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Related Concept Videos

MicroRNAs01:22

MicroRNAs

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MicroRNA (miRNA) are short, regulatory RNA transcribed from introns (non-coding regions of a gene) or intergenic regions (stretches of DNA present between genes). Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself, forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After the pre-miRNA...
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MicroRNA (miRNA) are short, regulatory RNA transcribed from introns—non-coding regions of a gene—or intergenic regions—stretches of DNA present between genes. Several processing steps are required to form biologically active, mature miRNA. The initial transcript, called primary miRNA (pri-mRNA), base-pairs with itself forming a stem-loop structure. Within the nucleus, an endonuclease enzyme, called Drosha, shortens the stem-loop structure into hairpin-shaped pre-miRNA. After...
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MicroRNA Amplification and Recognition through Locked-nucleic-acid In situ Hybridization as A Novel Detection and Quantification Method
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mirPLS: a partial linear structure identifier method for cancer subtyping using microRNAs.

Peifeng Ruan1, Shuang Wang2, Hua Liang1

  • 1Department of Statistics, Columbian College of Arts and Sciences, The George Washington University, Washington, DC 20052, USA.

Bioinformatics (Oxford, England)
|July 2, 2020
PubMed
Summary
This summary is machine-generated.

The novel mirPLS tool identifies microRNAs (miRNAs) with complex associations for accurate cancer subtyping. This method improves patient survival prediction and biological understanding in diverse cancer types.

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Area of Science:

  • Bioinformatics
  • Genomics
  • Cancer Research

Background:

  • MicroRNAs (miRNAs) are small non-coding RNAs implicated in various cancers.
  • Differential expression of miRNAs across cancer subtypes presents complex, non-linear associations.
  • Identifying these non-linear miRNA-cancer associations is crucial for precise cancer subtyping.

Purpose of the Study:

  • To introduce mirPLS, a Partial Linear Structure identifier for miRNA data.
  • To simultaneously identify both linear and non-linear miRNA associations with cancer status.
  • To leverage non-linearly associated miRNAs for improved cancer subtyping.

Main Methods:

  • Developed mirPLS, a Partial Linear Structure identifier.
  • Conducted simulation studies to evaluate mirPLS performance against existing methods.
  • Applied mirPLS to miRNA data from three distinct cancer types.

Main Results:

  • mirPLS accurately identifies both linearly and non-linearly outcome-associated miRNAs.
  • Utilizing non-linearly associated miRNAs significantly enhances cancer subtyping accuracy.
  • Cancer subtypes identified by mirPLS-derived miRNAs show improved survival prediction and biological relevance.

Conclusions:

  • mirPLS is an effective tool for identifying complex miRNA-cancer associations.
  • The identified non-linear miRNA associations are valuable for cancer subtyping and prognostication.
  • The R package mirPLS is publicly available for broader research application.