ArticleJournal of translational medicine2021
Gene clusters based on OLIG2 and CD276 could distinguish molecular profiling in glioblastoma.
Article in Journal of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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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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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it, 4 citations in OpenAlex.
- mRNA markers for survival prediction in glioblastoma multiforme patients: a systematic review with bioinformatic analyses.BMC cancer · 2024Pooled it
- MiRNAs from the Dlk1-Dio3 locus and miR-224/452 cluster contribute to glioblastoma tumor heterogeneity.Scientific reports · 2024Article
- Cell Cycle-Related FAM64A Could be Activated by TGF-β Signaling to Promote Glioma Progression.Cellular and molecular neurobiology · 2023Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundThe molecular profiling of glioblastoma (GBM) based on transcriptomic analysis could provide precise treatment and prognosis. However, current subtyping (classic, mesenchymal, neural, proneural) is time-consuming and cost-intensive hindering its clinical application. A simple and efficient method for classification was imperative.
methodsIn this study, to simplify GBM subtyping more efficiently, we applied a random forest algorithm to conduct 26 genes as a cluster featured with hub genes, OLIG2 and CD276. Functional enrichment analysis and Protein-protein interaction were performed using the genes in this gene cluster. The classification efficiency of the gene cluster was validated by WGCNA and LASSO algorithms, and tested in GSE84010 and Gravandeel's GBM datasets.
resultsThe gene cluster (n = 26) could distinguish mesenchymal and proneural excellently (AUC = 0.92), which could be validated by multiple algorithms (WGCNA, LASSO) and datasets (GSE84010 and Gravandeel's GBM dataset). The gene cluster could be functionally enriched in DNA elements and T cell associated pathways. Additionally, five genes in the signature could predict the prognosis well (p = 0.0051 for training cohort, p = 0.065 for test cohort).
conclusionsOur study proved the accuracy and efficiency of random forest classifier for GBM subtyping, which could provide a convenient and efficient method for subtyping Proneural and Mesenchymal GBM.
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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.