Circular RNA circ_SKA3 enhances gastric cancer development by targeting miR-520h.

PURPOSE: To explore the mechanisms of action of circ_SKA3 in gastric cancer (GC), which are still not fully understood. METHODS: Subcellular localization assay was used to analyze the localization of circ_SKA3, and Actinomycin D assay was applied to confirm the stability of circ_SKA3. The levels of circ_SKA3, microRNA (miR)-520h, and cell division cycle 42 (CDC42) mRNA were gauged by quantitative real-time polymerase chain reaction (qRT-PCR). The protein levels of CDC42 and proliferating cell nuclear antigen (PCNA) were assessed by western blot. Cell proliferation, colony formation, cell cycle distribution, apoptosis, migration, and invasion were detected by 3-[4,5-dimethylthiazol-2-yl]-2, 5-diphenyltetrazolium bromide (MTT), 5-Ethynyl-2'-Deoxyuridine (EdU) incorporation, colony formation, flow cytometry, and transwell assays, respectively. Directed relationship between miR-520h and circ_SKA3 or CDC42 was verified by a dual-luciferase reporter assay. Mouse xenograft experiments were used to elucidate the impact of circ_SKA3 in vivo. RESULTS: Overexpression of circ_SKA3 was validated in GC tissues and cells. The down-regulation of circ_SKA3 suppressed proliferation, cell cycle progression, colony formation, migration, invasion, and promoted cell apoptosis in vitro, as well as weakening tumor growth in vivo. Circ_SKA3 directly bound to miR-520h, and circ_SKA3 regulated CDC42 expression through miR-520h. Circ_SKA3 exerted regulatory effects on GC cell behaviors by inhibiting miR-520h. Furthermore, CDC42 was a functional target of miR-520h in regulating GC cell behaviors. CONCLUSION: Our findings established a strong molecular mechanism, the miR-520h/CDC42 axis, at least in part, for the oncogenic role of circ_SKA3 in GC.

Effects of harmaline on cell growth of human liver cancer through the p53/p21 and Fas/FasL signaling pathways.

The effects of harmaline on the viability and apoptosis of human liver carcinoma were investigated in vitro. HepG2 cells were treated with harmaline (0-10 µM), and the proliferation and apoptosis of HepG2 cells were investigated using an MTT assay and flow cytometry, respectively. The protein expression of cellular tumor antigen p53 (p53), cyclin-dependent kinase inhibitor 1 (p21), tumor necrosis factor receptor superfamily member 6 (Fas), Fas ligand (FasL) and caspase-8 was subsequently measured using western blotting. In addition, an ELISA was used to analyze caspase-8/3 activity. Harmaline significantly increased p53, p21, Fas and FasL protein expression in HepG2 cells. Additionally, treatment with harmaline significantly increased the expression of caspase-8 and caspase-8/3 activity. The results from the present study suggest that harmaline suppresses the viability, but induces the apoptosis, of human liver carcinoma cells through upregulation of the p53/p21 and Fas/FasL signaling pathways.

Common and rare exonic MUC5B variants associated with type 2 diabetes in Han Chinese.

Genome-wide association studies have identified over one hundred common genetic risk variants associated with type 2 diabetes (T2D). However, most of the heritability of T2D has not been accounted for. In this study, we investigated the contribution of rare and common variants to T2D susceptibility by analyzing exome array data in 1,908 Han Chinese genotyped with Affymetrix Axiom® Exome Genotyping Arrays. Based on the joint common and rare variants analysis of 57,704 autosomal SNPs within 12,244 genes using Sequence Kernel Association Tests (SKAT), we identified significant associations between T2D and 25 variants (9 rare and 16 common) in MUC5B, p-value 1.01×10-14. This finding was replicated (p = 0.0463) in an independent sample that included 10,401 unrelated individuals. Sixty-six of 1,553 possible haplotypes based on 25 SNPs within MUC5B showed significant association with T2D (Bonferroni corrected p values < 3.2×10-5). The expression level of MUC5B is significantly higher in pancreatic tissues of persons with T2D compared to those without T2D (p-value = 5×10-5). Our findings suggest that dysregulated MUC5B expression may be involved in the pathogenesis of T2D. As a strong candidate gene for T2D, MUC5B may play an important role in the mechanisms underlying T2D etiology and its complications.

An Improved F(st) Estimator.

The fixation index F(st) plays a central role in ecological and evolutionary genetic studies. The estimators of Wright ([Formula: see text]), Weir and Cockerham ([Formula: see text]), and Hudson et al. ([Formula: see text]) are widely used to measure genetic differences among different populations, but all have limitations. We propose a minimum variance estimator [Formula: see text] using [Formula: see text] and [Formula: see text]. We tested [Formula: see text] in simulations and applied it to 120 unrelated East African individuals from Ethiopia and 11 subpopulations in HapMap 3 with 464,642 SNPs. Our simulation study showed that [Formula: see text] has smaller bias than [Formula: see text] for small sample sizes and smaller bias than [Formula: see text] for large sample sizes. Also, [Formula: see text] has smaller variance than [Formula: see text] for small Fst values and smaller variance than [Formula: see text] for large F(st) values. We demonstrated that approximately 30 subpopulations and 30 individuals per subpopulation are required in order to accurately estimate F(st).

Simultaneous Analysis of Common and Rare Variants in Complex Traits: Application to SNPs (SCARVAsnp).

Advances in technology and reduced costs are facilitating large-scale sequencing of genes and exomes as well as entire genomes. Recently, we described an approach based on haplotypes called SCARVA1 that enables the simultaneous analysis of the association between rare and common variants in disease etiology. Here, we describe an extension of SCARVA that evaluates individual markers instead of haplotypes. This modified method (SCARVAsnp) is implemented in four stages. First, all common variants in a pre-specified region (eg, gene) are evaluated individually. Second, a union procedure is used to combined all rare variants (RVs) in the index region, and the ratio of the log likelihood with one RV excluded to the log likelihood of a model with all the collapsed RVs is calculated. On the basis of previously-reported simulation studies,1 a likelihood ratio ≥1.3 is considered statistically significant. Third, the direction of the association of the removed RV is determined by evaluating the change in λ values with the inclusion and exclusion of that RV. Lastly, significant common and rare variants, along with covariates, are included in a final regression model to evaluate the association between the trait and variants in that region. We apply simulated and real data sets to show that the method is simple to use, computationally effcient, and that it can accurately identify both common and rare risk variants. This method overcomes several limitations of existing methods. For example, SCARVAsnp limits loss of statistical power by not including variants that are not associated with the trait of interest in the final model. Also, SCARVAsnp takes into consideration the direction of association by effectively modelling positively and negatively associated variants.

A novel approach for the simultaneous analysis of common and rare variants in complex traits.

Genome-wide association studies (GWAS) have been successful in detecting common genetic variants underlying common traits and diseases. Despite the GWAS success stories, the percent trait variance explained by GWAS signals, the so called "missing heritability" has been, at best, modest. Also, the predictive power of common variants identified by GWAS has not been encouraging. Given these observations along with the fact that the effects of rare variants are often, by design, unaccounted for by GWAS and the availability of sequence data, there is a growing need for robust analytic approaches to evaluate the contribution of rare variants to common complex diseases. Here we propose a new method that enables the simultaneous analysis of the association between rare and common variants in disease etiology. We refer to this method as SCARVA (simultaneous common and rare variants analysis). SCARVA is simple to use and is efficient. We used SCARVA to analyze two independent real datasets to identify rare and common variants underlying variation in obesity among participants in the Africa America Diabetes Mellitus (AADM) study and plasma triglyceride levels in the Dallas Heart Study (DHS). We found common and rare variants associated with both traits, consistent with published results.