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Sci Rep ; 10(1): 1224, 2020 01 27.
Artículo en Inglés | MEDLINE | ID: mdl-31988326

RESUMEN

The genetic etiology of childhood cancers still remains largely unknown. It is therefore essential to develop novel strategies to unravel the spectrum of pediatric cancer genes. Statistical network modeling techniques have emerged as powerful methodologies for enabling the inference of gene-disease relationship and have been performed on adult but not pediatric cancers. We performed a deep multi-layer understanding of pan-cancer transcriptome data selected from the Treehouse Childhood Cancer Initiative through a co-expression network analysis. We identified six modules strongly associated with pediatric tumor histotypes that were functionally linked to developmental processes. Topological analyses highlighted that pediatric cancer predisposition genes and potential therapeutic targets were central regulators of cancer-histotype specific modules. A module was related to multiple pediatric malignancies with functions involved in DNA repair and cell cycle regulation. This canonical oncogenic module gathered most of the childhood cancer predisposition genes and clinically actionable genes. In pediatric acute leukemias, the driver genes were co-expressed in a module related to epigenetic and post-transcriptional processes, suggesting a critical role of these pathways in the progression of hematologic malignancies. This integrative pan-cancer study provides a thorough characterization of pediatric tumor-associated modules and paves the way for investigating novel candidate genes involved in childhood tumorigenesis.


Asunto(s)
Perfilación de la Expresión Génica/métodos , Redes Reguladoras de Genes/genética , Neoplasias/genética , Biomarcadores de Tumor/genética , Niño , Preescolar , Biología Computacional/métodos , Simulación por Computador , Bases de Datos Genéticas , Femenino , Expresión Génica/genética , Regulación de la Expresión Génica/genética , Regulación Neoplásica de la Expresión Génica/genética , Predisposición Genética a la Enfermedad/genética , Genómica/métodos , Humanos , Masculino , Modelos Estadísticos , Neoplasias/etiología , Mapeo de Interacción de Proteínas/métodos , Mapas de Interacción de Proteínas/genética , Mapas de Interacción de Proteínas/fisiología , Integración de Sistemas , Transcriptoma/genética
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