Development of microRNA therapeutics is coming of age

Current and future targets of antiviral therapy in the hepatitis C virus life cycle

ABSTRACT 

Advances in our understanding of the hepatitis C virus (HCV) life cycle have enabled the development of numerous clinically advanced direct-acting antivirals. Indeed, the recent approval of first-generation direct-acting antivirals that target the viral NS3–4A protease and NS5B RNA-dependent RNA polymerase brings closer the possibility of universally efficacious and well-tolerated antiviral therapies for this insidious infection. However, the complexities of comorbidities, unforeseen side effects or drug–drug interactions, viral diversity, the high mutation rate of HCV RNA replication and the elegant and constantly evolving mechanisms employed by HCV to evade host and therapeutically implemented antiviral strategies remain as significant obstacles to this goal. Here, we review advances in our understanding of the HCV life cycle and associated opportunities for antiviral therapy.

Neural network cascade optimizes microRNA biomarker selection for nasopharyngeal cancer prognosis

MicroRNAs (miRNAs) have been shown to be promising biomarkers in predicting cancer prognosis. However, inappropriate or poorly optimized processing and modeling of miRNA expression data can negatively affect prediction performance. Here, we propose a holistic solution for miRNA biomarker selection and prediction model building. This work introduces the use of a neural network cascade, a cascaded constitution of small artificial neural network units, for evaluating miRNA expression and patient outcome. A miRNA microarray dataset of nasopharyngeal carcinoma was retrieved from Gene Expression Omnibus to illustrate the methodology. Results indicated a nonlinear relationship between miRNA expression and patient death risk, implying that direct comparison of expression values is inappropriate. However, this method performs transformation of miRNA expression values into a miRNA score, which linearly measures death risk. Spearman correlation was calculated between miRNA scores and survival status for each miRNA. Finally, a nine-miRNA signature was optimized to predict death risk after nasopharyngeal carcinoma by establishing a neural network cascade consisting of 13 artificial neural network units. Area under the ROC was 0.951 for the internal validation set and had a prediction accuracy of 83% for the external validation set. In particular, the established neural network cascade was found to have strong immunity against noise interference that disturbs miRNA expression values. This study provides an efficient and easy-to-use method that aims to maximize clinical application of miRNAs in prognostic risk assessment of patients with cancer.

Combined downregulation of microRNA-133a and microRNA-133b predicts chemosensitivity of patients with esophageal squamous cell carcinoma undergoing paclitaxel-based chemotherapy

microRNA-133a (miR-133a) and miR-133b, located on chromosome 18 in the same bicistronic unit, have been commonly identified as being downregulated in esophageal squamous cell carcinoma (ESCC). The aim of this study was to investigate the correlation of miR-133a/b expression with efficacy of paclitaxel-based chemotherapy and clinical outcome of ESCC patients. miR-133a expression and miR-133b expression were examined in 100 newly diagnosed ESCC patients prior to treatment by quantitative real-time PCR. Then, the patients received four cycles of paclitaxel-based chemotherapy, the short-term treatment efficacy was evaluated, and a 3-year follow-up was performed. Expression levels of miR-133a and miR-133b were both significantly lower in ESCC tissues compared to adjacent noncancerous tissues (both P < 0.001). In addition, combined miR-133a/b downregulation was found to be closely correlated with advanced tumor stage (P = 0.02) and poor differentiation (P = 0.01). Moreover, the response rate of ESCC patients to paclitaxel-based chemotherapy was significantly higher in combined miR-133a/b downregulation group compared with other groups (P = 0.02). Furthermore, univariate and multivariate Cox analyses revealed that tumor stage and combined expression of miR-133a/b were independent prognosis factors in ESCC patients. Our data offer the convincing evidence that combined expression of miR-133a and miR-133b may predict chemosensitivity of patients with ESCC undergoing paclitaxel-based chemotherapy, implying its importance in applying 'personalized cancer medicine' in the clinical treatment of ESCC. We also identified combined expression of miR-133a and miR-133b as an effective prognostic marker of this malignancy.