title: Full transcriptome analysis of rhabdomyosarcoma, normal, and fetal skeletal muscle: statistical comparison of multiple SAGE libraries.
abstract: Rhabdomyosarcoma (RMS) is the most frequent soft tissue sarcoma in children. Improved treatment strategies have increased overall survival, but the response of approximately one-third of the patients is still poor. To increase the knowledge of RMS pathogenesis, we performed the first full transcriptome analysis of RMS using serial analysis of gene expression (SAGE). With a G-test for the simultaneous comparison of subsets of SAGE libraries of normal skeletal muscle, embryonal (ERMS) and alveolar (ARMS) RMS, we identified 251 differentially expressed genes. A literature-mining procedure demonstrated that 158 of these genes have not previously been associated with RMS or normal muscle. Gene Ontology (GO) analysis assigned 198 of the 251 genes to muscle-specific classes, including those involved in normal myogenic development, as well as tumor-related classes. Prominent GO classes were those associated with proliferation and actin reorganization, which are processes that play roles during early muscle development, muscle function, and tumor progression. Using custom microarrays, we confirmed the (up- or down-) regulation of 80% of 98 differentially expressed genes. Another SAGE library of 19- to 22-week-old fetal skeletal muscle was compared with the RMS and normal muscle transcriptomes. Cluster analysis showed that the RMS and fetal muscle SAGE libraries formed one cluster distinct from normal muscle samples. Moreover, the expression profile of 86% of the differentially expressed genes between normal muscle and RMS was highly similar in fetal muscle and RMS. In conclusion, the G-test is a robust tool for analyzing groups of SAGE libraries and correctly identifies genes marking the difference between fully differentiated skeletal muscle and RMS. This study not only substantiates the close association between embryonic myogenesis and RMS development but also provides a rich source of candidate genes to further elucidate the etiology of RMS or to identify diagnostic and/or prognostic markers.
authors.affiliation: Department of Neurogenetics, Academic Medical Centre, University of Amsterdam, Meibergdreef 9, 1105 AZ Amsterdam, The Netherlands. g.j.schaaf@amc.uva.nl
authors.forename: Gerben J
authors.initials: GJ
authors.lastname: Schaaf
authors.forename: Jan M
authors.initials: JM
authors.lastname: Ruijter
authors.forename: Fred
authors.initials: F
authors.lastname: van Ruissen
authors.forename: Danny A
authors.initials: DA
authors.lastname: Zwijnenburg
authors.forename: Raymond
authors.initials: R
authors.lastname: Waaijer
authors.forename: Linda J
authors.initials: LJ
authors.lastname: Valentijn
authors.forename: Jennifer
authors.initials: J
authors.lastname: Benit-Deekman
authors.forename: Antoine H C
authors.initials: AH
authors.lastname: van Kampen
authors.forename: Frank
authors.initials: F
authors.lastname: Baas
authors.forename: Marcel
authors.initials: M
authors.lastname: Kool
mesh_terms: D002448:Cell Adhesion; D015870:Gene Expression; D020869:Gene Expression Profiling; D015723:Gene Library; D005947:Glucose / Q000378:metabolism; D006801:Humans; D016013:Likelihood Functions; D018482:Muscle, Skeletal / Q000737:chemistry* / Q000196:embryology*; D020411:Oligonucleotide Array Sequence Analysis; D012313:RNA / Q000378:metabolism; D012208:Rhabdomyosarcoma / Q000235:genetics*
chemical_list: D012313:RNA; D005947:Glucose