2023 World Conference on Lung Cancer (Posters)-EP07.04. Free Circulating and Extracellular Vesicles-Derived microRNA as Prognostic Biomarkers in Resected Early-Stage NSCLC: RESTING Study Results

EP07.04. Free Circulating and Extracellular Vesicles-Derived microRNA as Prognostic Biomarkers in Resected Early-Stage NSCLC: RESTING Study Results - PDF(Slides)

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The RESTING study aimed to investigate the prognostic potential of free circulating (FC) and extracellular vesicles (EVs)-derived microRNA (miRNAs) as biomarkers in resected early-stage non-small cell lung cancer (NSCLC) patients. A total of 222 patients were enrolled in the study, and peripheral blood samples were collected pre-surgery for miRNA analysis. The study found that two FC-miRNAs and five EV-miRNAs were significantly associated with disease-free survival (DFS). <br /><br />In terms of clinical characteristics, the study found that males, age over 70, and diabetic condition were associated with a higher risk of relapse. The combination of EV-miRNAs and clinical characteristics showed similar discriminatory and stratification capacity, while the combination of FC-miRNAs and clinical characteristics performed significantly worse. <br /><br />Pathway enrichment analysis revealed that higher values of certain miRNAs were predictive of worse DFS, and these miRNAs were associated with pathways such as signaling by SCF-KIT, cellular responses to stress, and immune system. <br /><br />The study concluded that FC-miRNAs and EV-miRNAs have the potential to identify early-stage NSCLC patients at a higher risk of relapse or death after surgery. This non-invasive, quick, and low-cost approach could aid in patient stratification and help clinicians make more accurate therapeutic decisions.

Asset Subtitle

Paola Ulivi

Meta Tag

Speaker Paola Ulivi

Topic Early-Stage NSCLC: Progress in Pathology

Keywords

RESTING study

prognostic potential

free circulating miRNAs

extracellular vesicles

biomarkers

resected early-stage NSCLC

disease-free survival

clinical characteristics

pathway enrichment analysis

patient stratification