ENTPD1 (CD39) and NT5E (CD73) expression in human medulloblastoma: an in silico analysis.
Marco Antonio StefaniElizandra BraganholGuilherme Tomasi SantosSamuel Masao SuwaDaiane Dias CabeleiraGuilherme Pamplona Bueno de AndradePublished in: Purinergic signalling (2024)
Medulloblastoma is the most common malignant tumor in the pediatric population. Its classification has incorporated key molecular variations alongside histological characterization. CD39 (also known as ENTPD1) and CD73 (also known as NT5E), enzymes of the purinergic signaling pathway, act in synergy to generate extracellular adenosine, creating an immunosuppressive tumor microenvironment. Our study examined the expression of mRNA of these genes in previously described transcriptome data sets of medulloblastoma patient samples from the Cavalli Cohort (n = 763). Survival distribution was estimated according to the Kaplan-Meier method using a median cut-off and log-rank statistics (p ≤ 0.05). In non-WNT and non-SHH medulloblastoma Group 4 (n = 264), the high expression of ENTPD1 and NT5E was significantly related to a lower overall survival (p = 2.7e-04; p = 2.6e-03). In the SHH-activated group (n = 172), the high expression of ENTPD1 was significantly related to lower overall survival (p = 7.8e-03), while the high expression of NT5E was significantly related to greater overall survival (p = 0.017). In the WNT group (n = 63), the expressions of ENTPD1 and NT5E were not significantly correlated with overall survival (p = 0.212; p = 0.101). In non-WNT and non-SHH medulloblastoma Group 3 (n = 113), the high expression of ENTPD1 was significantly related to greater survival (p = 0.034), while expression of NT5E was not significantly related to survival of patients (p = 0.124). This in silico analysis indicates that ENTPD1 (CD39) and NT5E (CD73) can be seen as potential prognostic markers and therapeutic targets for primary medulloblastomas in non-WNT and non-SHH Group 4.
Keyphrases
- poor prognosis
- signaling pathway
- stem cells
- binding protein
- free survival
- gene expression
- chronic kidney disease
- end stage renal disease
- newly diagnosed
- prognostic factors
- nk cells
- machine learning
- big data
- pi k akt
- single cell
- electronic health record
- climate change
- induced pluripotent stem cells
- protein kinase
- patient reported outcomes