LncRNA STARD13-AS Expression in Gastric Cancer and its Significance.

BACKGROUND: The aim of the study is to investigate lncRNA STARD13-AS's expression and clinical characteristics in the cancer tissues of gastric cancer (GC) patients. METHODS: Microarray assessment will be used to analyze GC and non-tumor tissues. The lncRNA STARD13-AS expression in 82 GC and non-tumor tissues were measured by RT-qPCR and ISH assay. The differential expression of lncRNA STARD13-AS between the two groups and the relationship with clinical characteristics and prognosis were analyzed statistically. RESULTS: By microarray assessment, lncRNA STARD13-AS was significantly down-regulated in tumor tissues. Compared with non-tumor tissues, using both RT-qPCR and ISH assay, STARD13-AS expression was significantly depressed in GC tissues (p < 0.01, respectively) and was closely correlated with tumor stage and histological grade (p < 0.05, respectively), but was not correlated with gender, age, tumor size, tumor location or lymph node metastasis. The 5-year overall survival and progression free survival rates of the low lncRNA STARD13-AS expression group were significantly down-regulated compared with those of the high lncRNA STARD13-AS expression group (p < 0.001, respectively). CONCLUSIONS: The expression of lncRNA STARD-AS is depressed in gastric cancer tissues; the expression of lnc-RNA STARD-AS is correlated to tumor stage and degree of tumor differentiation, and may be a new molecular marker for the diagnosis, treatment, and prognosis in GC.

Discovering potential WRN inhibitors from natural product database through computational methods.

Microsatellite instability (MSI) is a relatively common feature associated with multiple cancers, and Werner syndrome (WRN) ATP-dependent helicase has been recognized as a novel target for treating MSI cancers, such as colorectal cancer. A small-molecule inhibitor targeting WRN would be a promising strategy for treating colorectal cancer with high MSI expression. In this study, we employed a computer-assisted drug discovery strategy to screen over 30,000 natural product molecules. By using a combination of docking, ligand efficiency, Molecular Mechanics/Generalized Born Surface Area (MM/GBSA), and thermodynamic integration (TI) calculations, we identified MOL008980, MOL010740, MOL011832, T4743, TN1166, and TNP-002173 as potential WRN inhibitors. Subsequent molecular dynamics simulation revealed that these screened natural products possessed better binding dynamic characteristics than ATP substrate and were capable of inhibiting the dynamic process of WRN, making them potential strong ATP competitive inhibitors. In conclusion, our computational approach revealed potential WRN inhibitors from a natural product database, providing a theoretical basis for future research.