Although methyltransferase has been recognized as a major element that governs the epigenetic regulation of the genome during temozolomide (TMZ) chemotherapy in glioblastoma multiforme (GBM) patients, its regulatory effect on glioblastoma chemoresistance has not been well defined. This study investigated whether DNA methyltransferase (DNMT) expression was associated with TMZ sensitivity in glioma cells and elucidated the underlying mechanism. DNMT expression was analyzed by western blotting. miR-20a promoter methylation was evaluated by methylation-specific PCR. Cell viability and apoptosis were assessed using the 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide (MTT) and TdT-mediated dUTP-biotin nick end labeling assays, respectively. The results showed that compared with parental U251 cells, DNMT1 expression was downregulated, miR-20a promoter methylation was attenuated and miR-20a levels were elevated in TMZ-resistant U251 cells. Methyltransferase inhibition by 5-aza-2\'-deoxycytidine treatment reduced TMZ sensitivity in U251 cells. In U251/TM cells, DNMT1 expression was negatively correlated with miR-20a expression and positively correlated with TMZ sensitivity and leucine-rich repeats and immunoglobulin-like domains 1 expression; these effects were reversed by changes in miR-20a expression. DNMT1 overexpression induced an increase in U251/TM cell apoptosis that was inhibited by the miR-20a mimic, whereas DNMT1 silencing attenuated U251/TM cell apoptosis in a manner that was abrogated by miR-20a inhibitor treatment. Tumor growth of the U251/TM xenograft was inhibited by pcDNA-DNMT1 pretreatment and boosted by DNMT1-small hairpin RNA pretreatment. In summary, DNMT1 mediated chemosensitivity by reducing methylation of the microRNA-20a promoter in glioma cells.
Animals ; Antineoplastic Agents, Alkylating/*pharmacology/therapeutic use ; Apoptosis/drug effects ; Brain/drug effects/metabolism/pathology ; Brain Neoplasms/drug therapy/*genetics/pathology ; DNA (Cytosine-5-)-Methyltransferase/antagonists & inhibitors/*genetics/metabolism ; DNA Methylation ; Dacarbazine/*analogs & derivatives/pharmacology/therapeutic use ; Drug Resistance, Neoplasm ; Female ; Gene Expression Regulation, Neoplastic ; Glioma/drug therapy/*genetics/pathology ; Humans ; Mice, Inbred C57BL ; MicroRNAs/*genetics ; Promoter Regions, Genetic

Acute pancreatitis (AP) is a common disease faced by clinicians. Severe acute pancreatitis (SAP) has a high mortality rate, so early identification of patients who may develop into SAP is of great significance for guiding treatment. Machine learning is a multi-layer representational learning algorithm that analyzes and obtains laws from existing data and uses these laws to make predictions on unknown data. This study established an SAP prediction scoring system based on machine learning, which can predict the SAP risk of patients within 24 hours. The prediction accuracy rate is as high as 87.36% and AUC 94.11%. The model can better assist clinical decision-making and treatment, and guide doctors to make relevant interventions earlier.