Emerging evidence supports that oral microbiota are associated with health and diseases of the esophagus. How oral microbiota change in Chinese patients with esophageal cancer (EC) is unknown, neither is their biomarker role. For an objective to understand alterations of oral microbiota in Chinese EC patients, we conducted a case-control study including saliva samples from 39 EC patients and 51 healthy volunteers. 16S rDNA genes of V3-V4 variable regions were sequenced to identify taxon. Relationship between oral flora and disease was analyzed according to alpha diversity and beta diversity. Resultantly, the Shannon index (p = 0.2) and the Simpson diversity index (p = 0.071) were not significant between the two groups. Yet we still found several species different in abundance between the two groups. For the EC group, the most significantly increased taxa were Firmicutes, Negativicutes, Selenomonadales, Prevotellaceae, Prevotella, and Veillonellaceae, while the most significantly decreased taxa were Proteobacteria, Betaproteobacteria, Neisseriales, Neisseriaceae, and Neisseria. In conclusion, there are significant alterations in abundance of some oral microbiomes between the EC patients and the healthy controls in the studied Chinese participants, which may be meaningful for predicting the development of EC, and the potential roles of these species in EC development deserve further studies.
Background and Aim: Reflux Esophagitis (RE) is caused by a variety of factors including anatomical and functional alterations involved in the pathogenesis. Oral microbiota is influenced by many factors such as heredity, nutrition, environments and host conditions, but little is known about relationship between oral microbiota and RE. The aim of this study was to explore whether the oral microbiota is changed in patients with RE. Methods: To clarify this correlation, fresh saliva samples from all subjects were collected and then oral microorganism diversity was analysed in 55 patients with RE and 51 controls via hypervariable tag sequencing and analyzing the V3-V4 region of the 16S rDNA gene. Results: There was no difference found in oral microbial diversity between RE patients and healthy controls by Shannon diversity index (p=0.60) and Simpson diversity index (p= 0.38). The abundance of Proteobacteria was lower, but Bacteroidetes was higher in patients with RE at the phylum level. At the genus level the abundances of Prevotella,
Primary hepatocellular carcinoma (HCC) is one of the most common malignant tumors. At present, the molecular mechanism of HCC remains unclear. A recent circular RNA (circRNA) profiling study showed that circRBM23 expression was upregulated in HCC tissues. Therefore, in this study, the impact of circRBM23 during the progression of HCC was evaluated. The expression levels of circRBM23 and miR-138 in HCC tissues and HCC cell lines were determined by RT-PCR and the results indicated that circRBM23 expression was increased in the HCC tissues and HCC cell lines, whereas miR-138 expression was decreased. An upregulation of circRBM23 expression in HCC cells was shown to increase cell viability, and also increased the ability of cells to migrate. Downregulation of circRBM23 was found to decrease cell viability, proliferation, and migration, and promote the expression of miR-138 and its related target genes, vimentin, and CCND3. Moreover, miR-138 was found to regulate HCC cell viability and migration, and the levels of vimentin and CCND3 protein expression were found to be inversely correlated with those of miR-138 expression. The downregulation of circRBM23 in HCC tissues can regulate the miR-138-mediated signal pathway by promoting miR-138 expression. The results in vivo demonstrated that circRBM23 is required for the tumorigenesis with downregulation of tumor suppressor miR-138. These data indicated that upregulated circRBM23 functioned as oncogene in HCC through regulating the tumor suppressor miR-138.
BackgroundParalog reduction, the loss of duplicate genes after whole genome duplication (WGD) is a pervasive process. Whether this loss proceeds gene by gene or through deletion of multi-gene DNA segments is controversial, as is the question of fractionation bias, namely whether one homeologous chromosome is more vulnerable to gene deletion than the other.ResultsAs a null hypothesis, we first assume deletion events, on one homeolog only, excise a geometrically distributed number of genes with unknown mean µ, and these events combine to produce deleted runs of length l, distributed approximately as a negative binomial with unknown parameter r, itself a random variable with distribution π(·). A more realistic model requires deletion events on both homeologs distributed as a truncated geometric. We simulate the distribution of run lengths l in both models, as well as the underlying π(r), as a function of µ, and show how sampling l allows us to estimate µ. We apply this to data on a total of 15 genomes descended from 6 distinct WGD events and show how to correct the bias towards shorter runs caused by genome rearrangements. Because of the difficulty in deriving π(·) analytically, we develop a deterministic recurrence to calculate each π(r) as a function of µ and the proportion of unreduced paralog pairs.ConclusionsThe parameter µ can be estimated based on run lengths of single-copy regions. Estimates of µ in real data do not exclude the possibility that duplicate gene deletion is largely gene by gene, although it may sometimes involve longer segments.
BackgroundParalog reduction, the loss of duplicate genes after whole genome duplication (WGD) is a pervasive process. Whether this loss proceeds gene by gene or through deletion of multi-gene DNA segments is controversial, as is the question of fractionation bias, namely whether one homeologous chromosome is more vulnerable to gene deletion than the other.ResultsAs a null hypothesis, we first assume deletion events, on either homeolog, excise a geometrically distributed number of genes with unknown mean μ, and a number r of these events overlap to produce deleted runs of length l. There is a fractionation bias 0 ≤ ϕ ≤ 1 for deletions to fall on one homeolog rather than the other. The parameter r is a random variable with distribution π(·). We simulate the distribution of run lengths l, as well as the underlying π(·), as a function of μ, ϕ and θ, the proportion of remaining genes in duplicate form. We show how sampling l allows us to estimate μ and ϕ. The main part of this work is the derivation of a deterministic recurrence to calculate each π(r) as a function of μ, ϕ and θ.ConclusionsThe recurrence for π provides a deeper mathematical understanding of fractionation process than simulations. The parameters μ and ϕ can be estimated based on run lengths of single-copy regions.
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