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

Elevated cfDNA after exercise is derived primarily from mature polymorphonuclear neutrophils, with a minor contribution of cardiomyocytes.

Cell reports. Medicine·2026
Same author

Metabolic Endotoxemia Amplifies Estrogen Signaling Through Stromal Crosstalk in Obesity-Associated Breast Cancer.

International journal of molecular sciences·2026
Same author

Single-session transanal minimally invasive surgery (TAMIS) and adjuvant radiotherapy in a patient with three synchronous early rectal adenocarcinomas: a video vignette.

Techniques in coloproctology·2026
Same author

DNA methylation-based deconvolution study of glioblastoma heterogeneity and identification of cell compositions associated with patient survival.

Neuro-oncology advances·2026
Same author

BRAF <sup><b>V600E</b></sup> Metastatic Synovial Sarcoma Treated with BRAF & MEK Inhibitors Achieves Complete Response. A Case Report & Literature Review.

Oncology research·2026
Same author

Recombinant Human Amelogenin Protein Enhances Healing of Osteochondral Injury in a Goat Model.

Journal of orthopaedic research : official publication of the Orthopaedic Research Society·2026

Related Experiment Video

Updated: Jul 18, 2025

LINE-1 Methylation Analysis in Mesenchymal Stem Cells Treated with Osteosarcoma-Derived Extracellular Vesicles
12:18

LINE-1 Methylation Analysis in Mesenchymal Stem Cells Treated with Osteosarcoma-Derived Extracellular Vesicles

Published on: February 1, 2020

5.8K

Rapid Classification of Sarcomas Using Methylation Fingerprint: A Pilot Study.

Aviel Iluz1,2, Myriam Maoz3, Nir Lavi1,2,4

  • 1Leslie and Michael Gaffin Center for Neuro-Oncology, Hadassah Medical Center and Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem 9190501, Israel.

Cancers
|August 26, 2023
PubMed
Summary

Classifying sarcomas using nanopore sequencing and methylation signatures shows promise for faster diagnosis. This approach aids in identifying cancer types, though further validation is needed for clinical application.

Keywords:
classificationcopy-numbermachine learningmethylationnanoporesarcoma

More Related Videos

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

Methyl-binding DNA capture Sequencing for Patient Tissues

Published on: October 31, 2016

8.7K
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

5.6K

Related Experiment Videos

Last Updated: Jul 18, 2025

LINE-1 Methylation Analysis in Mesenchymal Stem Cells Treated with Osteosarcoma-Derived Extracellular Vesicles
12:18

LINE-1 Methylation Analysis in Mesenchymal Stem Cells Treated with Osteosarcoma-Derived Extracellular Vesicles

Published on: February 1, 2020

5.8K
Methyl-binding DNA capture Sequencing for Patient Tissues
08:40

Methyl-binding DNA capture Sequencing for Patient Tissues

Published on: October 31, 2016

8.7K
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

5.6K

Area of Science:

  • Oncology
  • Genomics
  • Bioinformatics

Background:

  • Sarcoma classification is complex, often causing treatment delays.
  • Previous diagnostic methods relied on DNA aberrations and methylation profiles with machine learning.
  • Accurate and timely sarcoma diagnosis is critical for effective patient treatment.

Purpose of the Study:

  • To classify sarcomas using methylation signatures from low-coverage whole-genome sequencing.
  • To evaluate the utility of nanopore sequencing for identifying copy-number alterations alongside methylation data.
  • To develop and test a methylation-based classifier for sarcoma diagnosis.

Main Methods:

  • DNA extraction from 23 suspected sarcoma samples.
  • Low-coverage whole-genome sequencing using Oxford Nanopore technology.
  • Application of a customized methylation classifier (nanoDx pipeline) with Random Forest and t-distributed stochastic neighbor embedding, alongside copy-number alteration detection.

Main Results:

  • 20 out of 23 samples were successfully sequenced; 18 contained tumor tissue.
  • 14 out of 18 tumor samples achieved classification concordant with pathology reports.
  • Four classifications were discordant, highlighting areas for methodological improvement.

Conclusions:

  • Nanopore sequencing combined with methylation analysis offers a potential method for sarcoma classification.
  • Improvements in tissue handling, DNA extraction, and detection of other genetic alterations are necessary.
  • Further validation in diverse cohorts could enable rapid, point-of-care sarcoma classification.