Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Cancer-Critical Genes II: Tumor Suppressor Genes

Cancer-Critical Genes II: Tumor Suppressor Genes

Genes usually encode proteins necessary for the proper functioning of a healthy cell. Mutations can often cause changes to the gene expression pattern, thereby altering the phenotype.
When the function of certain critical genes, especially those involved in cell cycle regulation and cell growth signaling cascades, gets disrupted, it upsets the cell cycle progression. Such cells with unchecked cell cycles start proliferating uncontrollably and eventually develop into tumors.
Such genes that act...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Comment on: miR-129-5p Modulates Fndc5 Expression in Adipose Tissue of Diabetic Mice.

Cell journal·2026

Same author

Eight-color multiparameter flow cytometry (EuroFlow-NGF) is as sensitive as next-generation sequencing in detecting minimal/measurable residual disease in autografts of patients with multiple myeloma.

EJHaem·2023

Same author

Marine Microbial-Derived Resource Exploration: Uncovering the Hidden Potential of Marine Carotenoids.

Marine drugs·2022

See all related articles

Search research articles

Related Experiment Video

Updated: May 5, 2026

Characterization of Functionally Associated miRNAs in Glioblastoma and their Engineering into Artificial Clusters for Gene Therapy

Characterization of Functionally Associated miRNAs in Glioblastoma and their Engineering into Artificial Clusters for Gene Therapy

Published on: October 4, 2019

Identification of Key Genes Associated with Overall Survival in Glioblastoma Multiforme Using TCGA RNA-Seq Expression

Lilies Handayani^1,2, Denis Chegodaev¹, Ray Steven¹

¹Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa 9201192, Japan.

|July 29, 2025

Summary

Deep learning models effectively identified key genes for glioblastoma multiforme (GBM) survival prediction, outperforming traditional machine learning. These findings offer novel biomarkers for this aggressive brain cancer.

Keywords:

Cox regression RNA-Seq biomarkers deep learning gene network analysis glioblastoma multiforme machine learning survival analysis

More Related Videos

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Published on: October 11, 2019

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres

Published on: July 22, 2020

Related Experiment Videos

Last Updated: May 5, 2026

Characterization of Functionally Associated miRNAs in Glioblastoma and their Engineering into Artificial Clusters for Gene Therapy

Characterization of Functionally Associated miRNAs in Glioblastoma and their Engineering into Artificial Clusters for Gene Therapy

Published on: October 4, 2019

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Published on: October 11, 2019

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres

Published on: July 22, 2020

Area of Science:

Oncology
Bioinformatics
Genomics

Background:

Glioblastoma multiforme (GBM) is an aggressive brain tumor with a poor prognosis.
Identifying reliable molecular biomarkers is crucial for patient stratification and treatment.
Current treatment strategies require improved prognostic tools.

Purpose of the Study:

To identify key genes associated with overall survival in GBM.
To compare the efficacy of machine learning (ML) and deep learning (DL) approaches for GBM survival prediction.
To discover novel prognostic gene expression biomarkers for GBM.

Main Methods:

Utilized RNA-Seq gene expression and clinical data from The Cancer Genome Atlas (TCGA) for primary GBM tumors.
Employed univariate Cox regression to identify survival-associated genes.
Compared ML models (RF, GB, SVM-RFE, RF-RFE, PCA) and a DL model (DeepSurv) for survival prediction.

Main Results:

Univariate analysis identified 694 survival-associated genes.
The best ML model (RF-RFE) achieved a c-index of 0.725.
DeepSurv demonstrated superior predictive accuracy with a c-index of 0.822, identifying top prognostic genes like CMTR1, GMPR, and PPY.

Conclusions:

Deep learning, specifically DeepSurv, offers higher predictive accuracy for GBM survival compared to ML models.
The study identified key genes with prognostic significance, implicating purine metabolism, RNA processing, and neuroendocrine signaling pathways.
These findings provide valuable insights into GBM biology and highlight potential biomarkers for therapeutic development.