Arthrospira platensis is a multi-cellular and filamentous non-N2-fixing cyanobacterium that is capable of performing oxygenic photosynthesis. In this study, we determined the nearly complete genome sequence of A. platensis YZ. A. platensis YZ genome is a single, circular chromosome of 6.62 Mb in size. Phylogenetic and comparative genomic analyses revealed that A. platensis YZ was more closely related to A. platensis NIES-39 than Arthrospira sp. PCC 8005 and A. platensis C1. Broad gene gains were identified between A. platensis YZ and three other Arthrospira speices, some of which have been previously demonstrated that can be laterally transferred among different species, such as restriction-modification systems-coding genes. Moreover, unprecedented extensive chromosomal rearrangements among different strains were observed. The chromosomal rearrangements, particularly the chromosomal inversions, were analysed and estimated to be closely related to palindromes that involved long inverted repeat sequences and the extensively distributed type IIR restriction enzyme in the Arthrospira genome. In addition, species from genus Arthrospira unanimously contained the highest rate of repetitive sequence compared with the other species of order Oscillatoriales , suggested that sequence duplication significantly contributed to Arthrospira genome phylogeny. These results provided in-depth views into the genomic phylogeny and structural variation of A. platensis , as well as provide a valuable resource for functional genomics studies.

As endometrial cancer (EC) is a major threat to female health worldwide, the ability to provide an accurate diagnosis and prognosis of EC is promising to improve its treatment guidance. Since the discovery of miRNAs, it has been realized that miRNAs are associated with every cell function, including malignant transformation and metastasis. This study aimed to explore diagnostic and prognostic miRNA markers of EC. In this study, differential analysis and machine learning were performed, followed by correlation analysis of miRNA‐mRNA based on the miRNA and mRNA expression data. Nine miRNAs were identified as diagnostic markers, and a diagnostic classifier was established to distinguish between EC and normal endometrium tissue with overall correct rates >95%. Five specific prognostic miRNA markers were selected to construct a prognostic model, which was confirmed more effective in identifying EC patients at high risk of mortality compared with the FIGO staging system. This study demonstrates that the expression patterns of miRNAs may hold promise for becoming diagnostic and prognostic biomarkers and novel therapeutic targets for EC.

Wilm's tumour‐1 (WT1) is overexpressed in pancreatic ductal adenocarcinoma (PDAC) and enhances metastasis. Deubiquitination stabilizes target proteins, and inhibiting deubiquitination facilitates the degradation of target proteins. However, whether inhibiting deubiquitination of WT1 facilitates its degradation and presents anti‐cancer ability in PDAC is unknown. Here, we found that deubiquitinase inhibitor degrasyn rapidly induced the degradation of endogenous and exogenous WT1 through enhancing ubiquitination of WT1 followed by the up‐regulation of E‐cadherin. Knockdown of WT1 by short hairpin RNAs (shRNAs) inhibited metastasis and overexpression of WT1 partially prevented degrasyn‐induced anti‐metastasis activity, suggesting that degrasyn presents anti‐metastasis activity partially through degrading WT1 protein. We further identified that USP5 deubiquitinated WT1 and stabilized its expression. The higher expressions of USP5 and WT1 are associated with tumour metastasis. More importantly, degrasyn inhibited the activity of USP5 and overexpression of USP5 partially prevented degrasyn‐induced degradation of WT1 protein, suggesting that degrasyn degraded WT1 protein through inhibiting the activity of USP5. Finally, degrasyn reduced the tumorigenicity in a xenograft mouse model and reduced the metastasis in vivo. Our results indicate that degrasyn presents strong anti‐cancer activity through USP5‐WT1‐E‐cadherin signalling in PDAC. Therefore, degrasyn holds promise as cancer therapeutic agent in PDAC with high expressions of USP5 and WT1.

The accurate diagnosis of endometrial cancer (EC) holds great promise for improving its treatment choice and prognosis prediction. This work aimed to identify diagnostic biomarkers for differentiating EC tumors from tumors in other tissues, as well as prognostic signatures for predicting survival in EC patients. We identified 48 tissue-specific markers using a cohort of genome-wide methylation data from three common gynecological tumors and their corresponding normal tissues. A diagnostic classifier was constructed based on these 48 CpG markers that could predict cancerous versus normal tissue with an overall correct rate of 98.3% in the entire repository. Fifteen CpG markers associated with the overall survival (OS) and development of EC were also identified based on the methylation patterns of the EC samples. A prognostic model that aggregated these prognostic CpG markers was established and shown to have a higher discriminative ability to distinguish EC patients with an elevated risk of mortality than the FIGO staging system and several other clinical prognostic variables. This study presents the utility of DNA methylation in identifying biomarkers for the diagnosis and prognosis of EC and will help improve our understanding of the underlying mechanisms involved in the development of EC.

Endometrial carcinoma (EC) is the most common malignant tumor of the female genital tract in developed countries. The prognosis of early stage EC is favorable, but a subset faces high risk of cancer progression or recurrence. EC has a poor prognosis upon progression to advanced or metastatic stages. Therefore, our goal is to build a robust prognostic model for predicting overall survival (OS) in EC patients. In this study, 1571 genes were identified as being associated with OS based on genomewide expression profiles using a training dataset. Kyoto Encyclopedia of Genes and Genomes enrichment analysis revealed that these genes were involved in various cancer‐related signaling pathways. Nine signature genes were further selected using stepwise selection, and their potential role in the development of EC was demonstrated by performing differential expression analysis between EC and normal uterine tissues. A prognostic model that aggregated these nine signature genes was ultimately established and effectively divided EC patients into two risk groups. OS for patients in the high‐risk group was significantly poorer compared with that of the low‐risk group. This nine‐gene model was subsequently validated and evaluated using the TCGA dataset and shown to have a high discriminating power to distinguish EC patients with an elevated risk of mortality based on the FIGO staging system and other prognostic factors. This study provides a novel prognostic model for the identification of EC patients with elevated risk of mortality and will help to improve our understanding of the underlying mechanisms involved in prognostic EC factors.