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Article

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

1
Institute of Chemical Biology, National Hellenic Research Foundation, 11635 Athens, Greece
2
Biomedical Engineering Department, University of West Attica, 11243 Athens, Greece
3
Department of Pathology, School of Medicine, University of Patras, 26504 Patras, Greece
4
American Community Schools (ACS), 15234 Athens, Greece
5
Department of Neurosurgery, University Hospital of Patras, 26504 Patras, Greece
6
Department of Radiation Oncology, University of Patras Medical School, 26504 Patras, Greece
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Curr. Oncol. 2022, 29(6), 4315-4331; https://doi.org/10.3390/curroncol29060345
Received: 29 April 2022 / Revised: 10 June 2022 / Accepted: 13 June 2022 / Published: 16 June 2022
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. View Full-Text
Keywords: glioblastoma multiforme; 3-microRNA signature; risk stratification; machine learning; image classification; pattern recognition glioblastoma multiforme; 3-microRNA signature; risk stratification; machine learning; image classification; pattern recognition
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MDPI and ACS Style

Bafiti, V.; Ouzounis, S.; Chalikiopoulou, C.; Grigorakou, E.; Grypari, I.M.; Gregoriou, G.; Theofanopoulos, A.; Panagiotopoulos, V.; Prodromidi, E.; Cavouras, D.; Zolota, V.; Kardamakis, D.; Katsila, T. A 3-miRNA Signature Enables Risk Stratification in Glioblastoma Multiforme Patients with Different Clinical Outcomes. Curr. Oncol. 2022, 29, 4315-4331. https://doi.org/10.3390/curroncol29060345

AMA Style

Bafiti V, Ouzounis S, Chalikiopoulou C, Grigorakou E, Grypari IM, Gregoriou G, Theofanopoulos A, Panagiotopoulos V, Prodromidi E, Cavouras D, Zolota V, Kardamakis D, Katsila T. A 3-miRNA Signature Enables Risk Stratification in Glioblastoma Multiforme Patients with Different Clinical Outcomes. Current Oncology. 2022; 29(6):4315-4331. https://doi.org/10.3390/curroncol29060345

Chicago/Turabian Style

Bafiti, Vivi, Sotiris Ouzounis, Constantina Chalikiopoulou, Eftychia Grigorakou, Ioanna M. Grypari, Gregory Gregoriou, Andreas Theofanopoulos, Vasilios Panagiotopoulos, Evangelia Prodromidi, Dionisis Cavouras, Vasiliki Zolota, Dimitrios Kardamakis, and Theodora Katsila. 2022. "A 3-miRNA Signature Enables Risk Stratification in Glioblastoma Multiforme Patients with Different Clinical Outcomes" Current Oncology 29, no. 6: 4315-4331. https://doi.org/10.3390/curroncol29060345

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