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Cancer-Critical Genes II: Tumor Suppressor Genes01:05

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In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
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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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Mining the coding and non-coding genome for cancer drivers.

Jia Li1, Damien Drubay2, Stefan Michiels2

  • 1Institute for Integrative Biology of the Cell (I2BC), CNRS, CEA, Université Paris-Sud, Université Paris-Saclay, 91198 Gif sur Yvette, France.

Cancer Letters
|October 4, 2015
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Summary
This summary is machine-generated.

Prioritizing cancer-driving mutations is challenging. This review focuses on computational methods for identifying non-coding cancer variants, crucial for understanding cancer genomes.

Keywords:
BioinformaticsCancer driversNon-coding driversSomatic mutation scoring

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Next-generation sequencing (NGS) reveals extensive somatic mutations in cancer genomes.
  • Prioritizing cancer-driving mutations from large datasets remains a significant challenge.
  • Current bioinformatics tools primarily focus on coding regions, neglecting non-coding variants.

Purpose of the Study:

  • To review computational approaches for prioritizing cancer-driving variants in the non-coding genome.
  • To highlight the importance of non-coding elements as potential cancer drivers.
  • To discuss methods for evaluating the deleteriousness of non-coding variants.

Main Methods:

  • Summary of existing methods for coding variant prioritization.
  • Review of diverse non-coding genomic elements implicated in cancer.
  • Description of recent computational methods for non-coding variant deleteriousness assessment.

Main Results:

  • Non-coding variants, including regulatory elements and non-coding RNAs, can act as cancer drivers.
  • Emerging computational tools facilitate the evaluation of non-coding variant impact.
  • Prioritization and identification of cancer-implicated non-coding elements are becoming feasible.

Conclusions:

  • Computational prioritization of non-coding cancer variants is critical for comprehensive cancer genome characterization.
  • Developing effective tools for non-coding variant analysis expands our understanding of cancer drivers.
  • This work facilitates the identification of novel regulatory elements and non-coding RNAs involved in oncogenesis.