Small extracellular vesicle-derived miRNAs as diagnostic biomarkers for differentiating between different pathological types of early lung cancer

Lung cancer is the leading cause of cancer-related death worldwide. MicroRNAs (miRNAs) in circulating small extracellular vesicles (sEVs) have been suggested to be potential biomarkers for cancer diagnosis. The present study was designed to explore whether plasma-derived sEV miRNAs could be utilized as diagnostic biomarkers for differentiating between early-stage small cell lung cancer (SCLC) and early-stage non-small cell lung cancer (NSCLC). Researchers from Hebei Medical University compared the miRNA profiles of plasma-derived sEVs from healthy individuals, patients with early-stage SCLC and patients with early-stage NSCLC. Next-generation sequencing was used to screen for differentially expressed miRNAs (DEMs). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were used to predict the potential functions of these DEMs. Weighted gene coexpression network analysis (WGCNA) was used to identify the different pathology-related miRNA modules. The researchers found that 22 DEMs were significantly different among healthy individuals, patients with early-stage SCLC, and patients with early-stage NSCLC. They selected six representative DEMs for validation by qRT‒PCR, which confirmed that miRNA-483-3p derived from plasma sEVs could be used as a potential biomarker for the diagnosis of early-stage SCLC, miRNA-152-3p and miRNA-1277-5p could be used for the diagnosis of early-stage NSCLC respectively.

Plasma sEV-enriched fraction-derived miRNAs profiles
that could be used to discriminate SCLC from NSCLC

(a) Venn diagram comparing the differential expression of sEV-derived miRNAs, each circle representing the number of differentially expressed sEV-derived miRNAs between two conditions. (b) Heat map of the 22 DEM expression levels across all 35 samples in our plasma sEV-enriched fraction miRNA dataset. (c) The specificity and sensitivity of each DEM for identifying SCLC versus NSCLC patients in our plasma sEV-enriched fraction miRNA dataset.