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

PURPOSE

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.

EXPERIMENTAL DESIGN

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.

RESULTS

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 comprising 385 cases. Integrated analysis identified a key microRNA regulatory network likely driving the gastric cancer mesenchymal subtype. Three of the microRNAs (miR-200c, miR-200b, and miR-125b) targeting the most genes in the network were significantly associated with survival. Functional experiments demonstrated that miR-200b suppressed ZEB1, augmented E-cadherin, inhibited cell migration, and suppressed tumor growth in a mouse model.

CONCLUSIONS

We have uncovered a key microRNA regulatory network that defines the mesenchymal gastric cancer subtype significantly associated with poor overall survival in gastric cancer.