Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Cancer-Critical Genes II: Tumor Suppressor Genes01:05

Cancer-Critical Genes II: Tumor Suppressor Genes

7.6K
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...
7.6K
Cancer-Critical Genes I: Proto-oncogenes01:33

Cancer-Critical Genes I: Proto-oncogenes

9.1K
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...
9.1K
Cancer Survival Analysis01:21

Cancer Survival Analysis

411
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
411
Protein Networks02:26

Protein Networks

4.0K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.0K

You might also read

Related Articles

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

Sort by
Same author

Association of Interleukin 17 Polymorphisms and IL17A Levels with Papillary Thyroid Carcinoma: A Hospital-Based Study in the Chinese Population.

Journal of interferon & cytokine research : the official journal of the International Society for Interferon and Cytokine Research·2026
Same author

Expanded antigen-specific donor regulatory T cells for GVHD prevention.

Blood·2026
Same author

Predicting immune-related thyroiditis using polygenic risk scores in patients with advanced melanoma.

Journal for immunotherapy of cancer·2026
Same author

Dual inhibition of glycolysis and epigenetics via a nanodelivery system for colorectal cancer treatment.

Nanomedicine : nanotechnology, biology, and medicine·2026
Same author

Ficerafusp Alfa (BCA101) With Pembrolizumab for Recurrent or Metastatic Head and Neck Squamous Cell Carcinoma: Two-Year Results of an Expansion Cohort of a Phase I/Ib Trial.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology·2026
Same author

Response to "Ratios are misleading exposure variables that compromise a regression model unless fundamental scaling assumptions are satisfied: a comment on Valente et al."

Journal of clinical epidemiology·2026
Same journal

Bioactive carbon dots from peony seed meal for nanomedicine via circular economy.

iScience·2026
Same journal

Genetic ablation of <i>Sfxn5</i> induces mitochondrial dysfunction and precipitates lethal metabolic crisis in mice.

iScience·2026
Same journal

Expansion, functional diversification, and gene fusion events in the Ato protein family.

iScience·2026
Same journal

The pro-inflammatory cytokines IFN-α and TNF-α inhibit organoid-derived extravillous trophoblast invasion.

iScience·2026
Same journal

Urbanization compound pathways of global lung cancer incidence risk under proximal and distal interactions.

iScience·2026
Same journal

Capsid and integrase play essential apposing roles in viral ribonucleoprotein assembly during HIV-1 core morphogenesis.

iScience·2026
See all related articles

Related Experiment Video

Updated: Aug 13, 2025

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres
06:52

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

Published on: July 22, 2020

6.6K

A subnetwork-based framework for prioritizing and evaluating prognostic gene modules from cancer transcriptome data.

Biwei Cao1, Krupal B Patel2, Tingyi Li1

  • 1Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, 12902 Magnolia Drive, Tampa, FL 33612, USA.

Iscience
|January 23, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel subnetwork-based framework to identify reliable cancer prognostic biomarkers. The approach improves head and neck squamous cell carcinoma (HNSCC) gene module discovery, overcoming patient heterogeneity challenges.

Keywords:
BioinformaticsCancerGene network

More Related Videos

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

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

Published on: May 17, 2019

9.0K
Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal
08:00

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Published on: October 11, 2019

7.6K

Related Experiment Videos

Last Updated: Aug 13, 2025

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres
06:52

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

Published on: July 22, 2020

6.6K
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

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

Published on: May 17, 2019

9.0K
Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal
08:00

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Published on: October 11, 2019

7.6K

Area of Science:

  • Bioinformatics
  • Genomics
  • Cancer Research

Background:

  • Accurate cancer prognosis prediction is vital for clinical decisions.
  • Transcriptome data offers potential for prognostic biomarker discovery.
  • Tumor and patient heterogeneity pose significant challenges to biomarker identification.

Purpose of the Study:

  • To present a novel framework integrating multiple strategies for prioritizing candidate genes.
  • To improve the identification of robust prognostic biomarkers from omics data.
  • To address the limitations of standard gene selection methods in cancer prognosis.

Main Methods:

  • Developed a framework combining hypothesis-driven, data-driven, and literature-based subnetwork strategies.
  • Incorporated informative visualization for candidate gene prioritization.
  • Applied the framework to a head and neck squamous cell carcinoma (HNSCC) transcriptome dataset.

Main Results:

  • Identified multiple HNSCC-specific gene modules with enhanced prognostic value.
  • Achieved improved prognostic accuracy compared to standard gene panel selection methods.
  • Gained valuable mechanistic insights from the identified gene modules.

Conclusions:

  • The proposed subnetwork-based framework effectively distills reliable and biologically meaningful prognostic factors.
  • This generalizable approach can be applied to various omics datasets for biomarker discovery.
  • Subnetwork analysis offers a powerful strategy for advancing cancer prognosis prediction.