Identification of five important genes to predict glioblastoma subtypes. Issue 1 (10th October 2021)

• Record Type: Journal Article
• Authors: Tang, Yang; Qazi, Maleeha A; Brown, Kevin R; Mikolajewicz, Nicholas; Moffat, Jason; Singh, Sheila K; McNicholas, Paul D
• Is Part Of: Neuro-oncology advances. Volume 3:Issue 1(2021)
• Publication Date: 2021-10-10
• Subjects: classification -- gene signature -- glioblastoma subtypes -- statistical learning -- XGBoost; 616.99481
• DOI: 10.1093/noajnl/vdab144
• Languages: English
• ISSNs: 2632-2498

Abstract

Abstract: Background: Glioblastoma (GBM), the most common and aggressive primary brain tumour in adults, has been classified into three subtypes: classical, mesenchymal, and proneural. While the original classification relied on an 840 gene-set, further clarification on true GBM subtypes uses a 150-gene signature to accurately classify GBM into the three subtypes. We hypothesized whether a machine learning approach could be used to identify a smaller gene-set to accurately predict GBM subtype. Methods: Using a supervised machine learning approach, extreme gradient boosting (XGBoost), we developed a classifier to predict the three subtypes of glioblastoma (GBM): classical, mesenchymal, and proneural. We tested the classifier on in-house GBM tissue, cell lines, and xenograft samples to predict their subtype. Results: We identified the five most important genes for characterizing the three subtypes based on genes that often exhibited high Importance Scores in our XGBoost analyses. On average, this approach achieved 80.12% accuracy in predicting these three subtypes of GBM. Furthermore, we applied our five-gene classifier to successfully predict the subtype of GBM samples at our centre. Conclusion: Our 5-gene set classifier is the smallest classifier to date that can predict GBM subtypes with high accuracy, which could facilitate the future development of a five-gene subtype diagnostic biomarker for routine assays in GBM samples.