Emerging evidence suggested that circular RNAs (circRNAs) play critical roles in cervical cancer (CC) progression. However, the roles and molecular mechanisms of hsa_circ_0007364 in the tumorigenesis of CC remain unclear. In the present study, we used bioinformatics analysis and a series of experimental analysis to characterize a novel circRNA, hsa_circ_0007364 was upregulated and associated with advanced clinical features in CC patients. Hsa_circ_0007364 inhibition notably suppressed the proliferation and invasion abilities of CC cells in vitro and reduced tumor growth in vivo. Moreover, hsa_circ_0007364 was uncovered to sponge miR-101-5p. Additionally, methionine adenosyltransferase II alpha (MAT2A) was verified as a target gene of miR-101-5p, and its downregulation reversed the inhibitory effects of hsa_circ_0007364 knockdown on CC progression. Therefore, we suggested that hsa_circ_0007364 might serve as an oncogenic circRNA in CC progression by regulating the miR-101-5p/MAT2A axis, which provides a potential therapeutic target to the treatment. Research highlights (1) hsa_circ_0007364 was upregulated in CC (2) hsa_circ_0007364 promoted CC cell progression (3) hsa_circ_0007364/miR-101-5p/MAT2A axis in CC ARTICLE HISTORY

While Bayesian functional mixed models have been shown effective to model functional data with various complex structures, their application to extremely high-dimensional data is limited due to computational challenges involved in posterior sampling. We introduce a new computational framework that enables ultra-fast approximate inference for high-dimensional data in functional form. This framework adopts parsimonious basis to represent functional observations, which facilitates efficient compression and parallel computing in basis space. Instead of performing expensive Markov chain Monte Carlo sampling, we approximate the posterior distribution using variational Bayes and adopt a fast iterative algorithm to estimate parameters of the approximate distribution. Our approach facilitates a fast multiple testing procedure in basis space, which can be used to identify significant local regions that reflect differences across groups of samples. We perform two simulation studies to assess the performance of approximate inference, and demonstrate applications of the proposed approach by using a proteomic mass spectrometry dataset and a brain imaging dataset. Supplementary materials are available online.