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Article

A New Epigenetic Model to Stratify Glioma Patients According to Their Immunosuppressive State

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Experimental and Clinical Pharmacology Unit, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, 33081 Aviano, Italy
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Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy
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Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Division of Molecular Oncology, 33081 Aviano, Italy
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Department of Life Sciences, University of Trieste, 34127 Trieste, Italy
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Department of Chemical and Pharmaceutical Sciences, University of Trieste, Via L. Giorgieri 1, 34127 Trieste, Italy
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Bioinformatics and Statistical Genomics Unit, Istituto Auxologico Italiano IRCCS, 20095 Cusano Milanino, Italy
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Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy
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Neurosurgery Unit, Department of Neuroscience, Santa Maria della Misericordia University Hospital, 33100 Udine, Italy
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Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Department of Radiotherapy, 33081 Aviano, Italy
*
Author to whom correspondence should be addressed.
Academic Editor: Fabrizio Mattei
Cells 2021, 10(3), 576; https://doi.org/10.3390/cells10030576
Received: 25 January 2021 / Revised: 27 February 2021 / Accepted: 28 February 2021 / Published: 5 March 2021
Gliomas are the most common primary neoplasm of the central nervous system. A promising frontier in the definition of glioma prognosis and treatment is represented by epigenetics. Furthermore, in this study, we developed a machine learning classification model based on epigenetic data (CpG probes) to separate patients according to their state of immunosuppression. We considered 573 cases of low-grade glioma (LGG) and glioblastoma (GBM) from The Cancer Genome Atlas (TCGA). First, from gene expression data, we derived a novel binary indicator to flag patients with a favorable immune state. Then, based on previous studies, we selected the genes related to the immune state of tumor microenvironment. After, we improved the selection with a data-driven procedure, based on Boruta. Finally, we tuned, trained, and evaluated both random forest and neural network classifiers on the resulting dataset. We found that a multi-layer perceptron network fed by the 338 probes selected by applying both expert choice and Boruta results in the best performance, achieving an out-of-sample accuracy of 82.8%, a Matthews correlation coefficient of 0.657, and an area under the ROC curve of 0.9. Based on the proposed model, we provided a method to stratify glioma patients according to their epigenomic state. View Full-Text
Keywords: immunosuppression; tumor microenviroment; neural network; genome-wide methylation model; glioma; extracellular matrix immunosuppression; tumor microenviroment; neural network; genome-wide methylation model; glioma; extracellular matrix
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MDPI and ACS Style

Polano, M.; Fabbiani, E.; Andreuzzi, E.; Cintio, F.D.; Bedon, L.; Gentilini, D.; Mongiat, M.; Ius, T.; Arcicasa, M.; Skrap, M.; Dal Bo, M.; Toffoli, G. A New Epigenetic Model to Stratify Glioma Patients According to Their Immunosuppressive State. Cells 2021, 10, 576. https://doi.org/10.3390/cells10030576

AMA Style

Polano M, Fabbiani E, Andreuzzi E, Cintio FD, Bedon L, Gentilini D, Mongiat M, Ius T, Arcicasa M, Skrap M, Dal Bo M, Toffoli G. A New Epigenetic Model to Stratify Glioma Patients According to Their Immunosuppressive State. Cells. 2021; 10(3):576. https://doi.org/10.3390/cells10030576

Chicago/Turabian Style

Polano, Maurizio, Emanuele Fabbiani, Eva Andreuzzi, Federica D. Cintio, Luca Bedon, Davide Gentilini, Maurizio Mongiat, Tamara Ius, Mauro Arcicasa, Miran Skrap, Michele Dal Bo, and Giuseppe Toffoli. 2021. "A New Epigenetic Model to Stratify Glioma Patients According to Their Immunosuppressive State" Cells 10, no. 3: 576. https://doi.org/10.3390/cells10030576

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