Artificial Intelligence in proteomic profiling of Cerebrospinal fluid from extraventricular drainage in child Medulloblastoma

ABSTRACT: Medulloblastoma (MB) is the most common pediatric malignant central nervous system tumor. Overall survival in MB depends on treatment tuning. There is the need for biomarkers of residual disease, and recurrence. We analysed the proteome of waste cerebrospinal fluid (CSF) from extraventricular drainage (EVD) from 6 children bearing various subtypes of MB and 6 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%) 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 maximize the discrimination between control and MB samples, respectively. Artificial intelligence was able to distinguish between MB vs 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.

INSTRUMENT(S): Orbitrap Fusion

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Cerebrospinal Fluid

SUBMITTER: Martina Bartolucci  

LAB HEAD: Andrea Petretto

PROVIDER: PXD035292 | Pride | 2022-10-14

REPOSITORIES: Pride