Liraglutide promotes apoptosis of HepG2 cells by activating JNK signaling pathway.

OBJECTIVE: To study the effect and mechanism of liraglutide on the apoptosis of human hepatocellular carcinoma HepG2 cells. MATERIALS AND METHODS: HepG2 cell was treated with different concentrations of liraglutide at 0, 1, 10, 100, and 1000 nmol/L. The effect of liraglutide on HepG2 proliferation was detected by Cell Counting Kit-8 (CCK-8) method; the effect of liraglutide on the protein expression of c-Jun NH2-terminal Kinase (JNK) and phosphorylated JNK (p-JNK) was detected by Western blot; the degree of HepG2 apoptosis was observed by flow cytometry, and JNK pathway blocker SP600125 was used to further confirm that liraglutide promoted HepG2 apoptosis by regulating JNK signaling pathway. RESULTS: The proliferation inhibition rate of HepG2 cells increased with time and the increase in the concentration of liraglutide. The proliferation inhibition rate was the strongest when cultured for 48 h, and the IC50 (half maximal inhibitory concentration) was about 100 nmol/L of liraglutide. 100 nmol/L liraglutide was selected as the intervention condition for subsequent use of SP600165. The apoptosis rate of HepG2 cells increased with the increase of liraglutide's concentration. The apoptosis rate of HepG2 cells at blocker SP600125+100 nmol/L liraglutide was significantly lower than that at 100 nmol/L liraglutide alone (p<0.05). There was no significant difference in the expression of JNK protein in HepG2 cells at different concentrations of liraglutide (p>0.05). There was no significant difference in the expression of JNK protein in HepG2 cells using JNK pathway blocker SP600125 (p>0.05), while using JNK pathway blocker SP600125 significantly up-regulated the expression of p-JNK protein in HepG2 cells than 100 nmol/L of liraglutide alone (p<0.05). CONCLUSIONS: Liraglutide can promote the apoptosis of hepatocellular carcinoma HepG2 cells in a dose-dependent manner, and its mechanism may act by promoting the activation of the JNK signaling pathway.

A support vector machine model provides an accurate transcript-level-based diagnostic for major depressive disorder.

Major depressive disorder (MDD) is a critical cause of morbidity and disability with an economic cost of hundreds of billions of dollars each year, necessitating more effective treatment strategies and novel approaches to translational research. A notable barrier in addressing this public health threat involves reliable identification of the disorder, as many affected individuals remain undiagnosed or misdiagnosed. An objective blood-based diagnostic test using transcript levels of a panel of markers would provide an invaluable tool for MDD as the infrastructure-including equipment, trained personnel, billing, and governmental approval-for similar tests is well established in clinics worldwide. Here we present a supervised classification model utilizing support vector machines (SVMs) for the analysis of transcriptomic data readily obtained from a peripheral blood specimen. The model was trained on data from subjects with MDD (n=32) and age- and gender-matched controls (n=32). This SVM model provides a cross-validated sensitivity and specificity of 90.6% for the diagnosis of MDD using a panel of 10 transcripts. We applied a logistic equation on the SVM model and quantified a likelihood of depression score. This score gives the probability of a MDD diagnosis and allows the tuning of specificity and sensitivity for individual patients to bring personalized medicine closer in psychiatry.