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Transcription regulatory networks in medulloblastoma.
Pediatric central nervous system tumors are the second most frequent tumor after leukemia and the most common form of childhood solid tumor. They account for 22% and 10% of all malignancies among children aged up to 14 years and 15-19 years respectively. Medulloblastoma is the most common pediatric...
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AUC Knowledge Fountain
2015
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| Summary: | Pediatric central nervous system tumors are the second most frequent tumor after leukemia and the most common form of childhood solid tumor. They account for 22% and 10% of all malignancies among children aged up to 14 years and 15-19 years respectively. Medulloblastoma is the most common pediatric primary malignant intra-cranial neoplasm, accounting for 20-25% of all childhood brain tumors. Diagnosis and grading of medulloblastoma have mainly been dependent on histological assessment and clinical presentation. In recent years it has been becoming more evident that medulloblastoma tumors display heterogeneity in genetics and clinical response that cannot simply be explained by the histological classification. Recent analysis using microarray, and whole genome sequencing identified five core subgroups: WNT (WNT/wingless), SHH (Sonic hedgehog), Subgroup 3, Subgroup 4 and Subgroup 5 according to their molecular signatures. In the current study the gene expression signature of 76 medulloblastoma samples previously described by the St. Jude team was unitized to construct a protein-protein interaction network for differentially expressed genes of each molecular subgroup. Also, transcriptional regulatory network for each subgroup was demonstrated, providing novel insights into the complex transcriptional regulation of the genes involved in medulloblastoma. This work introduces transcriptional signatures for the medulloblastoma subgroups, which can be potentially utilized for diagnostic and therapeutic applications. The present study show that WIF1 (WNT inhibitory factor 1), LEF1 (lymphoid enhancer binding factor1) and FZD10 (frizzled receptors 10) are potential gene biomarkers for WNT subgroup. The WNT subgroup also demonstrated an up regulation in genes involved in MAPK (mitogen activating protein kinase) signaling pathway as well as WNT pathway. Transcription factors (TF)regulating the differential expression in this subgroup are SUZ12 (suppressor of zest 12) and NANOG. SHH pathway marker gene profiles belong to Gli3 (gli family zinc finger 3), PPARA (peroxisome proliferator-activated receptor alpha), LRRC7 (leucine rich repeat containing 7), BMP2/4 (Bone morphogenetic peptide 2, 4) and FOXG1 (forkhead box 1). With the involvement of this subtype gene behavior with the SHH signaling pathway, these expression profiles are suggested to be transcriptionally regulated by E2F1 and MYC. Subgroup 3 displayed a down regulation in FOXG1 with associated TTR1 (transthyretin) and DUSP (Dual specificity phosphatase). PRLR (prolactin receptor) and MET (met proto oncogene) up regulations supported the involvement of these patient profiles with retinopathy progression pathways as well as pathways. TFs regulating cancer progression in this subgroup included TBX1 (T box 1), CEBP( CCAAT enhancer binding protein) and CLK1 (CDC-like kinase), while the Apoptotic transcriptional regulation is through AVEN (apoptosis caspase activation inhibitor). Subgroup 4 exclusively showed TPN1 (Transport of pyridoxine protein 1) down regulation not observed in other profiles. Cytogenetic pathways involvement is witnessed in this subgroup with down regulation of cell cycle genes. TFs regulating cell cycle pathways are the E2F1 and SKP2 (S phase kinas associated protein 2) Subgroup 5 showed a FOXG1 down regulation with no differential expression of the MYC. TEX15 down regulation was unique to this subgroup with no clear mechanism. Involvement in cAMP biosynthesis is a trend in this subgroup. TFs suggested to regulate this behavior are ZNF281 and E2F1/ETS1 regulation. From this we conclude that, microarray gene expression profiling provides a comprehensive platform for the study of different gene behaviors in medulloblastoma. We also suggest that utilization of RNA-Seq high throughput technology can assist in detecting altered gene expressions as well as SNPs (single nucleotide polymorphisms) that might be an underlying cause for medulloblastoma progression. |
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