Machine learning revealed stemness features and a novel stemness-based classification with appealing implications in discriminating the prognosis, immunotherapy and temozolomide responses of 906 glioblastoma patients

• Published in: Briefings in bioinformatics Vol. 22; no. 5
• Main Authors: Wang, Zihao, Wang, Yaning, Yang, Tianrui, Xing, Hao, Wang, Yuekun, Gao, Lu, Guo, Xiaopeng, Xing, Bing, Wang, Yu, Ma, Wenbin
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 02.09.2021
• Subjects: Problem Solving Protocol; stemness subgroup; integrated multiomic analysis; glioblastoma; mRNAsi; immunotherapy

Glioblastoma (GBM) is the most malignant and lethal intracranial tumor, with extremely limited treatment options. Immunotherapy has been widely studied in GBM, but none can significantly prolong the overall survival (OS) of patients without selection. Considering that GBM cancer stem cells (CSCs) pl...