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A 3-miRNA Signature Enables Risk Stratification in Glioblastoma Multiforme Patients with Different Clinical Outcomes

Malignant gliomas constitute a complex disease phenotype that demands optimum decision-making as they are highly heterogeneous. Such inter-individual variability also renders optimum patient stratification extremely difficult. microRNA (hsa-miR-20a, hsa-miR-21, hsa-miR-21) expression levels were det...

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Detalles Bibliográficos
Autores principales: Bafiti, Vivi, Ouzounis, Sotiris, Chalikiopoulou, Constantina, Grigorakou, Eftychia, Grypari, Ioanna Maria, Gregoriou, Gregory, Theofanopoulos, Andreas, Panagiotopoulos, Vasilios, Prodromidi, Evangelia, Cavouras, Dionisis, Zolota, Vasiliki, Kardamakis, Dimitrios, Katsila, Theodora
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9221847/
https://www.ncbi.nlm.nih.gov/pubmed/35735454
http://dx.doi.org/10.3390/curroncol29060345
Descripción
Sumario:Malignant gliomas constitute a complex disease phenotype that demands optimum decision-making as they are highly heterogeneous. Such inter-individual variability also renders optimum patient stratification extremely difficult. microRNA (hsa-miR-20a, hsa-miR-21, hsa-miR-21) expression levels were determined by RT-qPCR, upon FFPE tissue sample collection of glioblastoma multiforme patients (n = 37). In silico validation was then performed through discriminant analysis. Immunohistochemistry images from biopsy material were utilized by a hybrid deep learning system to further cross validate the distinctive capability of patient risk groups. Our standard-of-care treated patient cohort demonstrates no age- or sex- dependence. The expression values of the 3-miRNA signature between the low- (OS > 12 months) and high-risk (OS < 12 months) groups yield a p-value of <0.0001, enabling risk stratification. Risk stratification is validated by a. our random forest model that efficiently classifies (AUC = 97%) patients into two risk groups (low- vs. high-risk) by learning their 3-miRNA expression values, and b. our deep learning scheme, which recognizes those patterns that differentiate the images in question. Molecular-clinical correlations were drawn to classify low- (OS > 12 months) vs. high-risk (OS < 12 months) glioblastoma multiforme patients. Our 3-microRNA signature (hsa-miR-20a, hsa-miR-21, hsa-miR-10a) may further empower glioblastoma multiforme prognostic evaluation in clinical practice and enrich drug repurposing pipelines.