Details

Autor(en) / Beteiligte

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

Titel

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

Ist Teil von

• Frontiers in oncology, 2023-10, Vol.13, p.1255527-1255527

Ort / Verlag

Switzerland: Frontiers Media S.A

Erscheinungsjahr

2023

Quelle

EZB Free E-Journals

Beschreibungen/Notizen

• 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. 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. 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. This study proposes a miRNA-based biomarker classifier for SCLC that considers clinical demographics with specific cut offs to inform SCLC diagnosis.