Resveratrol inhibits MUC5AC expression by regulating SPDEF in lung cancer cells.

BACKGROUND: MUC5AC was recently identified to play important roles in the proliferation and metastasis of malignant mucinous lung tumor cells. Resveratrol (Res), a natural compound with anticancer effects in lung cancer cells, has been reported to inhibit mucin production in airway epithelial cells. This study aimed to investigate the inhibitory effect of Res on MUC5AC expression in lung mucinous adenocarcinoma cells and the potential mechanisms. METHODS: Mucus-producing A549 human lung carcinoma cells were used to test the effects of Res on SPDEF and MUC5AC expression. Gene and protein expression was assessed by real-time quantitative PCR (qPCR), immunofluorescence and western blotting assays. SPDEF lentivirus was used to upregulate SPDEF expression levels in mucus-producing A549 human lung carcinoma cells. Cell proliferation was assessed by Cell Counting Kit-8 (CCK-8) assay. RESULTS: Res decreased MUC5AC expression in an SPDEF-dependent manner in mucus-producing A549 human lung carcinoma cells, and this change was accompanied by decreased ERK expression and AKT pathway activation. Moreover, SPDEF was found to be overexpressed in lung adenocarcinoma (LUAD), especially in mucinous adenocarcinoma. In-vitro functional assays showed that overexpression of SPDEF reduced the chemosensitivity of A549 cells to cisplatin (DDP). In addition, Res treatment increased A549 cell chemosensitivity to DDP by inhibiting the SPDEF-MUC5AC axis. CONCLUSION: Our results indicate that the SPDEF-MUC5AC axis is associated with DDP sensitivity, and that Res decreases SPDEF and MUC5AC expression by inhibiting ERK and AKT signaling in A549 cells, which provides a potential pharmacotherapy for the prevention and therapeutic management of mucinous adenocarcinoma.

Characterization of the volatile components in green tea by IRAE-HS-SPME/GC-MS combined with multivariate analysis.

In the present work, a novel infrared-assisted extraction coupled to headspace solid-phase microextraction (IRAE-HS-SPME) followed by gas chromatography-mass spectrometry (GC-MS) was developed for rapid determination of the volatile components in green tea. The extraction parameters such as fiber type, sample amount, infrared power, extraction time, and infrared lamp distance were optimized by orthogonal experimental design. Under optimum conditions, a total of 82 volatile compounds in 21 green tea samples from different geographical origins were identified. Compared with classical water-bath heating, the proposed technique has remarkable advantages of considerably reducing the analytical time and high efficiency. In addition, an effective classification of green teas based on their volatile profiles was achieved by partial least square-discriminant analysis (PLS-DA) and hierarchical clustering analysis (HCA). Furthermore, the application of a dual criterion based on the variable importance in the projection (VIP) values of the PLS-DA models and on the category from one-way univariate analysis (ANOVA) allowed the identification of 12 potential volatile markers, which were considered to make the most important contribution to the discrimination of the samples. The results suggest that IRAE-HS-SPME/GC-MS technique combined with multivariate analysis offers a valuable tool to assess geographical traceability of different tea varieties.