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Related Concept Videos

Cancer02:18

Cancer

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Cancers arise due to mutations in genes involved in the regulation of cell division, which leads to unrestricted cell proliferation. Modern science and medicine have made great strides in the understanding and treatment of cancer, including eradicating cancer in some patients. However, there is still no cure for cancer. This is largely due to the fact that cancer is a large group of many diseases.
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Cancer-Critical Genes II: Tumor Suppressor Genes01:05

Cancer-Critical Genes II: Tumor Suppressor Genes

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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...
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Detection of Rare Mutations in CtDNA Using Next Generation Sequencing
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QuaDMutNetEx: a method for detecting cancer driver genes with low mutation frequency.

Yahya Bokhari1,2,3, Areej Alhareeri4,5, Tomasz Arodz6

  • 1Department of Computer Science, College of Engineering, Virginia Commonwealth University, 401 W. Main St., Richmond, VA 23284, USA.

BMC Bioinformatics
|April 16, 2020
PubMed
Summary

Identifying low-frequency cancer driver genes is challenging. The new QuaDMutNetEx method uses protein-protein interaction networks and mutation patterns to find biologically connected driver genes, improving upon existing methods.

Keywords:
Cancer pathwaysDriver mutationsProtein-protein interaction networksSomatic mutations

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Cancer arises from genetic mutations, but distinguishing driver mutations from passenger mutations is difficult.
  • Many cancer driver genes are known, but identifying low-frequency driver genes remains a challenge.
  • Passenger mutations are common, complicating the identification of low-frequency driver mutations.

Purpose of the Study:

  • To develop a novel computational method for detecting low-frequency cancer driver genes.
  • To improve the identification of biologically relevant driver genes by integrating network information.
  • To provide a valuable tool for cancer research and potential therapeutic target discovery.

Main Methods:

  • Proposed a new method, QuaDMutNetEx, for cancer driver gene detection.
  • Utilized protein-protein interaction networks to prioritize genes.
  • Analyzed mutation patterns, focusing on mutual exclusivity within functional gene groups.
  • Applied the method to four different tumor sample datasets.

Main Results:

  • QuaDMutNetEx successfully identified biologically connected sets of low-frequency driver genes.
  • The method discovered driver genes missed by approaches not considering network connectivity.
  • The discovered gene sets showed improved quality and interpretability compared to existing methods.

Conclusions:

  • QuaDMutNetEx is an effective tool for detecting low-frequency cancer driver genes.
  • Integrating network information enhances the discovery of biologically relevant driver genes.
  • The method offers improved quality and interpretability for identifying novel cancer drivers.