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

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*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Introduction: Small 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. Methods: We 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. Results: We 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. Discussion: This study proposes a miRNA-based biomarker classifier for SCLC that considers clinical demographics with specific cut offs to inform SCLC diagnosis.

Original languageEnglish
Article number1255527
JournalFrontiers in Oncology
StatePublished - 2023


  • biomarkers
  • classifier
  • microRNAs
  • oncology
  • small cell lung cancer


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