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

You might also read

Related Articles

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

Sort by
Same author

Lower limb edema detection and grading classification using deep learning and image enhancement technologies.

Frontiers in medicine·2026
Same author

Improving stain normalization for digital histological image analysis based on the cycle generative adversarial network identity loss model.

Digital health·2026
Same author

SIAH2-WNK1 Signaling Drives Glycolytic Metabolism and Therapeutic Resistance in Colorectal Cancer.

International journal of molecular sciences·2026
Same author

Automatic assessment of fine motor development in children through hand-drawn shape images.

Pediatrics and neonatology·2025
Same author

Automatic Movement Recognition for Evaluating the Gross Motor Development of Infants.

Children (Basel, Switzerland)·2025
Same author

DNA methylation biomarker analysis from low-survival-rate cancers based on genetic functional approaches.

Frontiers in bioinformatics·2025

Related Experiment Video

Updated: May 10, 2025

Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer
07:50

Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

Published on: September 18, 2020

5.4K

DNA Methylation Biomarker Discovery for Colorectal Cancer Diagnosis Assistance Through Integrated Analysis.

Yi-Hsuan Tsai1, Yi-Husan Lai2, Shu-Jen Chen2

  • 1Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei, Taiwan.

Cancer Informatics
|April 28, 2025
PubMed
Summary

This study identified three key genes (ADHFE1, ADAMTS5, MIR129-2) as accurate biomarkers for colorectal cancer (CRC) detection in both tissue and blood samples, paving the way for improved diagnostics.

Keywords:
Epigeneticscancer diagnosiscomorbiditygenetic functionmachine learning

More Related Videos

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies
14:56

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies

Published on: May 6, 2022

4.3K
Methyl-binding DNA capture Sequencing for Patient Tissues
08:40

Methyl-binding DNA capture Sequencing for Patient Tissues

Published on: October 31, 2016

8.5K

Related Experiment Videos

Last Updated: May 10, 2025

Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer
07:50

Genome-Wide Analysis of DNA Methylation in Gastrointestinal Cancer

Published on: September 18, 2020

5.4K
Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies
14:56

Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies

Published on: May 6, 2022

4.3K
Methyl-binding DNA capture Sequencing for Patient Tissues
08:40

Methyl-binding DNA capture Sequencing for Patient Tissues

Published on: October 31, 2016

8.5K

Area of Science:

  • Genomics
  • Molecular Biology
  • Bioinformatics

Background:

  • Colorectal cancer (CRC) diagnosis relies on accurate biomarkers.
  • Identifying reliable biomarkers from tissue and blood samples remains a challenge.
  • DNA methylation patterns offer potential for novel CRC biomarker discovery.

Purpose of the Study:

  • To identify novel colorectal cancer (CRC) biomarkers with high classification accuracy.
  • To leverage DNA methylation profiles and gene functional annotations for biomarker selection.
  • To validate biomarker candidates using machine learning models.

Main Methods:

  • Integrated analysis of CRC DNA methylation data from The Cancer Genome Atlas.
  • Clustering of biomarker candidates based on promoter region proximity and functional annotations.
  • Application of three machine learning techniques for model construction and performance evaluation.

Main Results:

  • Ten genes exhibited significant methylation differences in both tissue and blood samples.
  • A three-gene combination (ADHFE1, ADAMTS5, MIR129-2) demonstrated superior classification performance.
  • The optimal biomarker combination achieved a Matthews correlation coefficient > 0.85 and an F1-score of 0.9.

Conclusions:

  • Identified three robust CRC biomarkers through integrated DNA methylation analysis.
  • These biomarkers show potential for a clinical diagnostic toolkit for CRC.
  • The identified biomarkers warrant further investigation via liquid biopsies for clinical utility.