{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T15:07:02Z","timestamp":1764688022959},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,10,11]],"date-time":"2022-10-11T00:00:00Z","timestamp":1665446400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,10,11]],"date-time":"2022-10-11T00:00:00Z","timestamp":1665446400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Gliomas are highly complex and heterogeneous tumors, rendering prognosis prediction challenging. The advent of deep learning algorithms and the accessibility of multi-omic data represent a new approach for the identification of survival-sensitive subtypes. Herein, an autoencoder-based approach was used to identify two survival-sensitive subtypes using RNA sequencing (RNA-seq) and DNA methylation (DNAm) data from The Cancer Genome Atlas (TCGA) dataset. The subtypes were used as labels to build a support vector machine model with cross-validation. We validated the robustness of the model on Chinese Glioma Genome Atlas (CGGA) dataset. DNAm-driven genes were identified by integrating DNAm and gene expression profiling analyses using the R MethylMix package and carried out for further enrichment analysis.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>For TCGA dataset, the model produced a high C-index (0.92\u2009\u00b1\u20090.02), low brier score (0.16\u2009\u00b1\u20090.02), and significant log-rank <jats:italic>p<\/jats:italic> value (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.0001). The model also had a decent performance for CGGA dataset (CGGA DNAm: C-index of 0.70, brier score of 0.21; CGGA RNA-seq: C-index of 0.79, brier score of 0.18). Moreover, we identified 389 DNAm-driven genes of survival-sensitive subtypes, which were significantly enriched in the glutathione metabolism pathway.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Our study identified two survival-sensitive subtypes of glioma and provided insights into the molecular mechanisms underlying glioma development; thus, potentially providing a new target for the prognostic prediction of gliomas and supporting personalized treatment strategies.\n<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-022-04970-x","type":"journal-article","created":{"date-parts":[[2022,10,11]],"date-time":"2022-10-11T10:03:11Z","timestamp":1665482591000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Deep learning algorithm reveals two prognostic subtypes in patients with gliomas"],"prefix":"10.1186","volume":"23","author":[{"given":"Jing","family":"Tian","sequence":"first","affiliation":[]},{"given":"Mingzhen","family":"Zhu","sequence":"additional","affiliation":[]},{"given":"Zijing","family":"Ren","sequence":"additional","affiliation":[]},{"given":"Qiang","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Puqing","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Colin K.","family":"He","sequence":"additional","affiliation":[]},{"given":"Min","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xiaochun","family":"Peng","sequence":"additional","affiliation":[]},{"given":"Beilei","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Rujia","family":"Feng","sequence":"additional","affiliation":[]},{"given":"Minglong","family":"Fu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,11]]},"reference":[{"key":"4970_CR1","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-642-02874-8_1","volume-title":"Oncology of CNS tumors","author":"G Reifenberger","year":"2010","unstructured":"Reifenberger G, Bl\u00fcmcke I, Pietsch T, Paulus W. 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Ethics approval and the requirement for informed consent for this study were waived as the data were obtained from public open-access databases.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"417"}}