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A panel of miRNAs derived from plasma extracellular vesicles as novel diagnostic biomarkers of lung adenocarcinoma.

ABSTRACT: Lung cancer is the leading cause of cancer-related morbidity and mortality worldwide, with lung adenocarcinoma (LUAD) being the most common histological subtype (approximately 40%). In the absence of reliable screening biomarkers for early diagnosis, most patients with LUAD are inevitably diagnosed at an advanced stage. MicroRNAs (miRNAs) encapsulated within plasma-derived extracellular vesicles (EVs) may be suitable for use as noninvasive diagnostic biomarkers for aggressive malignancies, including LUAD. In this study, we first investigated the miRNA profiles of plasma-derived EVs from LUAD patients and healthy donors, and then systematically evaluated the expression patterns of selected plasma-derived EV miRNAs in a large cohort of patients with LUAD and healthy controls. Notably, we observed that miR-451a, miR-194-5p, and miR-486-5p were significantly increased in EVs from LUAD patients, compared to healthy controls. The area under the curve values for the three miRNAs were 0.9040 (95% confidence interval [CI], 0.8633-0.9447) for miR-451a, 0.7492 (95% CI, 0.6992-0.7992) for miR-194-5p, and 0.9574 (95% CI, 0.9378-0.9769) for miR-486-5p, while the AUC of the combination of these three miRNAs was 0.9650. Thus, these results suggest that these EV miRNAs may be promising candidates for the development of highly effective, noninvasive biomarkers for early LUAD diagnosis.

SUBMITTER: Yao B

PROVIDER: S-EPMC6886307 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Project description:Lung adenocarcinoma (LUAD) is one of the most common cancers and lethal diseases in the world. Recognition of the undetermined lung nodules at an early stage is useful for a favorable prognosis. However, there is no good method to identify the undetermined lung nodules and predict their clinical outcome. DNA methylation alteration is frequently observed in LUAD and may play important roles in carcinogenesis, diagnosis, and prediction. This study took advantage of publicly available methylation profiling resources and a machine learning method to investigate methylation differences between LUAD and adjacent non-malignant tissue. The prediction panel was first constructed using 338 tissue samples from LUAD patients including 149 non-malignant ones. This model was then validated with data from The Cancer Genome Atlas database and clinic samples. As a result, the methylation status of four CpG loci in homeobox A9 (HOXA9), keratin-associated protein 8-1 (KRTAP8-1), cyclin D1 (CCND1), and tubby-like protein 2 (TULP2) were highlighted as informative markers. A random forest classification model with an accuracy of 94.57% and kappa of 88.96% was obtained. To evaluate this panel for LUAD, the methylation levels of four CpG loci in HOXA9, KRTAP8-1, CCND1, and TULP2 of tumor samples and matched adjacent lung samples from 25 patients with LUAD were tested. In these LUAD patients, the methylation of HOXA9 was significantly upregulated, whereas the methylation of KRTAP8-1, CCND1, and TULP2 were downregulated obviously in tumor samples compared with adjacent tissues. Our study demonstrates that the methylation of HOXA9, KRTAP8-1, CCND1, and TULP2 has great potential for the early recognition of LUAD in the undetermined lung nodules. The findings also exhibit that the application of improved mathematic algorithms can yield accurate and particularly robust and widely applicable marker panels. This approach could greatly facilitate the discovery process of biomarkers in various fields.

Project description:BackgroundThe differential diagnosis of pancreatic ductal adenocarcinoma (PDAC) from chronic pancreatitis (CP) is clinically challenging due to a lack of minimally invasive diagnosis methods. MicroRNAs (miRNAs) derived from small extracellular vesicles (EVs) in the blood have been reported as a promising diagnosis biomarker for various types of cancer. However, blood small EV miRNA signatures and their diagnostic value to differentiate between PDAC and CP remain to be determined.MethodsIn this study, 107 patients with PDAC or CP were recruited, and 90 patients were finally enrolled for a training cohort (n = 48) and test cohort (n = 42). Small RNA sequencing was used to assess the expression of blood small EV miRNAs in these patients.ResultsThe linear model from the differentially expressed blood small EV miR-95-3p divided by miR-26b-5p showed an average sensitivity of 84.1% and an average specificity of 96.6% to identify PDAC from CP in the training cohort and the test cohort, respectively. When the model was combined with serum carbohydrate antigen 19-9 (CA19-9), the average sensitivity increased to 96.5%, and the average specificity remained at 96.4% of both cohorts, which demonstrated the best performance of all the published biomarkers for distinguishing between PDAC and CP. The causal analysis performed using the Bayesian network demonstrated that miR-95-3p was associated with a "consequence" of "cancer" and miR-26b-5p as a "cause" of "pancreatitis." A subgroup analysis revealed that blood small EV miR-335-5p/miR-340-5p could predict metastases in both cohorts and was associated with an overall survival (p = 0.020).ConclusionsThis study indicated that blood small EV miR-95-3p/miR-26b-5p and its combination with serum levels of CA19-9 could separate PDAC from CP, and miR-335-5p/miR-340-5p was identified to associate with PDAC metastasis and poor prognosis. These results suggested the potentiality of blood small EV miRNAs as differential diagnosis and metastases biomarkers of PDAC.

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A panel of miRNAs derived from plasma extracellular vesicles as novel diagnostic biomarkers of lung adenocarcinoma.

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