geneexpressionmusicalgorithmbasedcharacterizationoftheewingsarcomastemcellsignature |