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However, detection of isocitrate dehydrogenase (<jats:italic>IDH<\/jats:italic>) mutation and chromosome1p\/19q codeletion, biomarkers to classify gliomas, is time- and cost-intensive and diagnostic discordance remains an issue. Adenosine to inosine (A-to-I) RNA editing has emerged as a novel cancer prognostic marker, but its value for glioma classification remains largely unexplored. We aim to (1) unravel the relationship between RNA editing and <jats:italic>IDH<\/jats:italic> mutation and 1p\/19q codeletion and (2) predict <jats:italic>IDH<\/jats:italic> mutation and 1p\/19q codeletion status using machine learning algorithms.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Results<\/jats:title>\n<jats:p>By characterizing genome-wide A-to-I RNA editing signatures of 638 gliomas, we found that tumors without <jats:italic>IDH<\/jats:italic> mutation exhibited higher total editing level compared with those carrying it (Kolmogorov-Smirnov test, <jats:italic>p<\/jats:italic>\u2009&lt;\u20090.0001). When tumor grade was considered, however, only grade IV tumors without <jats:italic>IDH<\/jats:italic> mutation exhibited higher total editing level. According to 10-fold cross-validation, support vector machines (SVM) outperformed random forest and AdaBoost (DeLong test, <jats:italic>p<\/jats:italic>\u2009&lt;\u20090.05). The area under the receiver operating characteristic curve (AUC) of SVM in predicting <jats:italic>IDH<\/jats:italic> mutation and 1p\/19q codeletion were 0.989 and 0.990, respectively. After performing feature selection, AUCs of SVM and AdaBoost in predicting <jats:italic>IDH<\/jats:italic> mutation were higher than that of random forest (0.985 and 0.983 vs. 0.977; DeLong test, <jats:italic>p<\/jats:italic>\u2009&lt;\u20090.05), but AUCs of the three algorithms in predicting 1p\/19q codeletion were similar (0.976\u20130.982). Furthermore, 67% of the six continuously misclassified samples by our 1p\/19q codeletion prediction models were misclassifications in the original labelling after inspection of 1p\/19q status and\/or pathology report, highlighting the accuracy and clinical utility of our models.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Conclusions<\/jats:title>\n<jats:p>The study represents the first genome-wide analysis of glioma editome and identifies RNA editing as a novel prognostic biomarker for glioma. Our prediction models provide standardized, accurate, reproducible and objective classification of gliomas. Our models are not only useful in clinical decision-making, but also able to identify editing events that have the potential to serve as biomarkers and therapeutic targets in glioma management and treatment.<\/jats:p>\n<\/jats:sec>","DOI":"10.1186\/s12859-019-3236-0","type":"journal-article","created":{"date-parts":[[2019,12,24]],"date-time":"2019-12-24T02:02:43Z","timestamp":1577152963000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["RNA editing-based classification of diffuse gliomas: predicting isocitrate dehydrogenase mutation and chromosome 1p\/19q codeletion"],"prefix":"10.1186","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2030-7822","authenticated-orcid":false,"given":"Sean Chun-Chang","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chung-Ming","family":"Lo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shih-Hua","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emily Chia-Yu","family":"Su","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,12,24]]},"reference":[{"issue":"1","key":"3236_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3988\/jcn.2019.15.1.1","volume":"15","author":"M Chammas","year":"2019","unstructured":"Chammas M, Saadeh F, Maaliki M, Assi H. 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