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Related Concept Videos

Cancer-Critical Genes II: Tumor Suppressor Genes01:05

Cancer-Critical Genes II: Tumor Suppressor Genes

Genes usually encode proteins necessary for the proper functioning of a healthy cell. Mutations can often cause changes to the gene expression pattern, thereby altering the phenotype.
When the function of certain critical genes, especially those involved in cell cycle regulation and cell growth signaling cascades, gets disrupted, it upsets the cell cycle progression. Such cells with unchecked cell cycles start proliferating uncontrollably and eventually develop into tumors.
Such genes that act...
Cancer-Critical Genes II: Tumor Suppressor Genes01:05

Cancer-Critical Genes II: Tumor Suppressor Genes

Genes usually encode proteins necessary for the proper functioning of a healthy cell. Mutations can often cause changes to the gene expression pattern, thereby altering the phenotype.
When the function of certain critical genes, especially those involved in cell cycle regulation and cell growth signaling cascades, gets disrupted, it upsets the cell cycle progression. Such cells with unchecked cell cycles start proliferating uncontrollably and eventually develop into tumors.
Such genes that act...
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...
Genetic Variation01:25

Genetic Variation

Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
Genes exist in different versions called alleles, which...
Classification of Illness01:17

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe and...

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Related Experiment Video

Updated: Jun 2, 2026

Cancer-Associated Fibroblasts from Mouse Mammary Tumors as Tools for Molecular and Computational Studies
09:01

Cancer-Associated Fibroblasts from Mouse Mammary Tumors as Tools for Molecular and Computational Studies

Published on: July 3, 2025

TC-VGC: a tumor classification system using variations in genes' correlation.

Eunji Shin1, Youngmi Yoon, Jaegyoon Ahn

  • 1Department of Computer Science, Yonsei University, 134 Sinchon-dong, Seodaemun-gu, Seoul 120-749, South Korea.

Computer Methods and Programs in Biomedicine
|May 3, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a novel cancer classification method using gene-gene correlation networks. The approach accurately identifies cancer genes and improves diagnostic accuracy with fewer genes.

Related Experiment Videos

Last Updated: Jun 2, 2026

Cancer-Associated Fibroblasts from Mouse Mammary Tumors as Tools for Molecular and Computational Studies
09:01

Cancer-Associated Fibroblasts from Mouse Mammary Tumors as Tools for Molecular and Computational Studies

Published on: July 3, 2025

Area of Science:

  • Bioinformatics
  • Genomics
  • Cancer Research

Background:

  • Microarray data analysis is crucial for disease diagnosis, particularly in oncology.
  • Current methods often overlook complex gene-gene functional dependencies, focusing on differential gene expression.
  • Identifying relevant genes is key for accurate cancer classification and understanding disease mechanisms.

Purpose of the Study:

  • To develop a new method for cancer classification that leverages gene-gene correlation variations.
  • To identify novel candidate prostate cancer genes by analyzing cancer-specific genetic networks.
  • To enhance classification accuracy using a minimal set of informative genes.

Main Methods:

  • Proposed a novel classification method based on distinguishing gene-gene correlation variations between normal and tumor samples.
  • Constructed cancer-specific genetic networks using identified gene pairs.
  • Validated the method's ability to identify known and novel prostate cancer genes.

Main Results:

  • The developed method achieved high accuracy in cancer classification.
  • The approach successfully identified literature-curated prostate cancer genes.
  • New candidate prostate cancer genes were inferred from the constructed genetic networks.
  • Effective classification was achieved using a significantly reduced number of genes.

Conclusions:

  • Gene-gene correlation networks offer a powerful approach for cancer classification.
  • This method enhances the identification of cancer-specific genes and improves diagnostic accuracy.
  • The approach holds promise for advancing cancer diagnostics and biomarker discovery.