PRKAR2A‐derived circular RNAs promote the malignant transformation of colitis and distinguish patients with colitis‐associated colorectal cancer

Abstract

Abstract Background Emerging studies have proved that colonic inflammation caused by refractory inflammatory bowel disease (IBD) can initiate the colitis‐associated cancer (CAC), but the transition from inflammation to carcinoma is still largely unknown. Methods In this study, mouse colitis and CAC models were established, and the RNA‐seq by circRNA microarray was employed to identify the differentially expressed circRNAs and mRNAs in different comparisons (DSS vs. NC and AOM/DSS vs. DSS). The bioinformatics analyses were used to search the common characteristics in mouse colitis and CAC. Results The K‐means clustering algorithm packaged these differential expressed circRNAs into subgroup analysis, and the data strongly implied that mmu_circ_0001109 closely correlated to the pro‐inflammatory signals, while mmu_circ_0001845 was significantly associated with the Wnt signalling pathway. Our subsequent data in vivo and in vitro confirmed that mmu_circ_0001109 could exacerbate the colitis by up‐regulating the Jak‐STAT3 and NF‐kappa B signalling pathways, and mmu_circ_0001845 promoted the CAC transformation through the Wnt signalling pathway. By RNA blasting between mice and humans, the human RTEL1‐ and PRKAR2A‐derived circRNAs, which might be considered as homeotic circRNAs of mmu_circ_0001109 and mmu_circ_0001845, respectively, were identified. The clinical data revealed that RTEL1‐derived circRNAs had no clinical significance in human IBD and CAC. However, three PRKAR2A‐derived circRNAs, which had the high RNA similarities to mmu_circ_0001845, were remarkably up‐regulated in CAC tissue samples and promoted the transition from colitis to CAC. Conclusions Our results suggested that these human PRKAR2A‐derived circRNAs could be novel candidates for distinguishing CAC patients and predicted the prognosis of CAC.

Keywords

• circular RNA, colitis‐associated colorectal cancer, PRKAR2A, RNA sequencing
• K‐means clustering algorithm