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Updated: Aug 22, 2025
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Clinicopathological Analysis of miRNA Expression in Breast Cancer Tissues by Using miRNA In Situ Hybridization
Published on: June 7, 2016
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Circulating miRNA Expression Profiling in Breast Cancer Molecular Subtypes: Applying Machine Learning Analysis in
Alexandra Triantafyllou1, Nikolaos Dovrolis2, Eleni Zografos3
11st Propaedeutic Surgical Department, Hippocration General Hospital, National and Kapodistrian University of Athens, Athens, Greece.
Cancer Diagnosis & Prognosis
|November 7, 2022
Summary
This study identified distinct circulating microRNA (miRNA) signatures for breast cancer subtypes using machine learning. These miRNA profiles offer insights into molecular characteristics for personalized breast cancer care.
Area of Science:
- Oncology
- Molecular Biology
- Genomics
Background:
- Breast cancer is a major global health concern, with molecular subtypes influencing treatment decisions.
- Understanding circulating microRNA (miRNA) profiles is crucial for personalized breast cancer management.
Purpose of the Study:
- To investigate differential circulating miRNA expression across breast cancer subtypes and healthy controls.
- To identify specific miRNA signatures for each breast cancer subtype.
- To uncover potential miRNA-associated target genes and molecular functions.
Main Methods:
- Serum samples from 66 breast cancer patients and 16 healthy controls were analyzed.
- MicroRNA expression profiling was performed using a miScript™ miRNA PCR Array.
- A machine learning approach was employed for miRNA profiling and subtype classification.
Main Results:
- miR-21 was a common circulating miRNA across all breast cancer subtypes.
- Distinct miRNA expression profiles were identified for Luminal A, Luminal B, HER2+, and Triple Negative breast cancer subtypes.
- Specific differentially expressed miRNAs were cataloged for each subtype.
Conclusions:
- Machine learning effectively delineates unique miRNA signatures for breast cancer molecular subtypes.
- These identified miRNA signatures hold potential clinical relevance for understanding breast cancer molecular characteristics.
