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Background: Hydrogen peroxide (H2O2)-based tooth bleaching reagents have recently increased in popularity and controversy. H2O2 gel (3%) is used in a Nightguard for vital bleaching; transient tooth sensitivity and oral mucosa irritation have been reported. Genotoxicity and carcinogenicity have also been significant concerns. Methods: We used primary cultured normal human oral keratinocytes (NHOKs) as an in vitro model to investigate the pathological effects to mitochondria functions on human oral keratinocytes exposed to different doses of H2O2 for different durations. Results: An MTT assay showed compromised cell viability at a dose over 5 mM. The treatments induced nuclear DNA damage, measured using a single-cell gel electrophoresis assay. A real-time quantitative polymerase chain reaction showed H2O2 induced significant increase in mitochondrial 4977-bp deletion. Mitochondrial membrane potential and apoptosis assays suggested that oxidative damage defense mechanisms were activated after prolonged exposure to H2O2. Reduced intracellular glutathione was an effective defense against oxidative damage from 5 mM of H2O2. Conclusion: Our study suggests the importance for keratinocyte damage of the dose and the duration of the exposure to H2O2 in at-home-bleaching. A treatment dose ≥100 mM directly causes severe cytotoxicity with as little as 15 min of exposure. [ABSTRACT FROM AUTHOR]

Background: Although advances in sequencing technologies have popularized the use of microRNA (miRNA) sequencing (miRNA-seq) for the quantification of miRNA expression, questions remain concerning the optimal methodologies for analysis and utilization of the data. The construction of a miRNA sequencing library selects RNA by length rather than type. However, as we have previously described, miRNAs represent only a subset of the species obtained by size selection. Consequently, the libraries obtained for miRNA sequencing also contain a variety of additional species of small RNAs. This study looks at the prevalence of these other species obtained from bone marrow aspirate specimens and explores the predictive value of these small RNAs in the determination of response to therapy in myelodysplastic syndromes (MDS)., Methods: Paired pre and post treatment bone marrow aspirate specimens were obtained from patients with MDS who were treated with either azacytidine or decitabine (24 pre-treatment specimens, 23 post-treatment specimens) with 22 additional non-MDS control specimens. Total RNA was extracted from these specimens and submitted for next generation sequencing after an additional size exclusion step to enrich for small RNAs. The species of small RNAs were enumerated, single nucleotide variants (SNVs) identified, and finally the differential expression of tRNA-derived species (tDRs) in the specimens correlated with diseasestatus and response to therapy., Results: Using miRNA sequencing data generated from bone marrow aspirate samples of patients with known MDS (N = 47) and controls (N = 23), we demonstrated that transfer RNA (tRNA) fragments (specifically tRNA halves, tRHs) are one of the most common species of small RNA isolated from size selection. Using tRNA expression values extracted from miRNA sequencing data, we identified six tRNA fragments that are differentially expressed between MDS and normal samples. Using the elastic net method, we identified four tRNAs-derived small RNAs (tDRs) that together can explain 67 % of the variation in treatment response for MDS patients. Similar analysis of specifically mitochondrial tDRs (mt-tDRs) identified 13 mt-tDRs which distinguished disease status in the samples and a single mt-tDR which predited response. Finally, 14 SNVs within the tDRs were found in at least 20 % of the MDS samples and were not observed in any of the control specimens., Discussion: This study highlights the prevalence of tDRs in RNA-seq studies focused on small RNAs. The potential etiologies of these species, both technical and biologic, are discussed as well as important challenges in the interpretation of tDR data., Conclusions: Our analysis results suggest that tRNA fragments can be accurately detected through miRNA sequencing data and that the expression of these species may be useful in the diagnosis of MDS and the prediction of response to therapy.

Background: Comparative analysis of expression profiles between early and late stage cancers can help to understand cancer progression and metastasis mechanisms and to predict the clinical aggressiveness of cancer. The observed stage-dependent expression changes can be explained by genetic and epigenetic alterations as well as transcription dysregulation. Unlike genetic and epigenetic alterations, however, activity changes of transcription factors, generally occurring at the post-transcriptional or post-translational level, are hard to detect and quantify., Methods: Here we developed a statistical framework to infer the activity changes of transcription factors by simultaneously taking into account the contributions of genetic and epigenetic alterations to mRNA expression variations., Results: Applied to kidney renal clear cell carcinoma (KIRC), the model underscored the role of methylation as a significant contributor to stage-dependent expression alterations and identified key transcription factors as potential drivers of cancer progression., Conclusions: Integrating copy number, methylation, and transcription factor activity signatures to explain stage-dependent expression alterations presented a precise and comprehensive view on the underlying mechanisms during KIRC progression.

Background: Sample size calculation is an important issue in the experimental design of biomedical research. For RNA-seq experiments, the sample size calculation method based on the Poisson model has been proposed; however, when there are biological replicates, RNA-seq data could exhibit variation significantly greater than the mean (i.e. over-dispersion). The Poisson model cannot appropriately model the over-dispersion, and in such cases, the negative binomial model has been used as a natural extension of the Poisson model. Because the field currently lacks a sample size calculation method based on the negative binomial model for assessing differential expression analysis of RNA-seq data, we propose a method to calculate the sample size., Results: We propose a sample size calculation method based on the exact test for assessing differential expression analysis of RNA-seq data., Conclusions: The proposed sample size calculation method is straightforward and not computationally intensive. Simulation studies to evaluate the performance of the proposed sample size method are presented; the results indicate our method works well, with achievement of desired power.

Background: Identification of expression alternations between early and late stage cancers is helpful for understanding cancer development and progression. Much research has been done focusing on stage-dependent gene expression profiles. In contrast, relatively fewer studies on isoform expression profiles have been performed due to the difficulty of quantification and noisy splicing. Here we conducted both gene- and isoform-level analysis on RNA-seq data of 234 stage I and 81 stage IV kidney renal clear cell carcinoma patients, aiming to uncover the stage-dependent expression signatures and investigate the advantage of isoform expression profiling for identifying advanced stage cancers and predicting clinical outcome., Results: Both gene and isoform expression signatures are useful for distinguishing cancer stages. They provide common and unique information associated with cancer progression and metastasis. Combining gene and isoform signatures even improves the classification performance and reveals additional important biological processes, such as angiogenesis and TGF--beta signaling pathway. Moreover, expression abundance of a number of genes and isoforms is predictive of the risk of cancer death in an independent dataset, such as gene and isoform expression of ITPKA, the expression of a functional important isoform of UPS19., Conclusion: Isoform expression profiling provides unique and important information which cannot be detected by gene expression profiles. Combining gene and isoform expression signatures helps to identify advanced stage cancers, predict clinical outcome, and present a comprehensive view of cancer development and progression.

Background: Dealing with high dimensional markers, such as gene expression data obtained using microarray chip technology or genomics studies, is a key challenge because the numbers of features greatly exceeds the number of biological samples. After selecting biologically relevant genes, how to summarize the expression of selected genes and then further build predicted model is an important issue in medical applications. One intuitive method of addressing this challenge assigns different weights to different features, subsequently combining this information into a single score, named the compound covariate. Investigators commonly employ this score to assess whether an association exists between the compound covariate and clinical outcomes adjusted for baseline covariates. However, we found that some clinical papers concerned with such analysis report bias p-values based on flawed compound covariate in their training data set., Results: We correct this flaw in the analysis and we also propose treating the compound score as a random covariate, to achieve more appropriate results and significantly improve study power for survival outcomes. With this proposed method, we thoroughly assess the performance of two commonly used estimated gene weights through simulation studies. When the sample size is 100, and censoring rates are 50%, 30%, and 10%, power is increased by 10.6%, 3.5%, and 0.4%, respectively, by treating the compound score as a random covariate rather than a fixed covariate. Finally, we assess our proposed method using two publicly available microarray data sets., Conclusion: In this article, we correct this flaw in the analysis and the propose method, treating the compound score as a random covariate, can achieve more appropriate results and improve study power for survival outcomes.