microRNAs derived from circulating exosomes as noninvasive biomarkers for screening and diagnosing lung cancer

Abstract

Introduction: Lung cancer is the highest cause of mortality among tumor pathologies worldwide. There are no validated techniques for an early detection of pulmonary cancer lesions other than low-dose helical computed tomography scan. Unfortunately, this method has some negative effects. Recent studies have laid the basis for development of exosomes-based techniques to screen/diagnose lung cancers. As the isolation of circulating exosomes is a minimally invasive procedure, this technique opens new possibilities for diagnostic applications.

Methods: We used a first set of 30 plasma samples from as many patients, including 10 patients affected by lung adenocarcinomas, 10 with lung granulomas, and 10 healthy smokers matched for age and sex as negative controls. Wide-range microRNAs analysis (742 microRNAs) was performed by quantitative real time polymerase chain reaction. Data were compared on the basis of lesion characteristics, using WEKA software for statistics and modeling. Subsequently, selected microRNAs were evaluated on an independent larger group of samples (105 specimens: 50 lung adenocarcinomas, 30 lung granulomas, and 25 healthy smokers).

Results: This analysis led to the selection of four microRNAs to perform a screening test (miR-378a, miR-379, miR-139-5p, and miR-200b-5p), useful to divide population into two groups: nodule (lung adenocarcinomas + carcinomas) and non-nodule (healthy former smokers). Six microRNAs (miR-151a-5p, miR-30a-3p, miR-200b-5p, miR-629, miR-100, and miR-154-3p) were selected for a second test on the nodule population to discriminate between lung adenocarcinoma and granuloma.

Conclusions: The screening test showed 97.5% sensitivity, 72.0% specificity, and area under the curve receiver operating characteristic of 90.8%. The diagnostic test had 96.0% sensitivity, 60.0% specificity, and area under the curve receiver operating characteristic of 76.0%. Further evaluation is needed to confirm the predictive power of these models on larger cohorts of samples.

Figure 1. 1a and 1b: Exosomes stability in time

We analyzed exosomes stability in frozen samples using raw Ct of mir-20a, mir-221 and let-7a. Graphs show RT-PCR resulting raw Ct of the same plasma sample, divided in several aliquots and RNA extracted from fresh plasma aliquot and 24 hours, 48 hours, one week, one and two months frozen plasma aliquots. Graph (a) shows raw Cts of the various aliquots and the duplicate tests divided by microRNAs. Graph (b) compares microRNAs trend in time. There is no detectable oscillation of microRNAs raw Cts from fresh aliquot to 2 months frozen one.

Figure 1. 1a and 1b: Exosomes stability in time

We analyzed exosomes stability in frozen samples using raw Ct of mir-20a, mir-221 and let-7a. Graphs show RT-PCR resulting raw Ct of the same plasma sample, divided in several aliquots and RNA extracted from fresh plasma aliquot and 24 hours, 48 hours, one week, one and two months frozen plasma aliquots. Graph (a) shows raw Cts of the various aliquots and the duplicate tests divided by microRNAs. Graph (b) compares microRNAs trend in time. There is no detectable oscillation of microRNAs raw Cts from fresh aliquot to 2 months frozen one.

Figure 2. MicroRNAs expression levels overview

After data set normalization, the log₁₀ of 2^(-ΔΔCt) was taken each microRNAs. The graph show expression levels value of each microRNAs, one for Lung Adenocarcinomas from Healthy Controls (reported as “Cancer” bars in the graph) and another for Lung Granulomas from Healthy Controls (reported as “Granuloma” bars in the graph). Concerning Lung Granulomas expression levels, 3 to 14 microRNAs showed slightly downregulation, 10 microRNAs were found upregulated. MiR-151a-5p has shown no expression levels compared to normal donors. All 14 microRNAs resulted upregulated in Lung Adenocarcinomas expression levels.

Figure 3. Screening model ROC curve

Receiver operating characteristic (ROC) plot for screening model microRNAs set. Screening model distinguish between control subjects and patients with any kind of nodules (Lung Adenocarcinomas and Granulomas) with an AUC = 0.908 (p<0.001).

Figure 4. Diagnosis model ROC curve

Receiver operating characteristic (ROC) plot for diagnostic model microRNAs set. Diagnostic model distinguish between Lung Adenocarcinomas and Granulomas patients with an AUC = 0.760 (p<0.001).