Expression patterns of circulating microRNAs in classification models: New opportunities in brain tumor diagnosis.

Abstract

2051 Background: Diffuse gliomas are the most common primary brain tumors and, according to the 2021 WHO classification, are divided into three main types: IDH-wild-type glioblastomas, IDH-mutant astrocytomas, and IDH-mutant oligodendrogliomas with 1p/19q codeletion. Oligodendrogliomas are neuroepithelial tumors of the central nervous system that have histological and molecular characteristics distinct from other subtypes of diffuse gliomas. The problem of early diagnosis of oligodendrogliomas, due to their location, requires the search for accessible and informative markers. Circulating microRNAs in blood plasma appear to be the most promising in this aspect. The purpose of the study was to develop a model for the diagnosis of oligodendrogliomas based on the expression profiles of microRNAs circulating in blood plasma. Methods: The study included 251 participants: 84 patients with glioblastomas, 51 patients with astrocytomas, 12 patients with oligodendrogliomas, 47 patients with meningiomas, 14 patients with lung cancer metastases, 25 patients with breast cancer metastases, and 18 healthy donors. The miRNeasy Serum/Plasma Kit (Qiagen, Germany) was used to isolate the microRNA fraction from blood plasma. The expression levels of hsa-miR-30c-5p, hsa-miR-186-5p, hsa-miR-194-5p, hsa-miR-484, hsa-miR-19b-3p, hsa-miR-431-5p, hsa-miR-363-3p, hsa-miR-128-3p, hsa-miR-3168, hsa-miR-192-5p, hsa-let-7c-5p, hsa-miR-340-5p, hsa-miR-182-5p and hsa-miR-144-5p were measured by stem loop RT-qPCR. Results: The model for diagnosing cases with oligodendrogliomas was as follows: K o =-1.59×hsa-miR-128-3p-1.97×hsa-miR-194-5p+1.78×hsa-miR-19b-3p-2.47×hsa-miR-3168+3.41×hsa-let-7c-5p+0.91×hsa-miR-192-5p+1.39×hsa-miR-340-5p-1.8, where K o is the coefficient used to differentiate cases with oligodendrogliomas from other cases. The optimal cut-off value for K o was 0.38 with a Youden index of 0.86. Model validation on the test dataset revealed that cases with oligodendrogliomas typically have calculated coefficient values ≥ 0.38. ROC analysis yielded the following model metrics: sensitivity – 100%, specificity – 85.7%, accuracy – 92.9%, and AUC – 0.93. Conclusions: The developed model opens up new opportunities for the early diagnosis of oligodendrogliomas and may eventually become part of a test system for the differential diagnosis of brain tumors.