ABSTRACT
Neuroblastoma is a common childhood tumor and accounts for 15% of pediatric cancer deaths. To investigate the microRNA (miRNA) profile and role of Dicer and Drosha in neuroblastoma, we assessed the expression of 162 human miRNAs, Dicer and Drosha in 66 neuroblastoma tumors by using real-time PCR methods. We found global downregulation of miRNA expression in advanced neuroblastoma and identified 27 miRNAs that can clearly distinguish low- from high-risk patients. Furthermore, expression levels of Dicer or Drosha were low in high-risk neuroblastoma tumors, which accounted for global downregulation of miRNAs in advanced disease and correlated with poor outcome. Notably, for patients with non-MYCN-amplified tumors, low expression of Dicer can serve as a significant and independent predictor of poor outcome (hazard ratio, 9.6; P = 0.045; n = 52). Using plausible neural networks to select a combination of 15 biomarkers that consist of 12 miRNAs' signature, expression levels of Dicer and Drosha, and age at diagnosis, we were able to segregate all patients into four distinct patterns that were highly predictive of clinical outcome. In vitro studies also showed that knockdown of either Dicer or Drosha promoted the growth of neuroblastoma cell lines. Our results reveal that a combination of 15 biomarkers can delineate risk groups of neuroblastoma and serve as a powerful predictor of clinical outcome. Moreover, our findings of growth promotion by silencing Dicer/Drosha implied their potential use as therapeutic targets for neuroblastoma.
Subject(s)
Gene Silencing , MicroRNAs/genetics , Neuroblastoma/genetics , Ribonuclease III/genetics , Cell Line, Tumor , Colony-Forming Units Assay , DNA Primers , Gene Expression , Gene Expression Regulation, Enzymologic , Gene Expression Regulation, Neoplastic , Humans , Neuroblastoma/enzymology , Neuroblastoma/epidemiology , Plasmids , Polymerase Chain Reaction/methods , Predictive Value of Tests , Prognosis , RNA, Messenger/genetics , RNA, Neoplasm/genetics , Ribonuclease III/deficiency , Risk Assessment , TransfectionABSTRACT
Biomarkers for various diseases have been extensively searched for the past 5â years. Nevertheless, most efforts were focused on the search for protein biomarkers from serum samples. In this work, we tried to look for peptide biomarkers from gastric juice samples with MALDI-TOF-MS. More than 200 gastric juice samples from healthy people, gastric ulcer patients, duodenal ulcer patients, and cancer patients were examined. There were clear pattern differences of mass spectra among samples from healthy people and patients with different gastric diseases. We found five peptides for gastric cancer diagnosis with high sensitivity and specificity. Sequences of these five peptides, including two pepsinogen fragments, leucine zipper protein fragment, albumin fragment, and α-1-antitrypsin fragment, have been identified by mass spectrometric analysis and immuno-deplete assay with antibodies.
ABSTRACT
Biological sequence usually contains yet to find knowledge, and mining biological sequences usually involves a huge dataset and long computation time. Common tasks for biological sequence mining are pattern discovery, classification and clustering. The newly developed model, Plausible Neural Network (PNN), provides an intuitive and unified architecture for such a large dataset analysis. This paper introduces the basic concepts of the PNN, and explains how it is applied to biological sequence mining. The specific task of biological sequence mining, exon/intron prediction, is implemented by using PNN. The experimental results show the capability of solving biological sequence mining tasks using PNN.
