1. A 3-miRNA Signature Enables Risk Stratification in Glioblastoma Multiforme Patients with Different Clinical Outcomes
- Author
-
Vivi Bafiti, Sotiris Ouzounis, Constantina Chalikiopoulou, Eftychia Grigorakou, Ioanna Maria Grypari, Gregory Gregoriou, Andreas Theofanopoulos, Vasilios Panagiotopoulos, Evangelia Prodromidi, Dionisis Cavouras, Vasiliki Zolota, Dimitrios Kardamakis, and Theodora Katsila
- Subjects
glioblastoma multiforme ,3-microRNA signature ,risk stratification ,machine learning ,image classification ,pattern recognition ,Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,RC254-282 - Abstract
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 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.
- Published
- 2022
- Full Text
- View/download PDF