OBJECTIVETo investigate the molecular mechanism by which LKB1 regulates epithelial-mesenchymal transition (EMT) in Peutz-Jeghers hamartoma and intestinal epithelial cells.

METHODSImmunohistochemistry was used to detect gene expression of LKB1, E-cadherin, and vimentin in 20 hamartoma tissues and 10 normal intestinal tissues, and collagen fiber deposition was analyzed using Masson trichrome staining. Normal intestinal epithelial NCM460 cells were transfected with LKB1 shRNA plasmid or negative control via lentiviral vectors, and the role of LKB1 in cell polarization and migration were determined using CCK8 and Transwell assays. Western blotting, quantitative real-time PCR (qPCR) and immunofluorescence were used to assess the alterations of EMT markers in the cells with LKB1 knockdown.

RESULTSCompared with normal intestinal tissues, hamartoma polyps showed significantly decreased LKB1 and E-cadherin expressions and increased vimentin expression with increased collagen fiber deposition. The cells with LKB1 knockdown exhibited enhanced cell proliferation and migration activities (P<0.01). Western blot analysis, qPCR and immunofluorescence all detected decreased E-cadherin and increased N-cadherin, vimentin, Snail, and Slug expressions in the cells with LKB1 knockdown.

CONCLUSIONs LKB1 deficiency triggers EMT in intestinal epithelial cells and Peutz-Jeghers hamartoma, suggesting that EMT can serve as the therapeutic target for treatment of Peutz-Jeghers syndrome.

Objective Alzheimer's disease (AD) is the most common cause of dementia. The pathophysiology of the disease mostly remains unearthed, thereby challenging drug development for AD. This study aims to screen high throughput gene expression data using weighted co-expression network analysis (WGCNA) to explore the potential therapeutic targets.Methods The dataset of GSE36980 was obtained from the Gene Expression Omnibus (GEO) database. Normalization, quality control, filtration, and soft-threshold calculation were carried out before clustering the co-expressed genes into different modules. Furthermore, the correlation coefficients between the modules and clinical traits were computed to identify the key modules. Gene ontology and pathway enrichment analyses were performed on the key module genes. The STRING database was used to construct the protein-protein interaction (PPI) networks, which were further analyzed by Cytoscape app (MCODE). Finally, validation of hub genes was conducted by external GEO datasets of GSE 1297 and GSE 28146.Results Co-expressed genes were clustered into 27 modules, among which 6 modules were identified as the key module relating to AD occurrence. These key modules are primarily involved in chemical synaptic transmission (GO:0007268), the tricarboxylic acid (TCA) cycle and respiratory electron transport (R-HSA-1428517).