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

301
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...
301

You might also read

Related Articles

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

Sort by
Same author

[Kinase-Glo luminescent kinase assay for in vitro determination of PKA activity].

Xi bao yu fen zi mian yi xue za zhi = Chinese journal of cellular and molecular immunology·2012
Same author

Functional characterization of an arrestin gene on insecticide resistance of Culex pipiens pallens.

Parasites & vectors·2012
Same author

MiR-23a inhibits myogenic differentiation through down regulation of fast myosin heavy chain isoforms.

Experimental cell research·2012
Same author

Let-7b inhibits human cancer phenotype by targeting cytochrome P450 epoxygenase 2J2.

PloS one·2012
Same author

Role of IKK/NF-κB signaling in extinction of conditioned place aversion memory in rats.

PloS one·2012
Same author

Inhibition of poly(ADP-ribose) polymerase attenuates acute kidney injury in sodium taurocholate-induced acute pancreatitis in rats.

Pancreas·2012

Related Experiment Video

Updated: May 12, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.6K

Predicting breast cancer prognosis based on a novel pathomics model through CHEK1 expression analysis using machine

Chen Chen1, Dan Gao1, Huan Yue2

  • 1Breast and Thyroid Center, The First People's Hospital of Zunyi (The Third Affiliated Hospital of Zunyi Medical University), Zunyi, Guizhou, China.

Plos One
|May 9, 2025
PubMed
Summary

This study developed a machine learning pathomics model to predict breast cancer prognosis based on CHEK1 gene expression. The model shows potential for guiding treatment decisions and improving patient outcomes.

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

8.8K
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

6.8K

Related Experiment Videos

Last Updated: May 12, 2025

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.6K
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

8.8K
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

6.8K

Area of Science:

  • Oncology
  • Computational Pathology
  • Bioinformatics

Background:

  • Checkpoint kinase 1 (CHEK1) is frequently overexpressed in solid tumors, but its prognostic role in breast cancer (BrC) is not well-defined.
  • This study investigates the prognostic significance of CHEK1 in breast cancer using a novel pathomics approach.

Purpose of the Study:

  • To develop and validate a machine learning-based pathomics model for predicting breast cancer prognosis using CHEK1 gene expression.
  • To assess the clinical utility of the pathomics score (PS) for prognosis and treatment guidance in breast cancer.

Main Methods:

  • Utilized hematoxylin-eosin (H&E)-stained images from The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) dataset.
  • Extracted radiomic features using PyRadiomics, mRMRe, and Gradient Boosting Machine (GBM) to generate a pathomics score (PS) predicting CHEK1 expression.
  • Validated the PS using RNA-seq data and assessed prognostic significance via Kaplan-Meier and Cox regression, with immunohistochemistry (IHC) validation on tissue microarrays (TMA).

Main Results:

  • A pathomics model was generated using 8 recursive feature elimination-screened features, correlating a high pathomics score (PS) with CHEK1 overexpression and poorer survival outcomes within 96 months.
  • Patients with a high PS demonstrated responsiveness to anti-programmed cell death protein 1 (anti-PD-1) and anti-cytotoxic T lymphocyte antigen-4 (anti-CTLA4) therapies.
  • Tissue microarray validation confirmed that the high PS accurately predicted poorer prognosis and correlated with elevated CHEK1 expression.

Conclusions:

  • A novel pathomics model effectively predicts CHEK1 expression in breast cancer using machine learning.
  • This pathomics approach offers potential clinical utility for improving breast cancer prognosis and guiding treatment strategies.