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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 Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier MSC for Lung Cancer Screening
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Circulating microRNA-based screening tool for breast cancer.

Pierre Frères1,2, Stéphane Wenric2, Meriem Boukerroucha2

  • 1University Hospital (CHU), Department of Medical Oncology, Liège, Belgium.

Oncotarget
|January 7, 2016
PubMed
Summary

This study introduces a novel blood test using circulating microRNAs (miRNAs) as a minimally invasive screening tool for breast cancer detection. The developed 8-miRNA signature shows promise as an alternative to mammography.

Keywords:
biomarkersbreast cancercirculating microRNAsminimally invasive screening

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Area of Science:

  • Biochemistry
  • Oncology
  • Molecular Diagnostics

Background:

  • Circulating microRNAs (miRNAs) are emerging as significant biomarkers for various diseases, including breast cancer.
  • Current breast cancer screening methods like mammography have limitations, necessitating alternative diagnostic approaches.

Purpose of the Study:

  • To evaluate plasma miRNA levels as a potential non-invasive screening method for breast cancer diagnosis.
  • To develop and validate a diagnostic model based on a specific miRNA signature for breast cancer detection.

Main Methods:

  • Quantitative reverse transcription polymerase chain reaction (RT-qPCR) was used to measure plasma miRNA profiles in a cohort of 378 women.
  • A diagnostic model was constructed using the expression of 8 miRNAs and validated across multiple patient cohorts, including primary breast cancers, controls, remission, metastatic cases, and gynecologic tumors.
  • Multivariate supervised learning and random forest algorithms were employed to design the diagnostic tool.

Main Results:

  • The 8-miRNA random forest model achieved an area under the curve (AUC) of 0.81 in receiver operating characteristic analysis.
  • The diagnostic accuracy was maintained irrespective of patient age and tumor stage.
  • The model successfully identified metastatic breast cancer patients and showed similar prediction distributions to controls for patients in remission or with gynecologic cancers.

Conclusions:

  • A minimally invasive screening tool for breast cancer has been developed using a signature of 8 circulating miRNAs.
  • This miRNA-based diagnostic model demonstrates accuracy and potential as an alternative to mammography for breast cancer screening.
  • The findings support the utility of circulating miRNAs as reliable biomarkers for breast cancer diagnosis and management.