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Related Concept Videos

Tumor Progression02:07

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...
Tumor Progression02:07

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...
Cancer Survival Analysis01:21

Cancer Survival Analysis

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...

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Related Experiment Video

Updated: Jun 23, 2026

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

Network-based inference of cancer progression from microarray data.

Yongjin Park1, Stanley Shackney, Russell Schwartz

  • 1Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA 15213, USA. jpark28@jhu.edu

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|May 2, 2009
PubMed
Summary
This summary is machine-generated.

Inferring cancer progression pathways using gene networks improves tumor similarity estimates. This approach aids in identifying key mutations for targeted cancer therapeutics and diagnostics.

Related Experiment Videos

Last Updated: Jun 23, 2026

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

Area of Science:

  • Oncology
  • Computational Biology
  • Genomics

Background:

  • Cancer is characterized by uncontrolled cell growth, driven by diverse mutations.
  • Understanding cancer progression pathways is crucial for developing targeted therapies and diagnostics.

Purpose of the Study:

  • To enhance the inference of evolutionary distances between tumors using gene network models.
  • To improve the accuracy of cancer progression pathway identification by accounting for gene correlations.

Main Methods:

  • Applied gene network models to estimate evolutionary distances between tumors.
  • Tested three network variants: optimized best-fit, sampled subnetwork, and modular network.
  • Utilized lung and breast cancer microarray data for analysis.

Main Results:

  • Network correction approaches showed improvements in phylogenetic accuracy.
  • Sampled and modular network models yielded more substantial improvements than the optimized network.
  • The study demonstrated the utility of network correction for tumor similarity estimation.

Conclusions:

  • Gene network models offer a promising approach to refine cancer progression pathway inference.
  • Sophisticated network models are necessary to address the complexity of cancer evolution and data limitations.
  • This research contributes to advancing computational methods for cancer research and personalized medicine.