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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...
Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...
Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...
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...
Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...

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Related Experiment Video

Updated: May 9, 2026

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres
06:52

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres

Published on: July 22, 2020

A novel algorithm for simplification of complex gene classifiers in cancer.

Raphael A Wilson1, Ling Teng, Karen M Bachmeyer

  • 1Authors' Affiliations: Division of Hematology/Oncology, Department of Pediatrics, University of Texas Southwestern Medical Center and Children's Medical Center, Dallas, Texas; Department of Pathology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma; Department of Pathology, University of Southern California and Children's Hospital of Los Angeles, Los Angeles, California; Department of Pathology and Laboratory Medicine, Nationwide Children's Hospital and Department of Pathology and Pediatrics, The Ohio State University, Columbus, Ohio; National Cancer Institute, Bethesda, Maryland; Division of Hematology/Oncology, Department of Pediatrics, University of Washington and Seattle Children's Hospital, Seattle, Washington; Department of Biostatistics, College of Public Health, University of Nebraska Medical Center, Omaha, Nebraska; The Section of Pediatric Hematology/Oncology, Department of Pediatrics, Department of Pathology, Center for Research Informatics, and The Computation Institute, University of Chicago, Chicago, Illinois.

Cancer Research
|August 6, 2013
PubMed
Summary

Complex molecular classifiers for cancer diagnosis can be simplified. An algorithm reduced a 50-gene rhabdomyosarcoma signature to two genes, maintaining diagnostic accuracy across multiple datasets and showing potential for clinical use.

Related Experiment Videos

Last Updated: May 9, 2026

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres
06:52

Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres

Published on: July 22, 2020

Area of Science:

  • Oncology
  • Bioinformatics
  • Molecular Biology

Background:

  • Complex molecular classifiers are crucial for cancer diagnosis and prognosis.
  • Current limitations in clinical application include high cost and time requirements.
  • Simplifying these classifiers is essential for routine clinical use.

Purpose of the Study:

  • To develop a method for simplifying complex gene expression classifiers.
  • To reduce a 50-gene rhabdomyosarcoma classifier to a minimal set of genes.
  • To validate the generalizability of the simplification algorithm in cancer research.

Main Methods:

  • Utilized an exhaustive iterative search algorithm to identify minimal gene signatures.
  • Applied the algorithm to a 50-gene expression signature for rhabdomyosarcoma subtypes.
  • Validated the reduced signatures on independent datasets, including formalin-fixed, paraffin-embedded samples.
  • Tested the algorithm's generalizability on a lung cancer survival dataset.

Main Results:

  • Successfully distilled the 50-gene classifier into a two-gene signature with equivalent discrimination.
  • Validated the two-gene signature's performance across three distinct datasets.
  • Demonstrated the algorithm's ability to identify minimal gene signatures for survival prediction in lung cancer.
  • Confirmed the robustness of the simplified signatures even with degraded RNA samples.

Conclusions:

  • A computational approach can significantly simplify complex gene expression classifiers.
  • Minimal gene signatures derived from this method retain diagnostic and prognostic accuracy.
  • This approach facilitates the development of cost-effective and time-efficient molecular tools for clinical application.
  • The generalized algorithm holds promise for routine clinical use in oncology.