Weighted Gene Co-Expression Network Analysis and Support Vector Machine Learning in the Proteomic Profiling of Cerebrospinal Fluid from Extraventricular Drainage in Child Medulloblastoma
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 (...
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| Format: | Article |
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MDPI AG
2022-08-01
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| Series: | Metabolites |
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| Online Access: | https://www.mdpi.com/2218-1989/12/8/724 |
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| author | Maurizio Bruschi Xhuliana Kajana Andrea Petretto Martina Bartolucci Marco Pavanello Gian Marco Ghiggeri Isabella Panfoli Giovanni Candiano |
| author_facet | Maurizio Bruschi Xhuliana Kajana Andrea Petretto Martina Bartolucci Marco Pavanello Gian Marco Ghiggeri Isabella Panfoli Giovanni Candiano |
| author_sort | Maurizio Bruschi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T04:06:52Z |
| format | Article |
| id | doaj.art-b45da63533b446d1b648fe8eea45125b |
| institution | Directory Open Access Journal |
| issn | 2218-1989 |
| language | English |
| last_indexed | 2024-03-09T04:06:52Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Metabolites |
| spelling | doaj.art-b45da63533b446d1b648fe8eea45125b2023-12-03T14:06:14ZengMDPI AGMetabolites2218-19892022-08-0112872410.3390/metabo12080724Weighted Gene Co-Expression Network Analysis and Support Vector Machine Learning in the Proteomic Profiling of Cerebrospinal Fluid from Extraventricular Drainage in Child MedulloblastomaMaurizio Bruschi0Xhuliana Kajana1Andrea Petretto2Martina Bartolucci3Marco Pavanello4Gian Marco Ghiggeri5Isabella Panfoli6Giovanni Candiano7Laboratory of Molecular Nephrology, IRCCS Istituto Giannina Gaslini, 16147 Genoa, ItalyLaboratory of Molecular Nephrology, IRCCS Istituto Giannina Gaslini, 16147 Genoa, ItalyCore Facilities–Clinical Proteomics and Metabolomics, IRCCS Istituto Giannina Gaslini, 16147 Genoa, ItalyCore Facilities–Clinical Proteomics and Metabolomics, IRCCS Istituto Giannina Gaslini, 16147 Genoa, ItalyDepartment of Neurosurgery, IRCCS Istituto Giannina Gaslini, 16147 Genoa, ItalyDivision of Nephrology, Dialysis, Transplantation, IRCCS Istituto Giannina Gaslini, 16147 Genoa, ItalyDipartimento di Farmacia (DIFAR), Università di Genova, 16147 Genoa, ItalyLaboratory of Molecular Nephrology, IRCCS Istituto Giannina Gaslini, 16147 Genoa, ItalyMedulloblastoma (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.https://www.mdpi.com/2218-1989/12/8/724medulloblastomabrain tumorartificial intelligencemass spectrometryextraventricular drainagecerebral spinal fluid |
| spellingShingle | Maurizio Bruschi Xhuliana Kajana Andrea Petretto Martina Bartolucci Marco Pavanello Gian Marco Ghiggeri Isabella Panfoli Giovanni Candiano 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 medulloblastoma brain tumor artificial intelligence mass spectrometry extraventricular drainage cerebral spinal fluid |
| title | Weighted Gene Co-Expression Network Analysis and Support Vector Machine Learning in the Proteomic Profiling of Cerebrospinal Fluid from Extraventricular Drainage in Child Medulloblastoma |
| title_full | Weighted Gene Co-Expression Network Analysis and Support Vector Machine Learning in the Proteomic Profiling of Cerebrospinal Fluid from Extraventricular Drainage in Child Medulloblastoma |
| title_fullStr | Weighted Gene Co-Expression Network Analysis and Support Vector Machine Learning in the Proteomic Profiling of Cerebrospinal Fluid from Extraventricular Drainage in Child Medulloblastoma |
| title_full_unstemmed | Weighted Gene Co-Expression Network Analysis and Support Vector Machine Learning in the Proteomic Profiling of Cerebrospinal Fluid from Extraventricular Drainage in Child Medulloblastoma |
| title_short | Weighted Gene Co-Expression Network Analysis and Support Vector Machine Learning in the Proteomic Profiling of Cerebrospinal Fluid from Extraventricular Drainage in Child Medulloblastoma |
| title_sort | weighted gene co expression network analysis and support vector machine learning in the proteomic profiling of cerebrospinal fluid from extraventricular drainage in child medulloblastoma |
| topic | medulloblastoma brain tumor artificial intelligence mass spectrometry extraventricular drainage cerebral spinal fluid |
| url | https://www.mdpi.com/2218-1989/12/8/724 |
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