ArticleBMC cancer2018
A 35-gene signature discriminates between rapidly- and slowly-progressing glioblastoma multiforme and predicts survival in known subtypes of the cancer.
Article in BMC cancer, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Multi-omics dissection of FOXA1 reveals cancer-type-specific transcriptional networks and functional risk variants.iScience · 2026Article
- Artificial Intelligence-Driven Multi-Omics Approaches in Glioblastoma.International journal of molecular sciences · 2025Review
- M6A RNA methylation modification and tumor immune microenvironment in lung adenocarcinoma.Biophysics reports · 2023Article
- Integrative genomic analysis facilitates precision strategies for glioblastoma treatment.iScience · 2022Article
- Multimetric feature selection for analyzing multicategory outcomes of colorectal cancer: random forest and multinomial logistic regression models.Laboratory investigation; a journal of technical methods and pathology · 2022Article
- Parvifloron D-based potential therapy for glioblastoma: Inducing apoptosisFrontiers in pharmacology · 2022Article
- Identification of a Five-mRNA Signature as a Novel Potential Prognostic Biomarker for Glioblastoma by Integrative Analysis.Frontiers in genetics · 2022Article
- Bioinformatics analysis reveals biomarkers with cancer stem cell characteristics in kidney renal clear cell carcinoma.Translational andrology and urology · 2021Article
- Machine learning analysis of TCGA cancer data.PeerJ. Computer science · 2021Article
- Molecular and Clinical Characterization of UBE2S in Glioma as a Biomarker for Poor Prognosis and Resistance to Chemo-Radiotherapy.Frontiers in oncology · 2021Article
- An integrated approach to biomarker discovery reveals gene signatures highly predictive of cancer progression.Scientific reports · 2020Article
- A seven-gene signature model predicts overall survival in kidney renal clear cell carcinoma.Hereditas · 2020Article
- Mechanistic models of signaling pathways deconvolute the glioblastoma single-cell functional landscape.NAR cancer · 2020Article
- Radiogenomic-Based Survival Risk Stratification of Tumor Habitat on Gd-T1w MRI Is Associated with Biological Processes in Glioblastoma.Clinical cancer research : an official journal of the American Association for Cancer Research · 2020Article
- A Serum Proteomic Signature Predicting Survival in Patients with Glioblastoma.Journal of biochemistry and analytical studies · 2020Article
- IRGS: an immune-related gene classifier for lung adenocarcinoma prognosis.Journal of translational medicine · 2020Article
- A Multi-Cohort and Multi-Omics Meta-Analysis Framework to Identify Network-Based Gene Signatures.Frontiers in genetics · 2019Article
- MicroRNAs, Hypoxia and the Stem-Like State as Contributors to Cancer Aggressiveness.Frontiers in genetics · 2019Review
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2 authors.
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Abstract
backgroundGene expression can be employed for the discovery of prognostic gene or multigene signatures cancer. In this study, we assessed the prognostic value of a 35-gene expression signature selected by pathway and machine learning based methods in adjuvant therapy-linked glioblastoma multiforme (GBM) patients from the Cancer Genome Atlas.
methodsGenes with high expression variance was subjected to pathway enrichment analysis and those having roles in chemoradioresistance pathways were used in expression-based feature selection. A modified Support Vector Machine Recursive Feature Elimination algorithm was employed to select a subset of these genes that discriminated between rapidly-progressing and slowly-progressing patients.
resultsSurvival analysis on TCGA samples not used in feature selection and samples from four GBM subclasses, as well as from an entirely independent study, showed that the 35-gene signature discriminated between the survival groups in all cases (p<0.05) and could accurately predict survival irrespective of the subtype. In a multivariate analysis, the signature predicted progression-free and overall survival independently of other factors considered.
conclusionWe propose that the performance of the signature makes it an attractive candidate for further studies to assess its utility as a clinical prognostic and predictive biomarker in GBM patients. Additionally, the signature genes may also be useful therapeutic targets to improve both progression-free and overall survival in GBM patients.
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