Long non-coding ribonucleic acid W5 inhibits progression and predicts favorable prognosis in hepatocellular carcinoma

BACKGROUND Accumulating evidence has revealed that several long non-coding ribonucleic acids(lncRNAs)are crucial in the progress of hepatocellular carcinoma(HCC).AIM To classify a long non-coding RNA,i.e.,lncRNA W5,and to determine the clinical significance and potential roles of lncRNA W5 in HCC.METHODS The results showed that lncRNA W5 expression was significantly downregulated in HCC cell lines and tissues.Analysis of the association between lncRNA W5 expression levels and clinicopathological features suggested that low lncRNA W5 expression was related to large tumor size(P<0.01),poor histological grade(P<0.05)and serious portal vein tumor thrombosis(P<0.05).Furthermore,Kaplan-Meier survival analysis showed that low expression of lncRNA W5 predicts poor overall survival(P=0.016).RESULTS Gain-of-loss function experiments,including cell counting kit8 assays,colony formation assays,and transwell assays,were performed in vitro to investigate thebiological roles of lncRNA W5.In vitro experiments showed that ectopic overexpression of lncRNA W5 suppressed HCC cell proliferation,migration and invasion;conversely,silencing of lncRNA W5 promoted cell proliferation,migration and invasion.In addition,acting as a tumor suppressor gene in HCC,lncRNA W5 inhibited the growth of HCC xenograft tumors in vivo.CONCLUSION These results showed that lncRNA W5 is down-regulated in HCC,and it may suppress HCC progression and predict poor clinical outcomes in patients with HCC.LncRNA W5 may serve as a potential HCC prognostic biomarker in addition to a therapeutic target.

Application and Exploration of Big Data Mining in Clinical Medicine

Objective： To review theories and technologies of big data mining and their application in clinical medicine. Data Sources： Literatures published in English or Chinese regarding theories and technologies of big data mining and the concrete applications of data mining technology in clinical medicine were obtained from PubMed and Chinese Hospital Knowledge Database from 1975 to 2015. Study Selection： Original articles regarding big data mining theory/technology and big data mining＇s application in the medical field were selected. Results： This review characterized the basic theories and technologies of big data mining including fuzzy theory, rough set theory, cloud theory, Dempster-Shafer theory, artificial neural network, genetic algorithm, inductive learning theory, Bayesian network, decision tree, pattern recognition, high-performance computing, and statistical analysis. The application of big data mining in clinical medicine was analyzed in the fields of disease risk assessment, clinical decision support, prediction of disease development, guidance of rational use of drugs, medical management, and evidence-based medicine. Conclusion： Big data mining has the potential to play an important role in clinical medicine.