Background
Accumulating evidence shows that, the dysregulation of circular RNAs (circRNAs) is associated with the progression of multiple malignancies. But, the underlying mechanisms by which has_circ_0032627 (circDLST) contributed to gastric cancer (GC) remain undocumented.
Methods
The expression and cellular localization of circDLST and its association with clinicopathological characteristics and prognosis in patients with GC was analysed by using fluorescence in situ hybridization. Gain- and loss-of-function experiments as well as a subcutaneous xenograft tumor model and a liver metastasis model from orthotopic implantation of GC tissues were conducted to assess the role of circDLST in GC cells. CircDLST specific binding with miR-502-5p was confirmed by dual luciferase gene report, RNA immunoprecipitation (RIP) assays and RIP-miRNA expression profiling. qRT-PCR and Western blot analysis was used to detect the effects of circDLST on miR-502-5p-mediated NRAS/MEK1/ERK1/2 signaling in GC cells.
Results
The expression levels of circDLST were dramatically elevated in GC tissues as compared with the adjacent normal tissues, and acted as an independent prognostic factor of poor survival in patients with GC. Knockdown of circDLST inhibited the cell viability, colony formation, DNA synthesis, cell invasion and liver metastasis in vitro and in vivo, whereas overexpression of circDLST had the opposite effects. Furthermore, circDLST was co-localized with miR-502-5p in the cytoplasm of GC cells, and acted as a sponge of miR-502-3p in GC cells, which abrogated the tumor promoting effects of circDLST by inactivating the NRAS/MEK1/ERK1/2 signaling in GC cells.
Conclusion
CircDLST promotes the tumorigenesis and metastasis of GC cells by sponging miR-502-5p to activate the NRAS/MEK1/ERK1/2 signaling.
Electronic supplementary material
The online version of this article (10.1186/s12943-019-1015-1) contains supplementary material, which is available to authorized users.
Significance
Breast cancer (BrCa) is the most common cancer worldwide, and high-performance metabolic analysis is emerging in diagnosis and prognosis of BrCa. Here, we used nanoparticle-enhanced laser desorption/ionization mass spectrometry to record serum metabolic fingerprints of BrCa in seconds, achieving high reproducibility and low consumption of direct serum detection. Our analytical method, combined with the aid of machine learning algorithms, was demonstrated to provide high diagnostic efficiency with accuracy of 88.8% and desirable prognostic prediction (
P
< 0.005). Furthermore, seven metabolic biomarkers differentially enriched in BrCa serum and their related pathways were identified. Together, our findings provide a tool to characterize BrCa and highlight certain metabolic signatures as potential diagnostic and prognostic factors of diseases including but not limited to BrCa.
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