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 Survival Analysis01:21

Cancer Survival Analysis

812
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...
812

You might also read

Related Articles

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

Sort by
Same author

ONT-only genome assembly of a Korean male individual using a semen sample.

Genes & genomics·2026
Same author

Immune phenotype-based stratification of colorectal cancer reveals subtype-specific immunotherapeutic opportunities: insights from a Korean patient cohort.

BMB reports·2026
Same author

Reinforcement learning with low-rank adaptation for targeted antimicrobial peptide design.

Briefings in bioinformatics·2025
Same author

DeepRNA-DTI: a deep learning approach for RNA-compound interaction prediction with binding site interpretability.

Journal of cheminformatics·2025
Same author

Enhancing multi-task in vivo toxicity prediction via integrated knowledge transfer of chemical knowledge and in vitro toxicity information.

Journal of cheminformatics·2025
Same author

KG-SLomics: Synthetic Lethality Prediction Using Knowledge Graph and Cancer Type-Specific Multiomics Integrated Graph Neural Network.

IEEE transactions on computational biology and bioinformatics·2025

Related Experiment Video

Updated: Mar 17, 2026

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

8.0K

Prognostic factor analysis for breast cancer using gene expression profiles.

Soobok Joe1, Hojung Nam2

  • 1School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, 123 Cheomdangwagi-ro, Buk-gu, Gwangju, Republic of Korea.

BMC Medical Informatics and Decision Making
|July 26, 2016
PubMed
Summary
This summary is machine-generated.

This study identifies gene expression patterns to predict breast cancer patient survival. A new scoring method accurately identifies patients with poor prognoses, aiding in developing new prognostic factors.

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

9.7K
Optimization of a Multiplex RNA-based Expression Assay Using Breast Cancer Archival Material
11:12

Optimization of a Multiplex RNA-based Expression Assay Using Breast Cancer Archival Material

Published on: August 1, 2018

8.5K

Related Experiment Videos

Last Updated: Mar 17, 2026

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

8.0K
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

9.7K
Optimization of a Multiplex RNA-based Expression Assay Using Breast Cancer Archival Material
11:12

Optimization of a Multiplex RNA-based Expression Assay Using Breast Cancer Archival Material

Published on: August 1, 2018

8.5K

Area of Science:

  • Oncology
  • Genomics
  • Bioinformatics

Background:

  • Breast cancer patient survival varies significantly, ranging from months to over 15 years.
  • Gene expression profiling of tumors is emerging as a key method for predicting prognosis.

Purpose of the Study:

  • To identify prognostic factors in breast cancer using tumor gene expression data.
  • To develop a reliable scoring system for predicting patient survival outcomes.

Main Methods:

  • Utilized gene expression datasets from breast tumors.
  • Employed log-rank tests and unsupervised clustering to identify gene sets linked to survival.
  • Developed a prognosis prediction score based on gene expression ratios.

Main Results:

  • Constructed four distinct prognosis prediction gene set modules.
  • These modules accurately predicted worse survival rates across three independent datasets.
  • Higher prediction scores correlated with aggressive breast cancer subtypes (e.g., triple-negative, HER2-enriched, TP53 mutated, high-grade).

Conclusions:

  • A novel, well-defined scoring method for predicting breast cancer survival outcomes has been developed.
  • This gene expression-based approach shows potential for creating new prognostic factors in breast cancer care.