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

Design and Control of a 1-DOF MRI Compatible Pneumatically Actuated Robot with Long Transmission Lines.

IEEE/ASME transactions on mechatronics : a joint publication of the IEEE Industrial Electronics Society and the ASME Dynamic Systems and Control Division·2011

Same author

Effect of oxidized low-density lipoprotein concentration polarization on human smooth muscle cells' proliferation, cycle, apoptosis and oxidized low-density lipoprotein uptake.

Journal of the Royal Society, Interface·2011

Same author

Acrolein hydrogenation on Pt(211) and Au(211) surfaces: a density functional theory study.

Physical chemistry chemical physics : PCCP·2011

Same author

Anhydrous proton-conducting membrane based on poly-2-vinylpyridinium dihydrogenphosphate for electrochemical applications.

The journal of physical chemistry. B·2011

Same author

Pharmacophore identification, virtual screening and biological evaluation of prenylated flavonoids derivatives as PKB/Akt1 inhibitors.

European journal of medicinal chemistry·2011

Same author

Metabolomic study of insomnia and intervention effects of Suanzaoren decoction using ultra-performance liquid-chromatography/electrospray-ionization synapt high-definition mass spectrometry.

Journal of pharmaceutical and biomedical analysis·2011

Same journal

The Oncogenic and Tumor-Suppressive Roles of SNHG18: A Double-Edged Long Noncoding RNA in Cancer.

BioMed research international·2026

Same journal

Evaluation of LncRNA NEAT1 and MEG3 Expression Levels in Hospitalized COVID-19 Patients.

BioMed research international·2026

Same journal

Perceived Self-Efficacy and Its Determinants for Noncommunicable Disease Prevention Among Adults in Southern Ethiopia: A Community-Based Cross-Sectional Study.

BioMed research international·2026

Same journal

Resveratrol Mitigates Noise-Induced Cochlear Damage and Delays Hearing Loss in Wistar Rats.

BioMed research international·2026

Same journal

RETRACTION: Green Fabrication of Silver Nanoparticles Using Euphorbia Serpens Kunth Aqueous Extract, their Characterization, and Investigation of its in Vitro Antioxidative, Antimicrobial, Insecticidal, and Cytotoxic Activities.

BioMed research international·2026

Same journal

Predictors of Prolonged Hospital Length of Stay in Patients With Odontogenic Infections in Ghana.

BioMed research international·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 7, 2026

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

Network-based inference framework for identifying cancer genes from gene expression data.

Bo Yang¹, Junying Zhang, Yaling Yin

¹School of Computer Science and Technology, Xidian University, Xi'an 710071, China.

Biomed Research International

|September 28, 2013

Summary

This summary is machine-generated.

This study introduces a novel differential network framework for identifying cancer-related genes. The method improves accuracy in detecting cancer biomarkers, including newly discovered ones for breast cancer.

More Related Videos

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

Related Experiment Videos

Last Updated: May 7, 2026

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

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

Area of Science:

Bioinformatics
Genomics
Computational Biology

Background:

Accurate identification of cancer genes is crucial for understanding tumor behavior.
Existing methods for detecting oncogenes face challenges in accuracy and biological relevance.

Purpose of the Study:

To develop a robust framework for detecting biologically meaningful cancer-related genes.
To improve the accuracy of identifying cancer biomarkers using a differential network approach.

Main Methods:

A novel gene regulatory network construction algorithm using boosting regression.
Independent construction of gene regulatory networks from case and control samples.
Development of a differential-network model by subtracting networks to rank differentially expressed hub genes.

Main Results:

The differential network method significantly improved accuracy compared to t-test and lasso on synthetic and real breast cancer datasets.
Six candidate breast cancer genes (TSPYL5, CD55, CCNE2, DCK, BBC3, MUC1) were identified and validated.
TSPYL5, CCNE2, and CD55 show known or suspected roles, while three are newly identified breast cancer biomarkers.

Conclusions:

The differential network framework offers a powerful approach for identifying cancer-associated genes.
This method can be extended to discover biomarkers for other complex diseases.
The study highlights novel biomarkers for breast cancer, advancing diagnostic and prognostic capabilities.