Background Gastric cancer (GC) is one of the most severe cancers worldwide, particularly in China. Circular RNA (circRNA) plays an essential role in GC. Hsa_circ_0000285 regulates the progression of several cancers. However, its role in GC has not been reported. This study elucidated the molecular mechanism and role of hsa_circ_0000285 in GC progression. Methods GC cells were transfected with silencers of hsa_circ_0000285 and fibronectin 1 ( FN1 ), an inhibitor of miR‐1278, and their negative controls (NC). Mice were injected with short hairpin (sh) RNAs targeting hsa_circ_0000285 or NC. The expression levels of hsa_circ_0000285 , miR‐1278, and FN1 were assessed using western blotting and reverse transcription quantitative real‐time polymerase chain reaction (qRT‐PCR). Several assays were used to evaluate cell proliferation, invasion, and apoptosis. Tumor burden was also analyzed. The interactions between miR‐1278, hsa_circ_0000285 , and FN1 were ascertained using dual‐luciferase reporter assays. An RNA immunoprecipitation (RIP) assay was used to assess the enrichment of hsa_circ_0000285 and miR‐1278 in GC. Results Hsa_circ_0000285 was significantly overexpressed in the GC tissues. Silencing hsa_circ_0000285 inhibited cell proliferation and invasion, promoted apoptosis, and inhibited tumor development. Hsa_circ_0000285 sponged miR‐1278. Inhibition of miR‐1278 in vitro reversed the effects of hsa_circ_0000285 silencing on GC progression. MiR‐1278 targeted FN1 , and silencing FN1 neutralized the effects of miR‐1278 inhibitors on GC progression. Conclusions The hsa_circ_0000285 /miR‐1278/ FN1 axis regulated GC progression. In addition, it may serve as a potential therapeutic biomarker for GC.

Background The role(s) of epigenetic reprogramming in gastric cancer (GC) remain obscure. This study was designed to identify methylated gene markers with prognostic potential for GC. Methods Five datasets containing gene expression and methylation profiles from GC samples were collected from the GEO database, and subjected to meta-analysis. All five datasets were subjected to quality control and then differentially expressed genes (DEGs) and differentially expressed methylation genes (DEMGs) were selected using MetaDE. Correlations between gene expression and methylation status were analysed using Pearson coefficient correlation. Then, enrichment analyses were conducted to identify signature genes that were significantly different at both the gene expression and methylation levels. Cox regression analyses were performed to identify clinical factors and these were combined with the signature genes to create a prognosis-related predictive model. This model was then evaluated for predictive accuracy and then validated using a validation dataset. Results This study identified 1565 DEGs and 3754 DEMGs in total. Of these, 369 were differentially expressed at both the gene and methylation levels. We identified 12 signature genes including VEGFC, FBP1, NR3C1, NFE2L2, and DFNA5 which were combined with the clinical data to produce a novel prognostic model for GC. This model could effectively split GC patients into two groups, high- and low-risk with these observations being confirmed in the validation dataset. Conclusion The differential methylation of the 12 signature genes, including VEGFC, FBP1, NR3C1, NFE2L2, and DFNA5, identified in this study may help to produce a functional predictive model for evaluating GC prognosis in clinical samples.