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Oral cancer ranks among the most common malignancies within the head and neck region; however, its etiology remains inadequately understood despite substantial research advances in recent years. Many studies highlight the regulatory role of circular RNAs (circRNAs) in human cancers, suggesting their potential as cancer biomarkers. However, their specific mechanisms in oral cancer are not well understood. This study analyzed circRNAs expression in oral cancer, identifying circRNF13 (circbaseID: has_circ_0006801) as having elevated expression in oral cancer cells and tissues. Our study demonstrated that circRNF13 is correlated with increased tumor grade and stage in oral cancer. Results from both in vitro and in vivo experiments indicated that circRNF13 enhances cancer cell proliferation and tumor growth, while concurrently diminishing tumor sensitivity to cisplatin. Mechanistically, circRNF13 interacts with the m6A "reader" protein IGF2BP1, inhibiting its ubiquitin-mediated degradation and promoting its phase separation formation. Subsequently, circRNF13 augments the stability of ITGB1 mRNA via IGF2BP1 in a manner dependent on m6A modification. The m6A modification of ITGB1 mRNA is modulated by the phase separation of IGF2BP1, thereby promoting the malignant progression of oral cancer cells. This evidence positions circRNF13 as a crucial regulatory molecule in the pathogenesis of oral cancer and suggests its potential as a therapeutic target. This discovery enriches our understanding of the mechanistic role of circRNAs. [ABSTRACT FROM AUTHOR]

Diffusion models have been widely used to describe the stochastic dynamics of the underlying economic variables. Renò (2008) introduced a nonparametric estimator of the volatility function, which is based on the estimation of quadratic variation between observations by means of realized variance. However, they may be misleading when one uses intraday data to implement directly the estimator, because intraday data display microstructure effects that could seriously distort the estimation. To filter out the impact of microstructure noise on the estimation of the volatility function, in this paper we propose an improved estimator when there is microstructure noise in the observed price. Also, we show that the proposed estimator has the same asymptotic properties as the Renò estimator when the step of discretization inclines to zero. Some simulations and empirical applications on Shanghai Stock Exchange data from March 3, 2002 to December 31, 2008 are used to illustrate the finite sample performance of the proposed estimator. [ABSTRACT FROM AUTHOR]

Bivariate spatial process is a natural tool for the assessments of environmental justice. Integrating the skewness arising in spatial data sets remains a challenge. While in terms of which, classical bivariate spatial skewness analysis receives relative little attention. As an attempt, this article provides a fully hierarchical bivariate approach for spatial modelling, using a Bayesian framework implemented via Markov chain Monte Carlo (MCMC) methods, called the bivariate double zero expectile normal with measurement error (BDZEXPNM). The BDZEXPNM modelling is derived by considering two variables simultaneously, where the covariance parameters representing the marginal and cross‐spatial dependence structure are measured. The posterior performance of BDZEXPNM is probed by leveraging multivariate spatial analysis. In addition, MCMC methods allows a straightforward interpretation of parameters. Simulation studies validate our method as well as a real data example. [ABSTRACT FROM AUTHOR]

Cancer cells prefer glycolysis to support their proliferation. Our previous studies have shown that the long palate, lung, and nasal epithelial cell clone 1 (LPLUNC1) can upregulate prohibitin 1 (PHB1) expression to inhibit the proliferation of nasopharyngeal carcinoma (NPC) cells. Given that PHB1 is an important regulator of cell energy metabolism, we explored whether and how LPLUNC1 regulated glucose glycolysis in NPC cells. LPLUNC1 or PHB1 overexpression decreased glycolysis and increased oxidative phosphorylation (OXPHOS)‐related protein expression in NPC cells, promoting phosphorylated PHB1 nuclear translocation through 14‐3‐3σ. LPLUNC1 overexpression also increased p53 but decreased c‐Myc expression in NPC cells, which were crucial for the decrease in glycolysis and increase in OXPHOS‐related protein expression induced by LPLUNC1 overexpression. Finally, we found that treatment with all‐trans retinoic acid (ATRA) reduced the viability and clonogenicity of NPC cells, decreased glycolysis, and increased OXPHOS‐related protein expression by enhancing LPLUNC1 expression in NPC cells. Therefore, the LPLUNC1‐PHB1‐p53/c‐Myc axis decreased glycolysis in NPC cells, and ATRA upregulated LPLUNC1 expression, ATRA maybe a promising drug for the treatment of NPC. [ABSTRACT FROM AUTHOR]

Metabolic reprogramming is the survival rule of tumor cells, and tumor cells can meet their high metabolic requirements by changing the energy metabolism mode. Metabolic reprogramming of tumor cells is an important biochemical basis of tumor malignant phenotypes. Ras‐related C3 botulinum toxin substrate 1 (Rac1) is abnormally expressed in a variety of tumors and plays an important role in the proliferation, invasion, and migration of tumor cells. However, the role of Rac1 in tumor metabolic reprogramming is still unclear. Herein, we revealed that Rac1 was highly expressed in colon cancer tissues and cell lines. Rac1 promotes the proliferation, migration, and invasion of colon cancer cells by upregulating SOX9, which as a transcription factor can directly bind to the promoters of HK2 and G6PD genes and regulate their transcriptional activity. Rac1 upregulates the expression of SOX9 through the PI3K/AKT signaling pathway. Moreover, Rac1 can promote glycolysis and the activation of the pentose phosphate pathway in colon cancer cells by mediating the axis of SOX9/HK2/G6PD. These findings reveal novel regulatory axes involving Rac1/SOX9/HK2/G6PD in the development and progression of colon cancer, providing novel promising therapeutic targets. [ABSTRACT FROM AUTHOR]

This article tests volatility jumps based on the high frequency data. Under the null hypothesis that the volatility process is a continuous semimartingale, our test statistic converges to a normal distribution, and under the alternative hypothesis where the volatility has jumps, the statistic diverges to infinity. Compared to the test statistic of Bibinger et al. (Bibinger et al. (2017). Annals of Statistics 45, 1542–1578), our proposed statistic diverges to infinity at a faster rate, and has a better power. Simulation studies confirm the theoretical results, and an empirical analysis shows that some real financial data possess volatility jumps. [ABSTRACT FROM AUTHOR]