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A Diagnostic Classifier Based on Circulating miRNA Pairs for COPD Using a Machine Learning Approach.

Shurui Xuan1, Jiayue Zhang2, Qinxing Guo1

  • 1Department of Respiratory & Critical Care Medicine, The First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou Road, Nanjing 210029, China.

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|May 16, 2023
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Summary

Early detection of chronic obstructive pulmonary disease (COPD) is crucial. This study developed a machine learning model using circulating microRNAs (miRNAs) to effectively screen for COPD, achieving high diagnostic accuracy.

Keywords:
COPDdiagnostic modelmachine learningmiRNA

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Area of Science:

  • Biomarkers
  • Pulmonary Medicine
  • Bioinformatics

Background:

  • Chronic obstructive pulmonary disease (COPD) is frequently underdiagnosed, necessitating early detection to prevent disease progression.
  • Circulating microRNAs (miRNAs) show promise as diagnostic biomarkers for various diseases, but their utility in COPD requires further validation.
  • Established diagnostic methods for COPD may lack the sensitivity for early-stage detection.

Purpose of the Study:

  • To develop and validate a robust diagnostic model for COPD using circulating miRNA expression profiles.
  • To identify specific miRNA pairs with significant diagnostic value for COPD.
  • To create a user-friendly tool for clinical application in COPD screening.

Main Methods:

  • Analysis of circulating miRNA expression profiles from independent COPD and healthy control cohorts.
  • Construction of a miRNA pair-based matrix to enhance diagnostic power.
  • Development and comparison of multiple machine learning algorithms, including LightGBM, for model construction.
  • Validation of the optimal model's performance using external datasets and calculation of the area under the curve (AUC).

Main Results:

  • Individual miRNA expression levels showed insufficient diagnostic value for COPD.
  • Five key miRNA pairs were identified as significant diagnostic indicators.
  • The LightGBM-based machine learning model demonstrated strong predictive performance with AUC values of 0.883 (test) and 0.794 (validation).
  • A web-based diagnostic assistance tool was developed.

Conclusions:

  • A robust machine learning model utilizing circulating miRNA pairs offers a promising approach for COPD screening.
  • The developed model and associated tool can aid clinicians in the early diagnosis of COPD.
  • Further research into the biological pathways implicated by the model may reveal novel insights into COPD pathogenesis.