Weighted Gene Co-Expression Network Analysis and Support Vector Machine Learning in the Proteomic Profiling of Cerebrospinal Fluid from Extraventricular Drainage in Child Medulloblastoma

Maurizio Bruschi; Xhuliana Kajana; Andrea Petretto; Martina Bartolucci; Marco Pavanello; Gian Marco Ghiggeri; Isabella Panfoli; Giovanni Candiano

doi:10.3390/metabo12080724

Weighted Gene Co-Expression Network Analysis and Support Vector Machine Learning in the Proteomic Profiling of Cerebrospinal Fluid from Extraventricular Drainage in Child Medulloblastoma

Metabolites. 2022 Aug 5;12(8):724. doi: 10.3390/metabo12080724.

Authors

Maurizio Bruschi¹, Xhuliana Kajana¹, Andrea Petretto², Martina Bartolucci², Marco Pavanello³, Gian Marco Ghiggeri⁴, Isabella Panfoli⁵, Giovanni Candiano¹

Affiliations

¹ Laboratory of Molecular Nephrology, IRCCS Istituto Giannina Gaslini, 16147 Genoa, Italy.
² Core Facilities-Clinical Proteomics and Metabolomics, IRCCS Istituto Giannina Gaslini, 16147 Genoa, Italy.
³ Department of Neurosurgery, IRCCS Istituto Giannina Gaslini, 16147 Genoa, Italy.
⁴ Division of Nephrology, Dialysis, Transplantation, IRCCS Istituto Giannina Gaslini, 16147 Genoa, Italy.
⁵ Dipartimento di Farmacia (DIFAR), Università di Genova, 16147 Genoa, Italy.

Abstract

Medulloblastoma (MB) is the most common pediatric malignant central nervous system tumor. Overall survival in MB depends on treatment tuning. There is aneed for biomarkers of residual disease and recurrence. We analyzed the proteome of waste cerebrospinal fluid (CSF) from extraventricular drainage (EVD) from six children bearing various subtypes of MB and six controls needing EVD insertion for unrelated causes. Samples included total CSF, microvesicles, exosomes, and proteins captured by combinatorial peptide ligand library (CPLL). Liquid chromatography-coupled tandem mass spectrometry proteomics identified 3560 proteins in CSF from control and MB patients, 2412 (67.7%) of which were overlapping, and 346 (9.7%) and 805 (22.6%) were exclusive. Multidimensional scaling analysis discriminated samples. The weighted gene co-expression network analysis (WGCNA) identified those modules functionally associated with the samples. A ranked core of 192 proteins allowed distinguishing between control and MB samples. Machine learning highlighted long-chain fatty acid transport protein 4 (SLC27A4) and laminin B-type (LMNB1) as proteins that maximized the discrimination between control and MB samples. Machine learning WGCNA and support vector machine learning were able to distinguish between MB versus non-tumor/hemorrhagic controls. The two potential protein biomarkers for the discrimination between control and MB may guide therapy and predict recurrences, improving the MB patients' quality of life.

Keywords: artificial intelligence; brain tumor; cerebral spinal fluid; extraventricular drainage; mass spectrometry; medulloblastoma; proteomics.

Grants and funding

This research was funded by the Italian Ministry of Health–“Cinque per mille to Maurizio Bruschi”, and Ricerca Corrente to IRCCS Istituto Giannina Gaslini.