ArticleScientific reports2022
Machine learning and bioinformatics approaches for classification and clinical detection of bevacizumab responsive glioblastoma subtypes based on miRNA expression.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.
- Investigating glioblastoma chemoresistance: a meta-analysis of microRNA signatures and gene networks.Journal of neuro-oncology · 2025Pooled it
- Identification of drug candidates against glioblastoma with machine learning and high-throughput screening of heterogeneous cellular models.Digital discovery · 2026Article
- Article
- Role of matrix metalloproteinases in the invasion of glioblastoma and drug interventions (Review).International journal of molecular medicine · 2026Review
- Artificial Intelligence-Driven Multi-Omics Approaches in Glioblastoma.International journal of molecular sciences · 2025Review
- Key genes altered in glioblastoma based on bioinformatics (Review).Oncology letters · 2025Review
- Article
- Comparative Insight into Microglia/Macrophages-Associated Pathways in Glioblastoma and Alzheimer's Disease.International journal of molecular sciences · 2023Review
- Review
Corrections and comments
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Authors and funding
1 author at 1 institution in 1 country.
Funding
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Abstract
For the precise treatment of patients with glioblastoma multiforme (GBM), we classified and detected bevacizumab (BVZ)-responsive subtypes of GBM and found their differential expression (DE) of miRNAs and mRNAs, clinical characteristics, and related functional pathways. Based on miR-21 and miR-10b expression z-scores, approximately 30% of GBM patients were classified as having the GBM BVZ-responsive subtype. For this subtype, GBM patients had a significantly shorter survival time than other GBM patients (p = 0.014), and vascular endothelial growth factor A (VEGF) methylation was significantly lower than that in other GBM patients (p = 0.005). It also revealed 14 DE miRNAs and 7 DE mRNAs and revealed functional characteristics between GBM BVZ subgroups. After comparing several machine learning algorithms, the construction and cross-validation of the SVM classifier were performed. For clinical use, miR-197 was optimized and added to the miRNA panel for better classification. Afterwards, we validated the classifier with several GBM datasets and discovered some key related issues. According to this study, GBM BVZ subtypes can be classified and detected by a combination of SVM classifiers and miRNA panels in existing tissue GBM datasets. With certain modifications, the classifier may be used for the classification and detection of GBM BVZ subtypes for future clinical use.
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