Unique Interplay between Molecular miR-181b/d Biomarkers and Health Related Quality of Life Score in the Predictive Glioma Models

In the last decade, an increasing amount of research has been conducted analyzing microRNA expression changes in glioma tissue and its expressed exosomes, but there is still sparse information on microRNAs or other biomarkers and their association with patients’ functional/psychological outcomes. In this study, we performed a combinational analysis measuring miR-181b and miR-181d expression levels by quantitative polymerase chain reaction (qPCR), evaluating isocitrate dehydrogenase 1 (IDH1) single nucleotide polymorphism (SNP), and O-6-methylguanine methyltransferase (MGMT) promoter methylation status in 92 post-surgical glioma samples and 64 serum exosomes, including patients’ quality of life evaluation applying European Organization for Research and Treatment of Cancer (EORTC) questionnaire for cancer patients (QLQ-30), EORTC the Brain Cancer-Specific Quality of Life Questionnaire (QLQ-BN20), and the Karnofsky performance status (KPS). The tumoral expression of miR-181b was lower in grade III and glioblastoma, compared to grade II glioma patients (p < 0.05). Additionally, for the first time, we demonstrated the association between miR-181 expression levels and patients’ quality of life. A positive correlation was observed between tumoral miR-181d levels and glioma patients’ functional parameters (p < 0.05), whereas increased exosomal miR-181b levels indicated a worse functional outcome (p < 0.05). Moreover, elevated miR-181b exosomal expression can indicate a significantly shorter post-surgical survival time for glioblastoma multiforme (GBM) patients. In addition, both tumoral and exosomal miR-181 expression levels were related to patients’ functioning and tumor-related symptoms. Our study adds to previous findings by demonstrating the unique interplay between molecular miR-181b/d biomarkers and health related quality of life (HRQOL) score as both variables remained significant in the predictive glioma models.