A plasma miRNA-based classifier for small cell lung cancer diagnosis [miRNA-Seq]
Objectives: 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 biomarker classifier to assist in SCLC diagnoses. Materials and Methods: We profiled deregulated circulating cell-free miRNA in the plasma of SCLC patients. We tested selected miRs on a training cohort and created a classifier by integrating miRNA expression and patient 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.  Conclusion: This study proposes a miRNA-based biomarker classifier for SCLC that considers clinical demographics with specific cut offs to inform SCLC diagnosis. Overall design: A total of 38 samples were used for miRNA extraction from plasma. The cohort comprises 10 high-risk smokers that were used as control samples, 10 adenocarcinomas, 10 squamous cell carcinomas, and 8 SCLC patients. The RNA was purified from 200 uL of plasma and NGS for small RNA was performed.