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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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Identification of hypertension subtypes using microRNA profiles and machine learning.

Smarti Reel1, Parminder S Reel1, Josie Van Kralingen2

  • 1Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee DD2 4BF, United Kingdom.

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|March 19, 2025
PubMed
Summary
This summary is machine-generated.

This study shows that circulating microRNAs (miRNAs) can help distinguish endocrine hypertension (EHT) from primary hypertension (PHT). Machine learning identified specific miRNAs for accurate EHT subtype diagnosis.

Keywords:
diagnosticshypertensionmachine learningmicroRNA

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Area of Science:

  • Biomarker discovery
  • Genomics
  • Cardiovascular research

Background:

  • Hypertension affects 1 in 3 adults, with primary hypertension (PHT) being most common.
  • Endocrine hypertension (EHT), ~10% of cases, arises from conditions like primary aldosteronism (PA), Cushing's syndrome (CS), or pheochromocytoma/paraganglioma (PPGL).
  • EHT is often misdiagnosed, delaying treatment and leading to ineffective management.

Purpose of the Study:

  • To identify circulating microRNA (miRNA) biomarkers for distinguishing EHT and its subtypes from PHT.
  • To evaluate the effectiveness of machine learning (ML) in classifying hypertension types using miRNA profiles.

Main Methods:

  • Systematic analysis of circulating miRNA features.
  • Application of 8 supervised machine learning methods for classification and prediction.
  • Utilizing miRNA data to differentiate EHT subtypes (PA, CS, PPGL) from PHT.

Main Results:

  • Machine learning models achieved high accuracy: AUC of 0.9 for classifying PPGL, CS, and EHT from PHT.
  • Models achieved AUC of 0.8 for distinguishing PA from PHT.
  • Key circulating miRNAs identified include hsa-miR-15a-5p and hsa-miR-32-5p.

Conclusions:

  • Circulating miRNAs show significant potential as diagnostic biomarkers for EHT.
  • Machine learning is a viable tool for identifying informative miRNAs for hypertension diagnosis.