Notes

Revisions about Excisional Treatments pertaining to Lymphedema.
Competing endogenous RNAs (ceRNAs) regulate each other by competitively binding microRNAs they share. This is a vital post-transcriptional regulation mechanism and plays critical roles in physiological and pathological processes. Current computational methods for the identification of ceRNA pairs are mainly based on the correlation of the expression of ceRNA candidates and the number of shared microRNAs, without considering the sensitivity of the correlation to the expression levels of the shared microRNAs. To overcome this limitation, we introduced liquid association (LA), a dynamic correlation measure, which can evaluate the sensitivity of the correlation of ceRNAs to microRNAs, as an additional factor for the detection of ceRNAs. To this end, we firstly analyzed the effect of LA on detecting ceRNA pairs. Subsequently, we proposed an LA-based framework, termed LAceModule, to identify ceRNA modules by integrating the conventional Pearson correlation coefficient and dynamic correlation LA with multi-view non-negative matrix factorization. see more Using breast and liver cancer datasets, the experimental results demonstrated that LA is a useful measure in the detection of ceRNA pairs and modules. We found that the identified ceRNA modules play roles in cell adhesion, cell migration, and cell-cell communication. Furthermore, our results show that ceRNAs may represent potential drug targets and markers for the treatment and prognosis of cancer. Copyright © 2020 Wen, Gao and Hu.Several long non-coding RNAs (lncRNAs) have been reported regulate the expression of neighbor protein-coding genes at post-transcriptional, transcriptional and epigenetic levels. Dmp1 (Dentin matrix protein 1), encoding a non-collagenous extracellular matrix protein, plays an important role in dentin and bone mineralization. However, the transcriptional regulation of lncRNA on Dmp1 has not been reported. In this study, we identified a novel lncRNA named lnc-DMP1, which is near the Dmp1 gene region and undergoes remarkable changes during mandible development. lnc-DMP1 is co-localized and significantly expressed correlation with Dmp1 in embryonic and postnatal mouse mandibles. In MC3T3-E1 cells, lnc-DMP1 positively regulates DMP1 expression and skeletal mineralization. Furthermore, lnc-DMP1 induces the promoter activity of Dmp1 by modulating H3K27Ac enrichment in the Dmp1 promoter. In conclusion, our results indicate that lnc-DMP1 is a novel lncRNA near the Dmp1 gene region and regulates Dmp1 expression by modulating the H3K27 acetylation level of Dmp1 promoter. Copyright © 2020 Xia, Ruan, Li, Yu, Kong, Zhuang and Wu.Kenaf (Hibiscus cannabinus) is one of the most fast-growing bast in the world and belongs to the family Malvaceae. However, the systematic classification and chloroplast (cp) genome of kenaf has not been reported to date. In this study, we sequenced the cp genome of kenaf and conducted phylogenetic and comparative analyses in the family of Malvaceae. The sizes of H. cannabinus cp genomes were 162,903 bp in length, containing 113 unique genes (79 protein-coding genes, four rRNA genes, and 30 tRNA genes). Phylogenetic analysis indicated that the cp genome sequence of H. cannabinus has closer relationships with Talipariti hamabo and Abelmoschus esculentus than with Hibiscus syriacus, which disagrees with the taxonomical relationship. Further analysis obtained a new version of the cp genome annotation of H. syriacus and found that the orientation variation of small single copy (SSC) region exists widely in the family of Malvaceae. The highly variable ycf1 and the highly conserved gene rrn32 were identified among the family of Malvaceae. In particular, the explanation for two different SSC orientations in the cp genomes associated with phylogenetic analysis is discussed. These results provide insights into the systematic classification of the Hibiscus genus in the Malvaceae family. Copyright © 2020 Cheng, Zhang, Qi and Zhang.High-grade serous ovarian cancer is one of the deadliest gynecological malignancies and remains a clinical challenge. There is a critical need to effectively define patient stratification in a clinical setting. In this study, we address this question and determine the optimal number of molecular subgroups for ovarian cancer patients. By studying several independent patient cohorts, we observed that classifying high-grade serous ovarian tumors into four molecular subgroups using a transcriptomic-based approach did not reproducibly predict patient survival. In contrast, classifying these tumors into only two molecular subgroups, fibrosis and non-fibrosis, could reliably inform on patient survival. In addition, we found complementarity between transcriptomic data and the genomic signature for homologous recombination deficiency (HRD) that helped in defining prognosis of ovarian cancer patients. We also established that the transcriptomic and genomic signatures underlined independent biological processes and defined four different risk populations. Thus, combining genomic and transcriptomic information appears as the most appropriate stratification method to reliably subgroup high-grade serous ovarian cancer patients. This method can easily be transferred into the clinical setting. Copyright © 2020 Kieffer, Bonneau, Popova, Rouzier, Stern and Mechta-Grigoriou.The direct RNA sequencing platform offered by Oxford Nanopore Technologies allows for direct measurement of RNA molecules without the need of conversion to complementary DNA, fragmentation or amplification. As such, it is virtually capable of detecting any given RNA modification present in the molecule that is being sequenced, as well as provide polyA tail length estimations at the level of individual RNA molecules. Although this technology has been publicly available since 2017, the complexity of the raw Nanopore data, together with the lack of systematic and reproducible pipelines, have greatly hindered the access of this technology to the general user. Here we address this problem by providing a fully benchmarked workflow for the analysis of direct RNA sequencing reads, termed MasterOfPores. The pipeline starts with a pre-processing module, which converts raw current intensities into multiple types of processed data including FASTQ and BAM, providing metrics of the quality of the run, quality-filtering, demultiplexing, base-calling and mapping.
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