Cell adhesion molecules

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.

Novel miRNA markers for the diagnosis and prognosis of endometrial cancer

Endometrial cancer is highly malignant and has a poor prognosis in the advanced stage, thus, prediction of its prognosis is important. DNA methylation has rapidly gained clinical attention as a biomarker for diagnostic, prognostic and predictive purposes in various cancers. In present study, differentially methylated positions and differentially expressed genes were identified according to DNA methylation and RNA-Seq data. Functional analyses and interaction network were performed to identify hub genes, and overall survival analysis of hub genes were validated. The top genes were evaluated by immunohistochemical staining of endometrial cancer tissues. The gene function was evaluated by cell growth curve after knockdown CDC20 and CCNA2 of endometrial cancer cell line. A total of 329 hypomethylated highly expressed genes and 359 hypermethylated lowly expressed genes were identified, and four hub genes were obtained according to the interaction network. Patients with low expression of CDC20 and CCNA2 showed better overall survival. The results also were demonstrated by the immunohistochemical staining. Cell growth curve also demonstrated that knockdown CDC20 and CCNA2 can suppress the cell proliferation. We have identified two aberrantly methylated genes, CDC20 and CCNA2 as novel biomarkers for precision diagnosis in EC.

/pdf/identification-of-prognosis-markers-for-endometrial-cancer-4dbj4inrzm.pdf

Identification of prognosis markers for endometrial cancer by integrated analysis of DNA methylation and RNA-Seq data.

Purpose Progestin resistance is a critical obstacle for endometrial conservative therapy. Therefore, studies to acquire a more comprehensive understanding of the mechanisms are urgent. However, the pivotal molecules are still unexplored. Materials and methods We downloaded GSE121367 from the GEO database. The "limma" R language package was applied to identify differentially expressed genes (DEGs). We conducted Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA). Protein-protein interaction was constructed by STRING and visualized in Cytoscape. The tumor immune microenvironment was explored by the TISIDB database. Methylation validation and overall survival analysis were conducted by the TCGA database. In addition, the upstream modulators of hub genes were predicted by miRTarBase and Network Analyst databases. The expression levels of candidate genes were validated by quantitative real-time PCR (qRT-PCR), Western blot, and immunohistochemical assay (IHC). Cell growth, clone formation, migration, invasion, and wound healing assays were studied to explore the role of MSX1 in progestin resistance in vitro. Results A total of 3,282 DEGs were identified and they were mostly enriched in the cell adhesion pathway. We screened out ten hub genes whose genomic alteration rates were low based on the current endometrial carcinoma sample sets. Has-miR-335-5p, has-miR-124-3p, MAZ, and TFDP1 were the most prominent upstream regulators. The methylation status of CDH1, JAG1, EPCAM, and MSX1 was decreased, corresponding to their high protein expression, which also predicted better overall survival. The homeobox protein of MSX1 showed significant tissue specificity and better prognostic value and its knockdown inhibited epithelial-mesenchymal transitions (EMT) and enhanced progesterone efficacy. Conclusion Our study identified that the gene of MSX1 promised to be the specific indicator and therapeutic target for progestin resistance. This would shed new light on the underlying biological mechanism to overcome progestin resistance of endometrial cancer.

Identification and Validation of MSX1 as a Key Candidate for Progestin Resistance in Endometrial Cancer

Under conditions of increasing environmental pollution, true saprophytes are capable of changing their survival strategies and demonstrating certain pathogenicity factors. Actinobacteria of the genus Rhodococcus, typical soil and aquatic biotope inhabitants, are characterized by high ecological plasticity and a wide range of oxidized organic substrates, including hydrocarbons and their derivatives. Their cell adaptations, such as the ability of adhering and colonizing surfaces, a complex life cycle, formation of resting cells and capsule-like structures, diauxotrophy, and a rigid cell wall, developed against the negative effects of anthropogenic pollutants are discussed and the risks of possible pathogenization of free-living saprotrophic Rhodococcus species are proposed. Due to universal adaptation features, Rhodococcus species are among the candidates, if further anthropogenic pressure increases, to move into the group of potentially pathogenic organisms with “unprofessional” parasitism, and to join an expanding list of infectious agents as facultative or occasional parasites.

Responses to Ecopollutants and Pathogenization Risks of Saprotrophic Rhodococcus Species.

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.

https://www.tandfonline.com/doi/pdf/10.1080/15592294.2018.1474071

Exploration of DNA methylation markers for diagnosis and prognosis of patients with endometrial cancer.

Rhodococcus equi, a member of the Rhodococcus genus, is a gram-positive pathogenic bacterium. Rhodococcus possesses an open pan-genome that constitutes the basis of its high genomic diversity and allows for adaptation to specific niche conditions and the changing host environments. Our analysis further showed that the core genome of R. equi contributes to the pathogenicity and niche adaptation of R. equi. Comparative genomic analysis revealed that the genomes of R. equi shared identical collinearity relationship, and heterogeneity was mainly acquired by means of genomic islands and prophages. Moreover, genomic islands in R. equi were always involved in virulence, resistance, or niche adaptation and possibly working with prophages to cause the majority of genome expansion. These findings provide an insight into the genomic diversity, evolution, and structural variation of R. equi and a valuable resource for functional genomic studies.

/pdf/comparative-genomic-analysis-of-rhodococcus-equi-an-insight-2oydjxjy5y.pdf

Comparative Genomic Analysis of Rhodococcus equi: An Insight into Genomic Diversity and Genome Evolution

Identification of prokaryotic transposases (Tnps) not only gives insight into the spread of antibiotic resistance and virulence but the process of DNA movement. This study aimed to develop a classifier for predicting Tnps in bacteria and archaea using machine learning (ML) approaches. We extracted a total of 2751 protein features from the training dataset including 14852 Tnps and 14852 controls, and selected 75 features as predictive signatures using the combined mutual information and least absolute shrinkage and selection operator algorithms. By aggregating these signatures, an ensemble classifier that integrated a collection of individual ML-based classifiers, was developed to identify Tnps. Further validation revealed that this classifier achieved good performance with an average AUC of 0.955, and met or exceeded other common methods. Based on this ensemble classifier, a stand-alone command-line tool designated TnpDiscovery was established to maximize the convenience for bioinformaticians and experimental researchers toward Tnp prediction. This study demonstrates the effectiveness of ML approaches in identifying Tnps, facilitating the discovery of novel Tnps in the future.

Prediction of prokaryotic transposases from protein features with machine learning approaches.

Objective The aim of this study is to investigate the characteristics and outcomes of shared bicycle-related injuries from a large emergency medical centre in China in five years from January 2017 to December 2021. Methods This study was conducted by reviewing the electronic medical record database of a large hospital in China for cases of shared bicycle-related injuries in five years from January 2017 to December 2021. The collected information included demographic data, injury characteristics, and outcomes. Multivariate logistic regression analysis was used to identify risk factors for fatal injury among victims. Results Most shared bicycle-related injuries occurred in male (60.50%), aged 18–35 (38.81%). Company employees (29.28%) were the majority of victims of shared bicycle-related injuries. Riding in a motor vehicle lane was the most common unsafe riding behaviour (26.52%). The lower limb was the most frequently injured body region (25.28%). Bruising (28.73%) was the most commonly diagnosed injury type. The fatality rate was 9.53%, 72.24% of victims recovered well without permanent disability, and 18.23% of victims had permanent disabilities. The length of hospital stay of most of the victims (67.54%) was less than 7 days, and the hospitalization cost of most of the victims (51.93%) was less than 20,000 Yuan. Riding in a motor vehicle lane, running red lights, and cycling against traffic flow are risk factors for fatal injury. Conclusions This study indicated that shared bicycle-related injuries make up a sizeable proportion of injuries presenting to the emergency department and with diverse injury characteristics and outcomes. The findings reflect that shared bicycle-related injury has become a public health problem. Therefore, it is necessary to establish injury prevention strategies for the safety of shared bicycle users.

/pdf/characteristics-and-outcomes-of-shared-bicycle-related-1bdkczan.pdf

Characteristics and Outcomes of Shared Bicycle-Related Injuries from a Large Emergency Medical Centre in China, 2017–2021

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Exploration of DNA methylation markers for diagnosis and prognosis of patients with endometrial cancer.

Comparative Genomic Analysis of Rhodococcus equi: An Insight into Genomic Diversity and Genome Evolution

Prediction of prokaryotic transposases from protein features with machine learning approaches.

Characteristics and Outcomes of Shared Bicycle-Related Injuries from a Large Emergency Medical Centre in China, 2017–2021