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Cross-comparison of high-throughput platforms for circulating mcroRNA in non small cell lung cancer - TaqMan OpenArray

ABSTRACT: This study focuses on platform comparison to assess performance variability in circulating microRNA (ct-miR) detection, agreement in assignment of a miR signature classifier (MSC) and concordance for the identification of cancer-associated miRs in plasma samples from non‐small cell lung cancer (NSCLC) patients. A plasma cohort of 10 NSCLC patients and 10 healthy donors matched for clinical features and MSC risk level was profiled for miRs expression using two sequencing- and three quantitative PCR (qPCR)-based platforms. Intra- and inter-platform variations were examined by correlation and concordance analysis. MSC risk levels were compared to those estimated using a reference method. Differentially expressed ct-miRs were identified among NSCLC patients and donors and the diagnostic value of those dysregulated in patients was assessed by receiver operating characteristic curve analysis. Downregulation of miR-150-5p was verified by qPCR. The Cancer Genome Atlas (TCGA) lung carcinoma dataset was used for validation at tissue level. Intra-platform reproducibility was consistent whereas the highest values of inter-platform correlations were among qPCR-based platforms. MSC classification concordance was >80% for four platforms. Dysregulation and discriminatory power of miR-150-5p and -210-3p were documented. Both were significantly dysregulated also on TCGA tissue-originated profiles from lung cell carcinoma in comparison to normal samples. Overall, our studies provide a large performance analysis between five different platforms for miRs quantification, indicate the solidity of MSC classifier and identify two noninvasive biomarkers for NSCLC

ORGANISM(S): Homo sapiens

PROVIDER: GSE203510 | GEO | 2022/08/10

REPOSITORIES: GEO

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Project description:Lifestyle disorders like obesity, type 2 diabetes (T2D), and cardiovascular diseases can be prevented and treated by regular physical activity. During exercise, skeletal muscles release signaling factors that communicate with other organs and mediate beneficial effects of exercise. These factors include myokines, metabolites, and extracellular vesicles (EVs). In the present study, we have examined how electrical pulse stimulation (EPS) of myotubes, a model of exercise, affects the cargo of released EVs. Chronic low frequency EPS was applied for 24 h to human myotubes isolated and differentiated from biopsy samples from 6 morbidly obese females with T2D, and EVs, both exosomes and microvesicles (MV), were isolated from cell media 24 h thereafter. Size and concentration of EV subtypes were characterized by nanoparticle tracking analysis, surface markers were examined by flow cytometry and Western blotting, and morphology was confirmed by transmission electron microscopy. Protein content was assessed by high-resolution proteomic analysis (LC-MS/MS), non-coding RNA was quantified by Affymetrix microarray, and selected microRNAs (miRs) validated by real time RT-qPCR. The size and concentration of exosomes and MV were unaffected by EPS. Of the 400 miRs identified in the EVs, EPS significantly changed the level of 15 exosome miRs, of which miR-1233-5p showed the highest fold change. The miR pattern of MV was unaffected by EPS. Totally, about 1000 proteins were identified in exosomes and 2000 in MV. EPS changed the content of 73 proteins in exosomes, 97 in MVs, and of these 4 were changed in both exosomes and MV (GANAB, HSPA9, CNDP2, and ATP5B). By matching the EPS-changed miRs and proteins in exosomes, 31 targets were identified, and among these several promising signaling factors. Of particular interest were CNDP2, an enzyme that generates the appetite regulatory metabolite Lac-Phe, and miR-4433b-3p, which targets CNDP2. Several of the regulated miRs, such as miR-92b-5p, miR-320b, and miR-1233-5p might also mediate interesting signaling functions.

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Cross-comparison of high-throughput platforms for circulating mcroRNA in non small cell lung cancer - TaqMan OpenArray

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