ArticleFrontiers in molecular biosciences2021
Prognostic Value of mRNAsi/Corrected mRNAsi Calculated by the One-Class Logistic Regression Machine-Learning Algorithm in Glioblastoma Within Multiple Datasets.
Article in Frontiers in molecular biosciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Construction and validation of risk prediction model for glioblastoma associated with cancer stem cells and disulfidptosis.Translational cancer research · 2026Article
- USP10 stabilizes BAZ1A to drive tumor stemness via an epigenetic mechanism in head and neck squamous cell carcinoma.Cell death & disease · 2025Article
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- Metabolic pathway-based molecular subtyping of colon cancer reveals clinical immunotherapy potential and prognosis.Journal of cancer research and clinical oncology · 2023Article
- Construction and Validation of a Prognostic Model Based on mRNAsi-Related Genes in Breast Cancer.Computational and mathematical methods in medicine · 2022Article
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9 authors.
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Abstract
Glioblastoma (GBM) is the most common glial tumour and has extremely poor prognosis. GBM stem-like cells drive tumorigenesis and progression. However, a systematic assessment of stemness indices and their association with immunological properties in GBM is lacking. We collected 874 GBM samples from four GBM cohorts (TCGA, CGGA, GSE4412, and GSE13041) and calculated the mRNA expression-based stemness indices (mRNAsi) and corrected mRNAsi (c_mRNAsi, mRNAsi/tumour purity) with OCLR algorithm. Then, mRNAsi/c_mRNAsi were used to quantify the stemness traits that correlated significantly with prognosis. Additionally, confounding variables were identified. We used discrimination, calibration, and model improvement capability to evaluate the established models. Finally, the
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