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Related Concept Videos

MicroRNAs01:22

MicroRNAs

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 ends...
MicroRNAs01:22

MicroRNAs

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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Related Experiment Video

Updated: Jun 8, 2026

MicroRNA Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier (MSC) for Lung Cancer Screening
08:14

MicroRNA Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier (MSC) for Lung Cancer Screening

Published on: October 26, 2017

Development of a miRNA-Based Model for Lung Cancer Detection.

Kai Chin Poh1, Toh Ming Ren1, Goh Liuh Ling2

  • 1Division of Respiratory Medicine, Sengkang General Hospital, Singapore 544886, Singapore.

Cancers
|March 28, 2025
PubMed
Summary

This study shows that combining serum microRNA (miRNA) biomarkers with lung nodule size significantly improves lung cancer detection accuracy. This integrated approach offers a promising tool for enhancing current lung cancer screening protocols.

Keywords:
biomarkerslow-dose computed tomographylung cancer screeningmiRNAmicroRNA

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Last Updated: Jun 8, 2026

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Area of Science:

  • Oncology
  • Biomarker Discovery
  • Medical Diagnostics

Background:

  • Lung cancer remains a leading global cause of cancer mortality, often diagnosed at late stages.
  • Current low-dose computed tomography (LDCT) screening has limitations like high false-positive rates.
  • Serum microRNA (miRNA) biomarkers present a potential non-invasive adjunct for early lung cancer detection.

Purpose of the Study:

  • To evaluate the predictive capability of serum miRNA biomarkers for lung cancer detection.
  • To identify key miRNA biomarkers using machine learning techniques.
  • To assess the combined predictive power of miRNA biomarkers and lung nodule characteristics.

Main Methods:

  • A case-control study involving 82 lung cancer patients and 123 controls.
  • Literature review to select 25 candidate miRNAs, with 16 showing significant differential expression.
  • Machine learning algorithms (Random Forest, KNN, NN, SVM) used to identify top six miRNAs.

Main Results:

  • A prediction model with six miRNAs achieved AUCs of 0.78-0.86.
  • Incorporating lung nodule size into the model significantly boosted performance (AUCs 0.96-0.99).
  • High sensitivity (92-98%) and specificity (93-98%) were achieved with the combined model.

Conclusions:

  • A combined prediction model of serum miRNAs and nodule size demonstrates high accuracy for lung cancer detection.
  • This integrated model holds potential for improving patient outcomes within existing screening programs.