Frontiers in Oncology (Oct 2023)

A plasma miRNA-based classifier for small cell lung cancer diagnosis

  • Michela Saviana,
  • Michela Saviana,
  • Giulia Romano,
  • Joseph McElroy,
  • Giovanni Nigita,
  • Rosario Distefano,
  • Robin Toft,
  • Federica Calore,
  • Patricia Le,
  • Daniel Del Valle Morales,
  • Sarah Atmajoana,
  • Stephen Deppen,
  • Kai Wang,
  • L. James Lee,
  • Mario Acunzo,
  • Patrick Nana-Sinkam

DOI
https://doi.org/10.3389/fonc.2023.1255527
Journal volume & issue
Vol. 13

Abstract

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IntroductionSmall cell lung cancer (SCLC) is characterized by poor prognosis and challenging diagnosis. Screening in high-risk smokers results in a reduction in lung cancer mortality, however, screening efforts are primarily focused on non-small cell lung cancer (NSCLC). SCLC diagnosis and surveillance remain significant challenges. The aberrant expression of circulating microRNAs (miRNAs/miRs) is reported in many tumors and can provide insights into the pathogenesis of tumor development and progression. Here, we conducted a comprehensive assessment of circulating miRNAs in SCLC with a goal of developing a miRNA-based classifier to assist in SCLC diagnoses.MethodsWe profiled deregulated circulating cell-free miRNAs in the plasma of SCLC patients. We tested selected miRNAs on a training cohort and created a classifier by integrating miRNA expression and patients’ clinical data. Finally, we applied the classifier on a validation dataset.ResultsWe determined that miR-375-3p can discriminate between SCLC and NSCLC patients, and between SCLC and Squamous Cell Carcinoma patients. Moreover, we found that a model comprising miR-375-3p, miR-320b, and miR-144-3p can be integrated with race and age to distinguish metastatic SCLC from a control group.DiscussionThis study proposes a miRNA-based biomarker classifier for SCLC that considers clinical demographics with specific cut offs to inform SCLC diagnosis.

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