ArticleScientific reports2024
Machine learning predicts cuproptosis-related lncRNAs and survival in glioma patients.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Comprehensive analysis of cuproptosis-related lncRNAs in immunotherapy response and prognosis in papillary thyroid cancer.Gland surgery · 2026Article
- PSMC4 promotes low-grade glioma progression and predicts poor prognosis.European journal of medical research · 2026Article
- Copper-Iron Cell Death Axis: Mechanistic Crosstalk, Disease Implications and an Integrated Metallo-Redox-Metabolic Framework.International journal of biological sciences · 2026Review
- Review
- A novel approach to enhance glioblastoma multiforme treatment efficacy: non-coding RNA targeted therapy and adjuvant approaches.Clinical epigenetics · 2025Review
- Multidimensional analysis reveals gene expression, cell interactions, and signaling networks in glioma and Alzheimer's disease.Discover oncology · 2025Article
- Long Non-Coding RNAs in Malignant Human Brain Tumors: Driving Forces Behind Progression and Therapy.International journal of molecular sciences · 2025Review
- Machine learning-based prediction of glioma grading.PloS one · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gliomas are the most common tumor in the central nervous system in adults, with glioblastoma (GBM) representing the most malignant form, while low-grade glioma (LGG) is a less severe. The prognosis for glioma remains poor even after various treatments, such as chemotherapy and immunotherapy. Cuproptosis is a newly defined form of programmed cell death, distinct from ferroptosis and apoptosis, primarily caused by the accumulation of the copper within cells. In this study, we compared the difference between the expression of cuproptosis-related genes in GBM and LGG, respectively, and conducted further analysis on the enrichment pathways of the exclusive expressed cuproptosis-related mRNAs in GBM and LGG. We established two prediction models for survival status using xgboost and random forest algorithms and applied the ROSE algorithm to balance the dataset to improve model performance.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.