ArticleJournal of cellular and molecular medicine2022
Identification of a glioma functional network from gene fitness data using machine learning.
Article in Journal of cellular and molecular medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed, 8 citations in OpenAlex.
- LRRC71 is essential for sperm motility, fertilization, and male fertility.The Journal of biological chemistry · 2026Article
- TENT5A Increases Glioma Malignancy and Promotes its Progression.Recent patents on anti-cancer drug discovery · 2025Article
- Identification and validation of the role of ZNF281 in 5-fluorouracil chemotherapy of gastric cancer.Journal of cancer research and clinical oncology · 2024Article
- Three's a crowd - why did three N-terminal methyltransferases evolve for one job?Journal of cell science · 2023Article
- Identification of a glioma functional network from gene fitness data using machine learning.Journal of cellular and molecular medicine · 2022Article
- Uses of artificial intelligence in glioma: A systematic review.Medicine internationalArticle
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Glioblastoma multiforme (GBM) is an aggressive form of brain tumours that remains incurable despite recent advances in clinical treatments. Previous studies have focused on sub-categorizing patient samples based on clustering various transcriptomic data. While functional genomics data are rapidly accumulating, there exist opportunities to leverage these data to decipher glioma-associated biomarkers. We sought to implement a systematic approach to integrating data from high throughput CRISPR-Cas9 screening studies with machine learning algorithms to infer a glioma functional network. We demonstrated the network significantly enriched various biological pathways and may play roles in glioma tumorigenesis. From densely connected glioma functional modules, we further predicted 12 potential Wnt/β-catenin signalling pathway targeted genes, including AARSD1, HOXB5, ITGA6, LRRC71, MED19, MED24, METTL11B, SMARCB1, SMARCE1, TAF6L, TENT5A and ZNF281. Cox regression modelling with these targets was significantly associated with glioma overall survival prognosis. Additionally, TRIB2 was identified as a glioma neoplastic cell marker in single-cell RNA-seq of GBM samples. This work establishes novel strategies for constructing functional networks to identify glioma biomarkers for the development of diagnosis and treatment in clinical practice.
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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 OpenQuestion graph.