Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Tumor Progression

Tumor Progression

Tumor progression is a phenomenon where the pre-formed tumor acquires successive mutations to become clinically more aggressive and malignant. In the 1950s, Foulds first described the stepwise progression of cancer cells through successive stages.
Colon cancer is one of the best-documented examples of tumor progression. Early mutation in the APC gene in colon cells causes a small growth on the colon wall called a polyp. With time, this polyp grows into a benign, pre-cancerous tumor. Further...

You might also read

Related Articles

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

Sort by

Same author

Genetic and Pathogenic Differentiation of <i>Fusarium oxysporum</i> Isolates from Ginger.

Journal of fungi (Basel, Switzerland)·2026

Same author

Rationale for Preservative-free Glaucoma Drops.

International ophthalmology clinics·2026

Same author

Association between ocular biometric parameters and scleral rigidity as measured by fundus pulsation optical coherence elastography in eyes with and without glaucoma.

Frontiers in medicine·2026

Same author

Neutral Sphingomyelinase-2 Restrains TAZ to Suppress Breast Tumor Growth.

bioRxiv : the preprint server for biology·2026

Same author

Cyclin-Dependent Kinase 5 Contributes to Bruton's Tyrosine Kinase Inhibitor Resistance via the IRE1α/XBP1 Axis in Mantle Cell Lymphoma.

Research square·2026

Same author

Detection of bladder cancer in patients with microscopic hematuria using Oncuria-Detect: results of a prospective, multicenter international study.

Journal of translational medicine·2026

Same journal

Layered social competition coordinates reproductive hierarchy formation in ants.

bioRxiv : the preprint server for biology·2026

Same journal

Combination epigenetic-targeted therapy increases the immunogenicity of poorly immunogenic sarcomas.

bioRxiv : the preprint server for biology·2026

Same journal

Loss of LanC-like proteins delays post-injury regeneration of aging skeletal muscles.

bioRxiv : the preprint server for biology·2026

Same journal

Integrative Transfer Network: Deep Transfer Learning Across Populations and Prediction Targets.

bioRxiv : the preprint server for biology·2026

Same journal

Confidence-supported label-free metabolic imaging with FPhaS phase autofluorescence microscopy.

bioRxiv : the preprint server for biology·2026

Same journal

Sequence-encoded autoinhibition couples mRNA decapping activity to phase separation.

bioRxiv : the preprint server for biology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: May 4, 2026

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors

Published on: August 18, 2023

Eco-evolutionary Guided Pathomics Analysis to Predict DCIS Upstaging.

Yujie Xiao¹, Manal Elmasry^2,3, Ji Dong K Bai²

¹Department of Applied Mathematics and Statistics, Stony Brook University, NY, USA.

Biorxiv : the Preprint Server for Biology

|July 9, 2024

Summary

This summary is machine-generated.

Ecological analysis of hypoxia and acidosis biomarkers improves prediction of early breast cancer (DCIS) progression. This approach identifies tumor microenvironment habitats and niches, enhancing biomarker discovery for disease staging.

Keywords:

DCIS Digital pathology Eco-evolutionary biomarkers Habitat Machine learning Metabolic phenotypes Niche Pathomics Tumor ecology and evolution

More Related Videos

Author Spotlight: Unveiling Transmembrane Protein Family-Related Markers in Gastric Cancer and Implications for Targeted Therapies

Author Spotlight: Unveiling Transmembrane Protein Family-Related Markers in Gastric Cancer and Implications for Targeted Therapies

Published on: September 15, 2023

Author Spotlight: Investigating Immune Cell Dynamics in the Tumor Microenvironment — Challenges and Innovations in Cancer Prognosis

Author Spotlight: Investigating Immune Cell Dynamics in the Tumor Microenvironment — Challenges and Innovations in Cancer Prognosis

Published on: April 12, 2024

Related Experiment Videos

Last Updated: May 4, 2026

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors

Published on: August 18, 2023

Author Spotlight: Unveiling Transmembrane Protein Family-Related Markers in Gastric Cancer and Implications for Targeted Therapies

Author Spotlight: Unveiling Transmembrane Protein Family-Related Markers in Gastric Cancer and Implications for Targeted Therapies

Published on: September 15, 2023

Author Spotlight: Investigating Immune Cell Dynamics in the Tumor Microenvironment — Challenges and Innovations in Cancer Prognosis

Author Spotlight: Investigating Immune Cell Dynamics in the Tumor Microenvironment — Challenges and Innovations in Cancer Prognosis

Published on: April 12, 2024

Area of Science:

Oncology
Cancer Biology
Tumor Microenvironment Ecology

Background:

Cancers evolve within dynamic ecosystems, necessitating characterization of their ecological dynamics for understanding evolution and discovering predictive biomarkers.
Ductal carcinoma in situ (DCIS) is an early-stage breast cancer with abnormal epithelial cell growth confined to milk ducts, where progression prediction remains challenging.

Purpose of the Study:

To investigate if ecological analysis of hypoxia and acidosis biomarkers can improve the prediction of DCIS upstaging.
To develop and apply an eco-evolutionary approach to identify tumor microenvironment habitats and niches for biomarker discovery.

Main Methods:

Developed an eco-evolutionary approach to define tumor habitats based on oxygen diffusion distance.
Identified cancer cell metabolic phenotypes using biomarkers CA9 (hypoxia) and LAMP2b (acidosis).
Analyzed spatial patterns of biomarkers to define distinct tumor niches and predict patient upstaging using a random forest classifier with 5-fold validation.

Main Results:

Ecological analysis significantly enhanced the predictive power of hypoxia and acidosis biomarkers for DCIS upstaging compared to traditional methods.
Distinct tumor niches characterized by specific biomarker spatial patterns were identified.
A random forest classifier achieved an AUC of 0.74 in predicting patient upstaging based on niche distribution.

Conclusions:

Tumor ecological features are crucial for eco-evolutionary-designed approaches in novel biomarker discovery.
This study demonstrates the potential of ecological analysis of biomarkers to improve the prediction of DCIS progression.
The findings highlight the importance of considering the tumor microenvironment's spatial and metabolic heterogeneity.