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Related Concept Videos

Cancer02:18

Cancer

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.
mTOR Signaling and Cancer Progression03:03

mTOR Signaling and Cancer Progression

The mammalian target of rapamycin or mTOR protein was discovered in 1994 due to its direct interaction with rapamycin. The protein gets its name from a yeast homolog called TOR. The mTOR protein complex in mammalian cells plays a major role in balancing anabolic processes such as the synthesis of proteins, lipids, and nucleotides and catabolic processes, such as autophagy in response to environmental cues, such as availability of nutrients and growth factors.
The mTOR pathway or the...
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...

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Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres
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Published on: July 22, 2020

Efficient methods for identifying mutated driver pathways in cancer.

Junfei Zhao1, Shihua Zhang, Ling-Yun Wu

  • 1National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.

Bioinformatics (Oxford, England)
|September 18, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces novel computational methods to identify cancer driver pathways from genomic data. The developed integrative model effectively combines mutation and expression data to uncover more biologically relevant gene sets for cancer research.

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Area of Science:

  • Computational Biology
  • Cancer Genomics
  • Bioinformatics

Background:

  • Understanding cancer's molecular mechanisms is crucial for diagnostics and therapeutics.
  • Large-scale cancer genomics projects generate vast amounts of data on genomic and gene expression aberrations.
  • Distinguishing driver mutations from passenger mutations remains a significant challenge.

Purpose of the Study:

  • To develop computational methods for de novo identification of mutated driver pathways in cancer.
  • To propose an integrative model combining mutation and gene expression data for improved pathway identification.
  • To provide researchers with an accessible software package for analyzing cancer mutation data.

Main Methods:

  • Developed two methods to solve the maximum weight submatrix problem for pathway identification.
  • Implemented an exact method for algorithm assessment and a stochastic method for incorporating diverse data types.
  • Created an integrative model combining mutation and gene expression profiles.

Main Results:

  • Validated methods on simulated data, demonstrating their efficiency.
  • Applied methods to real datasets including head and neck, glioblastoma, and ovarian cancers.
  • The integrative model successfully identified more biologically relevant gene sets compared to mutation data alone.

Conclusions:

  • The proposed methods effectively identify mutated driver pathways from cancer genomics data.
  • The integrative approach enhances the biological relevance of identified gene sets.
  • A user-friendly package, mutated driver pathway finder, is available for broader research application.