Laryngeal squamous cell carcinoma (LSCC) is a highly aggressive malignant cancer. The regulation of LSCC progression by long non-coding RNA (lncRNA) was not well understood. In this study, we reported that the lncRNA H19 was upregulated in LSCC. The expression levels of H19 were inversely correlated with the survival rate of LSCC patients. Knockdown of H19 expression inhibited LSCC cell migration, invasion and proliferation. We identified microRNA miR-148a-3p as an inhibitory target for H19. Overexpression of miR-148a-3p reduced LSCC migration, invasion and proliferation cell, while inhibition of miR-148a-3p did the opposite. The inhibition of LSCC progression induced by H19 knockdown required the activity of miR-148a-3p. We also identified DNA methyltransferase enzyme DNMT1 as a target of miR-148a-3p. Cellular DNA methylation levels were inhibited by both miR-148a-3p overexpression and H19 knockdown. In summary, our study demonstrated that the lncRNA H19 promoted LSCC progression via miR-148a-3p and DNMT1.
The expression of OPN and ITGAV was significantly influenced to the differentiation and metastasis of the LHSCC. Overexpression of the OPN and ITGAV may have contributed to invasion and metastasis of the LHSCC, and therefore, OPN and ITGAV may have value as a target for chemotherapy in LHSCC treatment.
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.
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