The link between nonresolving inflammation and cancer is well documented. On the one hand, epidemiologic evidence supports that approximately 25% of all human cancer worldwide is caused by nonresolving inflammation. On the other hand, inflammatory cells are found in the microenvironment of most, if not all, tumors. In the tumor micro-environment, inflammatory cells and molecules influence almost every aspect of cancer. MicroRNAs (miRNAs) participate in the initiation and progression of nonresolving inflammation-related cancer by regulating the key genes and related signaling pathways. Further investigation into the molecular mechanisms by which miRNAs carry out their functions will be of great value in the prevention, early diagnosis, and treatment of tumors.
Chronic Disease ; Humans ; Inflammation ; complications ; genetics ; immunology ; Inflammation Mediators ; immunology ; MicroRNAs ; genetics ; Neoplasms ; etiology ; genetics ; Tumor Microenvironment

Objective: To determine the most influential data features and to develop machine learning approaches that best predict hospital readmissions among patients with diabetes. Methods: In this retrospective cohort study, we surveyed patient statistics and performed feature analysis to identify the most influential data features associated with readmissions. Classification of all-cause, 30-day readmission outcomes were modeled using logistic regression, artificial neural network, and EasyEnsemble. F1 statistic, sensitivity, and positive predictive value were used to evaluate the model performance. Results: We identified 14 most influential data features (4 numeric features and 10 categorical features) and evaluated 3 machine learning models with numerous sampling methods (oversampling, undersampling, and hybrid techniques). The deep learning model offered no improvement over traditional models (logistic regression and EasyEnsemble) for predicting readmission, whereas the other two algorithms led to much smaller differences between the training and testing datasets. Conclusions: Machine learning approaches to record electronic health data offer a promising method for improving readmission prediction in patients with diabetes. But more work is needed to construct datasets with more clinical variables beyond the standard risk factors and to fine-tune and optimize machine learning models.

The research team on the National Key Scientific Program of China: "Transcriptomic regulation and molecular mechanism research of polygenic tumor at different stages" has focused on the field of transcriptomics of 4 common polygenic tumors, including nasopharyngeal carcinoma(NPC), breast cancer, colorectal cancer, and glioma. Extensive laboratory work has been carried out on the expression and regulation of tumor transcriptomics; identification of tumor suppressor/susceptible genes; mechanism of tumor epigenetics including miRNAs, and comparative study of specific gene/protein cluster of tumor transcriptomics and proteomics. Genes including SPLUNC1, LTF, BRD7, NOR1, BRCA1/2, PALB2, AF1Q, SOX17, NGX6, SOX7, and LRRC4 have been identified as the key transcriptional regulation genes during the stage of tumor initiation and invasion. Accordingly,the NPC gene signal regulation network of "SPLUNC1-miR-141-target genes", the breast cancer interaction signal pathway of "miR-193b-uPA",the glioma signal network of "miR-381- LRRC4-MEK/ERK/AKT", and the miRNA-target gene network of colorectal cancer metastasis related gene NGX6 have been thoroughly elucidated. These fruitful Results imply that the changes of key molecules in crucial signal pathway will cause severe dysfunction in signal transduction and gene regulation network in polygenic tumors, indicating that in the category of pathogenesis,these tumors may further classify as the "Disease of gene signal transduction and gene regulation network disorder". The researches have laid solid foundation for revealing the molecular mechanism and transcriptomic regulation of polygenic tumors at different stages.
Animals ; Brain Neoplasms ; genetics ; pathology ; Breast Neoplasms ; genetics ; pathology ; Colorectal Neoplasms ; genetics ; pathology ; Gene Expression Regulation, Neoplastic ; Gene Regulatory Networks ; Glioma ; genetics ; pathology ; Humans ; MicroRNAs ; genetics ; Multifactorial Inheritance ; Nasopharyngeal Neoplasms ; genetics ; pathology ; Neoplasm Proteins ; genetics ; Neoplasm Staging ; Neoplasms ; genetics ; Transcription, Genetic ; Transcriptome ; Tumor Suppressor Proteins ; genetics