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

The Tumor Microenvironment02:17

The Tumor Microenvironment

6.5K
Every normal cell or tissue is embedded in a complex local environment called stroma, consisting of different cell types, a basal membrane, and blood vessels. As normal cells mutate and develop into cancer cells, their local environment also changes to allow cancer progression. The tumor microenvironment (TME) consists of a complex cellular matrix of stromal cells and the developing tumor. The cross-talk between cancer cells and surrounding stromal cells is critical to disrupt normal tissue...
6.5K

You might also read

Related Articles

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

Sort by
Same author

A diagnostic pitfall in iron-refractory microcytic hypochromic anemia with acquired ring sideroblasts initially treated as iron deficiency anemia-a case report.

Frontiers in medicine·2026
Same author

Correlations among traditional Chinese medicine constitutions, sleep quality, and cancer-related fatigue in patients with breast cancer.

Frontiers in public health·2026
Same author

β-Hydroxybutyric acid impairs host antimicrobial defense by targeting the CEACAM1-NADPH-ROS axis to disrupt macrophage bactericidal activity.

Biochemical pharmacology·2026
Same author

Ventricular preexcitation-induced subclinical LV dysfunction: Insights from pressure-strain loop imaging.

American heart journal plus : cardiology research and practice·2026
Same author

The genetic association of miR-34a-5p rs72631823 with the susceptibility to obstetric antiphospholipid syndrome.

BMC women's health·2026
Same author

Patient-derived ovarian cancer organoids as platforms for predicting platinum resistance and screening tumor stem cell inhibitors.

Drug resistance updates : reviews and commentaries in antimicrobial and anticancer chemotherapy·2026

Related Experiment Video

Updated: May 26, 2025

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
09:53

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

6.9K

A Machine Learning Approach to Build and Evaluate a Molecular Prognostic Model for Endometrial Cancer Based on Tumour

Di Wu1, Zhifeng Yan2, Mingxia Li2

  • 1School of Medicine, Nankai University, Tianjin, China.

Journal of Cellular and Molecular Medicine
|February 21, 2025
PubMed
Summary
This summary is machine-generated.

A new machine learning model, RF16, accurately predicts endometrial cancer (EC) patient outcomes. This cost-effective approach uses immunohistochemistry, enhancing personalized treatment strategies for EC.

Keywords:
endometrial carcinomaimmunohistochemistrymachine learningmolecularprognosistumour microenvironment

More Related Videos

Molecular Profiling of the Invasive Tumor Microenvironment in a 3-Dimensional Model of Colorectal Cancer Cells and Ex vivo Fibroblasts
10:33

Molecular Profiling of the Invasive Tumor Microenvironment in a 3-Dimensional Model of Colorectal Cancer Cells and Ex vivo Fibroblasts

Published on: April 29, 2014

11.0K
Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer
08:20

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer

Published on: May 21, 2019

5.5K

Related Experiment Videos

Last Updated: May 26, 2025

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
09:53

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

6.9K
Molecular Profiling of the Invasive Tumor Microenvironment in a 3-Dimensional Model of Colorectal Cancer Cells and Ex vivo Fibroblasts
10:33

Molecular Profiling of the Invasive Tumor Microenvironment in a 3-Dimensional Model of Colorectal Cancer Cells and Ex vivo Fibroblasts

Published on: April 29, 2014

11.0K
Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer
08:20

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer

Published on: May 21, 2019

5.5K

Area of Science:

  • Oncology
  • Bioinformatics
  • Machine Learning

Background:

  • Global incidence and tumor burden of endometrial cancer (EC) are rising.
  • Personalized treatment strategies are crucial for improving patient outcomes.
  • Molecular expression profiling holds promise for prognostic modeling in EC.

Purpose of the Study:

  • To develop a molecular expression prognostic model for endometrial cancer (EC) using a machine learning approach.
  • To guide personalized treatment decisions by analyzing the tumor microenvironment.
  • To create a cost-effective and clinically valuable predictive tool for EC.

Main Methods:

  • Utilized two datasets (training n=698, testing n=151) of EC patients who underwent hysterectomy.
  • Employed R software for developing and optimizing predictive models for progression-free and overall survival.
  • Identified survival-related factors via univariate analysis and Cox regression, visualized with a nomogram.
  • Developed a random forest model (RF16) for molecular classification, replacing gene sequencing with immunohistochemistry.

Main Results:

  • A total of 849 EC patients were included in the study.
  • The developed random forest model (RF16) effectively characterizes tumor molecules.
  • RF16 enhances generalizability by utilizing immunohistochemistry instead of gene sequencing.
  • The model demonstrated low cost, high efficiency, and clinical value.

Conclusions:

  • The machine learning-based RF16 model is a clinically valuable tool for endometrial cancer (EC) prognosis.
  • RF16 offers a cost-effective and efficient method for guiding personalized treatment in EC.
  • This approach complements existing molecular classification methods for EC.