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

Mapping Meiotic Recombination DNA Double-Strand Breaks (DSBs) Hotspots -Methodological Advances and Challenges.

Advanced genetics (Hoboken, N.J.)·2026
Same author

A tri-component organoid model reveals field-cancerized fibroblasts as key drivers of LUAD recurrence and drug resistance.

Molecular therapy. Oncology·2026
Same author

ctDNA-guided precision therapy with trastuzumab deruxtecan plus pyrotinib in HER2-positive breast cancer brain metastases: a case report.

Frontiers in oncology·2026
Same author

DMN-YOLO: A Lightweight Small-Object Detector for Multi-Species Animal Detection in UAV Grassland Imagery.

Animals : an open access journal from MDPI·2026
Same author

Noise performance of InAs/GaSb/AlSb/GaSb SWIR FPA.

Optics express·2026
Same author

Research Progress on Marine Active Substances in Improving Atherosclerosis.

Marine drugs·2026

Related Experiment Video

Updated: Jun 25, 2025

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies
13:24

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Published on: April 11, 2016

11.8K

Generalizable transcriptome-based tumor malignant level evaluation and molecular subtyping towards precision

Dingxue Hu1,2, Ziteng Zhang3, Xiaoyi Liu1

  • 1Institute of Cancer Research, Shenzhen Bay Laboratory, Shenzhen, 518132, China.

Journal of Translational Medicine
|May 28, 2024
PubMed
Summary

Researchers identified key genes linked to cancer prognosis by analyzing tumor transcriptomes. This led to models predicting tumor malignancy and survival, advancing precision oncology and patient care.

Keywords:
Hepatocellular carcinomaOncogeneSurvival analysisTumor suppressor

More Related Videos

Author Spotlight: Finding New Therapeutic Targets for Malignant Peripheral Nerve Sheath Tumor Through Genome-Scale shRNA Screens
09:33

Author Spotlight: Finding New Therapeutic Targets for Malignant Peripheral Nerve Sheath Tumor Through Genome-Scale shRNA Screens

Published on: August 25, 2023

1.1K
Comparative Lesions Analysis Through a Targeted Sequencing Approach
08:16

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Published on: November 5, 2019

6.7K

Related Experiment Videos

Last Updated: Jun 25, 2025

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies
13:24

Integration of Wet and Dry Bench Processes Optimizes Targeted Next-generation Sequencing of Low-quality and Low-quantity Tumor Biopsies

Published on: April 11, 2016

11.8K
Author Spotlight: Finding New Therapeutic Targets for Malignant Peripheral Nerve Sheath Tumor Through Genome-Scale shRNA Screens
09:33

Author Spotlight: Finding New Therapeutic Targets for Malignant Peripheral Nerve Sheath Tumor Through Genome-Scale shRNA Screens

Published on: August 25, 2023

1.1K
Comparative Lesions Analysis Through a Targeted Sequencing Approach
08:16

Comparative Lesions Analysis Through a Targeted Sequencing Approach

Published on: November 5, 2019

6.7K

Area of Science:

  • Oncology
  • Genomics
  • Bioinformatics

Background:

  • Precision oncology leverages tumor-specific characteristics for improved cancer treatment.
  • Integrating tumor transcriptome data with patient prognosis is crucial for understanding cancer progression.

Purpose of the Study:

  • To identify dysregulated and prognosis-associated genes across various cancer types.
  • To develop models for quantitatively evaluating tumor malignancy and predicting patient survival.
  • To perform molecular subtyping of hepatocellular carcinoma based on transcriptome data.

Main Methods:

  • In-depth integration of tumor transcriptome and patient prognosis data.
  • Gene expression analysis to identify prognosis-associated genes.
  • Development of predictive models using gene expression matrices.
  • Transcriptome-based molecular subtyping of hepatocellular carcinoma.

Main Results:

  • Catalogued cancer type-dependent dysregulated and prognosis-associated genes.
  • Developed models that quantitatively evaluate tumor malignancy, enhancing clinical staging and survival prediction.
  • Identified three hepatocellular carcinoma subtypes with distinct clinical outcomes, mutation landscapes, immune microenvironments, and pathway dysregulations.

Conclusions:

  • Tumor transcriptome analysis provides a cost-effective approach for identifying prognostic markers and molecular subtypes.
  • The developed models offer practical tools for clinical application, supporting precision oncology.
  • This work facilitates improved healthcare through accessible transcriptome-based cancer analysis.