Integrated microRNA network analyses identify a poor-prognosis subtype of gastric cancer characterized by the miR-200 family

Clinical Cancer Research : an Official Journal of the American Association for Cancer Research
Fengju SongKexin Chen

Abstract

Our aim was to investigate whether microRNAs can predict the clinical outcome of patients with gastric cancer. We used integrated analysis of microRNA and mRNA expression profiles to identify gastric cancer microRNA subtypes and their underlying regulatory scenarios. MicroRNA-based gastric cancer subtypes were identified by consensus clustering analysis of microRNA profiles of 90 gastric cancer tissues. Activated pathways in the subtypes were identified by gene expression profiles. Further integrated analysis was conducted to model a microRNA regulatory network for each subtype. RNA and protein expression were analyzed by RT-PCR and tissue microarray, respectively, in a cohort of 385 gastric cancer cases (including the 90 cases for profiling) to validate the key microRNAs and targets in the network. Both in vitro and in vivo experiments were carried out to further validate the findings. MicroRNA profiles of 90 gastric cancer cases identified two microRNA subtypes significantly associated with survival. The poor-prognosis gastric cancer microRNA subtype was characterized by overexpression of epithelial-to-mesenchymal transition (EMT) markers. This gastric cancer "mesenchymal subtype" was further validated in a patient cohort com...Continue Reading

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Mar 14, 2014·Diagnostic Pathology·Xiaowen LiuYanong Wang
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