RESUMEN
Stau1 is a pluripotent RNA-binding protein that is responsible for the post-transcriptional regulation of a multitude of transcripts. Here, we observed that lung cancer patients with a high Stau1 expression have a longer recurrence free survival. Strikingly, Stau1 did not impair cell proliferation in vitro, but rather cell migration and cell adhesion. In vivo, Stau1 depletion favored tumor progression and metastases development. In addition, Stau1 depletion strongly impaired vessel maturation. Among a panel of candidate genes, we specifically identified the mRNA encoding the cell adhesion molecule Thrombospondin 1 (THBS1) as a new target for Staufen-mediated mRNA decay. Altogether, our results suggest that regulation of THBS1 expression by Stau1 may be a key process involved in lung cancer progression.
Asunto(s)
Carcinoma de Pulmón de Células no Pequeñas/genética , Neoplasias Pulmonares/genética , Estabilidad del ARN/genética , ARN Mensajero/genética , Trombospondina 1/genética , Animales , Adhesión Celular/genética , Línea Celular Tumoral , Movimiento Celular/genética , Proteínas del Citoesqueleto , Progresión de la Enfermedad , Femenino , Regulación de la Expresión Génica/genética , Humanos , Ratones , Ratones Desnudos , Estudios Prospectivos , Proteínas de Unión al ARN/genéticaRESUMEN
Although capillary electrophoresis coupled to mass spectrometry (CE-MS) has potential application in the field of metabolite profiling, very few studies actually used CE-MS to identify clinically useful body fluid metabolites. Here we present an optimized CE-MS setup and analysis pipeline to reproducibly explore the metabolite content of urine. We show that the use of a beveled tip capillary improves the sensitivity of detection over a flat tip. We also present a novel normalization procedure based on the use of endogenous stable urinary metabolites identified in the combined metabolome of 75 different urine samples from healthy and diseased individuals. This method allows a highly reproducible comparison of the same sample analyzed nearly 130 times over a range of 4 years. To demonstrate the use of this pipeline in clinical research we compared the urinary metabolome of 34 newborns with ureteropelvic junction (UPJ) obstruction and 15 healthy newborns. We identified 32 features with differential urinary abundance. Combination of the 32 compounds in a SVM classifier predicted with 76% sensitivity and 86% specificity UPJ obstruction in a separate validation cohort of 24 individuals. Thus, this study demonstrates the feasibility to use CE-MS as a tool for the identification of clinically relevant urinary metabolites.