Abstract 1120
Background
Medulloblastoma is the most common malignant brain tumor in children accounting for about 10% of all pediatric cancer deaths. According to the 2016 WHO Classification, four molecular subtypes of medulloblastoma including WNT, SHH, Group 3 and Group 4 were characterized by high-throughput gene expression profiling. These molecular subtypes display distinct clinical, demographic and genetic features that are associated with prognostic and therapeutic differences. Because it is currently impractical to perform microarray analysis in clinical settings, distinguishing the four molecular subgroups of medulloblastoma in the daily treatment of patients, as well in the setting of clinical trials, remains an important challenge.
Methods
Three medulloblastoma microarray datasets were curated to perform an integrative analysis. To identify a reliable biomarker, a training-validating approach was adopted in this study. The gene expression profiles of 103 samples were selected as a training set for signature identification. Additional 358 samples were used for signature validation. The Gene Ontology and KEGG pathway analysis were performed to reveal the biological features of candidate genes.
Results
A 23-gene expression signature derived from the training set was strongly associated with the molecular subtypes of medulloblastoma. The 23-gene expression signature was validated in 8 WNT, 61 SHH, 62 Group 3 and 227 Group 4 samples. With the 23-gene expression signature, 8 samples were classified as WNT, 63 as SHH, 73 as Group 3 and 214 as Group 4. The gene expression-based assignments reached a 95.5% overall agreement with the reference diagnoses (342 of 358; 95% CI: 0.928 to 0.974). Sensitivity ranged from 94% to 100%, while specificity ranged from 96% to 100%. The functional enrichment analysis showed that 23 genes were significantly associated with signal transduction and WNT signaling pathway.
Conclusions
A 23-gene expression signature that could accurately discriminate molecular subtypes of medulloblastoma was identified in this study. Our results may prompt further development of this gene expression signature into a molecular assay amenable to routine clinical practice.
Clinical trial identification
Legal entity responsible for the study
Qinghua Xu.
Funding
Has not received any funding.
Editorial Acknowledgement
Disclosure
J. Chen, C. Chen, Y. Sun, L. Chen: Employment: Canhelp Genomics Co. Ltd. Q. Xu: Employment, stock ownership: Canhelp Genomics Co. Ltd. All other authors have declared no conflicts of interest.
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