METHOD: By using the keywords "acute lymphoblastic leukemia", and "microarray", a total of 280 and 275 microarray datasets were found listed in Gene Expression Omnibus database GEO and ArrayExpress database respectively. Further manual inspection found that only three studies (GSE18497, GSE28460, GSE3910) were focused on gene expression profiling of paired diagnosis-relapsed pediatric B-ALL. These three datasets which comprised of a total of 108 matched diagnosis-relapsed pediatric B-ALL samples were then included for this meta-analysis using RankProd approach.
RESULTS: Our analysis identified a total of 1795 upregulated probes which corresponded to 1527 genes (pfp 1), and 1493 downregulated probes which corresponded to 1214 genes (pfp
METHODS: We analysed expression of NFIA and NFIB in mRNA expression data of high-grade astrocytoma and with immunofluorescence co-staining. Furthermore, we induced NFI expression in patient-derived subcutaneous glioblastoma xenografts via in vivo electroporation.
RESULTS: The expression of NFIA and NFIB is reduced in glioblastoma as compared to lower grade astrocytomas. At a cellular level, their expression is associated with differentiated and mature astrocyte-like tumour cells. In vivo analyses consistently demonstrate that expression of either NFIA or NFIB is sufficient to promote tumour cell differentiation in glioblastoma xenografts.
CONCLUSION: Our findings indicate that both NFIA and NFIB may have an endogenous pro-differentiative function in astrocytomas, similar to their role in normal astrocyte differentiation. Overall, our study establishes a basis for further investigation of targeting NFI-mediated differentiation as a potential differentiation therapy.
MATERIALS AND METHOD: 180 SNPs, shown to be previously associated with prostate cancer, were used to develop a PHS model in men with European ancestry. A machine-learning approach, LASSO-regularized Cox regression, was used to select SNPs and to estimate their coefficients in the training set (75,596 men). Performance of the resulting model was evaluated in the testing/validation set (6,411 men) with two metrics: (1) hazard ratios (HRs) and (2) positive predictive value (PPV) of prostate-specific antigen (PSA) testing. HRs were estimated between individuals with PHS in the top 5% to those in the middle 40% (HR95/50), top 20% to bottom 20% (HR80/20), and bottom 20% to middle 40% (HR20/50). PPV was calculated for the top 20% (PPV80) and top 5% (PPV95) of PHS as the fraction of individuals with elevated PSA that were diagnosed with clinically significant prostate cancer on biopsy.
RESULTS: 166 SNPs had non-zero coefficients in the Cox model (PHS166). All HR metrics showed significant improvements for PHS166 compared to PHS46: HR95/50 increased from 3.72 to 5.09, HR80/20 increased from 6.12 to 9.45, and HR20/50 decreased from 0.41 to 0.34. By contrast, no significant differences were observed in PPV of PSA testing for clinically significant prostate cancer.
CONCLUSIONS: Incorporating 120 additional SNPs (PHS166 vs PHS46) significantly improved HRs for prostate cancer, while PPV of PSA testing remained the same.