The SMART App: an interactive web application for comprehensive DNA methylation analysis and visualization.

Background: Data mining of The Cancer Genome Atlas (TCGA) data has significantly facilitated cancer genome research and provided unprecedented opportunities for cancer researchers. However, existing web applications for DNA methylation analysis does not adequately address the need of experimental biologists, and many additional functions are often required.

Results: To facilitate DNA methylation analysis, we present the SMART (Shiny Methylation Analysis Resource Tool) App, a user-friendly and easy-to-use web application for comprehensively analyzing the DNA methylation data of TCGA project.

A large cohort study identifying a novel prognosis prediction model for lung adenocarcinoma through machine learning strategies.

Background: Predicting lung adenocarcinoma (LUAD) risk is crucial in determining further treatment strategies. Molecular biomarkers may improve risk stratification for LUAD.

Methods: We analyzed the gene expression profiles of LUAD patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO).

Regulation of CXCR4/AKT-signaling-induced cell invasion and tumor metastasis by RhoA, Rac-1, and Cdc42 in human esophageal cancer.

CXC chemokines and their cognate receptors have been implicated wildly in cancer pathogenesis. In the present study, we report a critical cause relationship between CXCR4 expression and tumorigenesis in the setting of human esophageal squamous cell carcinoma (ESCC). In ESCC cells, CXCR4 expression was significantly higher than in human esophageal epithelial cells (HEEC).