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Related Concept Videos

MicroRNAs01:22

MicroRNAs

3.4K
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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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...
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Related Experiment Video

Updated: Nov 27, 2025

Clinicopathological Analysis of miRNA Expression in Breast Cancer Tissues by Using miRNA In Situ Hybridization
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Machine Learning Based Network Analysis Determined Clinically Relevant miRNAs in Breast Cancer.

Min Qiu1, Qin Fu1, Chunjie Jiang2,3

  • 1Department of Orthopedics, Shengjing Hospital of China Medical University, Shenyang, China.

Frontiers in Genetics
|December 7, 2020
PubMed
Summary

This study identifies 90 breast cancer risk microRNAs (miRNAs) using a novel network analysis. These risk miRNAs and their associated genes can predict patient survival, immune cell infiltration, and drug response, aiding in targeted breast cancer therapies.

Keywords:
SVM classifierbreast cancerdrug responseimmune infiltrationmiRNA

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Area of Science:

  • Genomics and Bioinformatics
  • Cancer Biology
  • Molecular Oncology

Background:

  • MicroRNAs (miRNAs) are crucial in breast cancer progression.
  • Previous network analyses often relied solely on expression data.
  • A more comprehensive approach is needed to understand dysregulated miRNA networks.

Purpose of the Study:

  • To construct a novel dysregulated miRNA target network (DMTN) for breast cancer.
  • To identify key microRNAs (miRNAs) associated with breast cancer risk.
  • To explore the clinical relevance of identified miRNAs in predicting patient outcomes and treatment response.

Main Methods:

  • Spearman correlation calculated miRNA-mRNA relationships in breast and normal tissues.
  • A dysregulated miRNA target network (DMTN) was built using significant dysregulation scores.
  • Support Vector Machine (SVM) classifier predicted breast cancer risk miRNAs.

Main Results:

  • A DMTN of 511 miRNAs was constructed.
  • 90 breast cancer risk miRNAs were identified using SVM.
  • Risk miRNAs and neighboring genes correlated with patient survival, immune cell infiltration, and drug sensitivity.

Conclusions:

  • 90 breast cancer risk miRNAs were identified via DMTN and SVM.
  • These miRNAs are biologically and clinically significant for breast cancer.
  • Identified miRNAs and genes serve as potential biomarkers for immunotherapy and targeted therapy.