Many studies have investigated the association between the allergic conditions and the risk of glioma. However, the evidence is inadequate to draw robust conclusions because most studies were generally small and conducted in heterogeneous populations. To shed light on these inconclusive findings, we conducted a meta-analysis of studies relating the allergic conditions to the risk of glioma. We identified the relevant studies by searching ISI Web of Science, PubMed, EMBASE, Chinese National Knowledge Infrastructure (CNKI) databases, and Wanfang database by October 2013. We included studies that reported odds ratio (OR) or hazard ratio (HR) with its 95% confidence interval (CI) for the association between the allergic condition and the risk of glioma. Eighteen independent publications, with 9,986 glioma cases and 118,950 controls, were included. Our results showed that allergic condition was reversely associated with the risk of glioma (OR = 0.78, 95% CI 0.73-0.83, P < 0.001). The results of our meta-analysis indicated that allergic conditions significantly reduce the risk of glioma.

To identify novel genes associated with pediatric pilocytic astrocytoma (PA) for better understanding the molecular mechanism underlying the pediatric PA pathogenesis. Gene expression profile data of GSE50161 and GSE44971 and the methylation data of GSE44684 were downloaded from Gene Expression Omnibus. The differentially expressed genes (DEGs) between PA and normal control samples were screened using the limma package in R, and then used to construct weighted gene coexpression network (WGCN) using the WGCN analysis (WGCNA) package in R. Significant modules of DEGs were selected using the clustering analysis. Function enrichment analysis of the DEGs in significant modules were performed using the WGCNA package and clusterprofiler package in R. Correlation between methylation sites of DEGs and PA was analyzed using the CpGassoc package in R. Totally, 3479 DEGs were screened in PA samples. Thereinto, 3424 DEGs were used to construct the WGCN. Several significant modules of DEGs were selected based on the WGCN, in which the turquoise module was positively related to PA, whereas blue module was negatively related to PA. DEGs (for example, DOCK2 (dedicator of cytokinesis 2), DOCK8 and FCGR2A (Fc fragment of IgG, low affinity IIa)) in blue module were mainly involved in Fc gamma R-mediated phagocytosis pathway and natural killer cell-mediated cytotoxicity pathway. Methylations of 14 DEGs among the top 30 genes in blue module were related to PA. Our data suggest that DOCK2, DOCK8 and FCGR2A may represent potential therapeutic targets in PA that merits further investigation.

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Our study provided evidence that NB cell may enhance bone invasion through PTHrP, OPG, RANKL, and ET-1, especially PTHrP and RANKL which may display stronger effects. CXCR4 appeared not participating in bone invasion, but in tumor growth, and homing to bone. Targeting PTHrP, OPG, ET-1, and RANKL may provide a new insight and method for patient therapy by inhibiting NB bone metastasis and invasiveness.

Background. Type 2 diabetes is a major health concern worldwide. The present study is aimed at discovering effective biomarkers for an efficient diagnosis of type 2 diabetes. Methods. Differentially expressed genes (DEGs) between type 2 diabetes patients and normal controls were identified by analyses of integrated microarray data obtained from the Gene Expression Omnibus database using the Limma package. Functional analysis of genes was performed using the R software package clusterProfiler. Analyses of protein-protein interaction (PPI) performed using Cytoscape with the CytoHubba plugin were used to determine the most sensitive diagnostic gene biomarkers for type 2 diabetes in our study. The support vector machine (SVM) classification model was used to validate the gene biomarkers used for the diagnosis of type 2 diabetes. Results. GSE164416 dataset analysis revealed 499 genes that were differentially expressed between type 2 diabetes patients and normal controls, and these DEGs were found to be enriched in the regulation of the immune effector pathway, type 1 diabetes mellitus, and fatty acid degradation. PPI analysis data showed that five MCODE clusters could be considered as clinically significant modules and that 10 genes (IL1B, ITGB2, ITGAX, COL1A1, CSF1, CXCL12, SPP1, FN1, C3, and MMP2) were identified as “real” hub genes in the PPI network using algorithms such as Degree, MNC, and Closeness. The sensitivity and specificity of the SVM model for identifying patients with type 2 diabetes were 100%, with an area under the curve of 1 in the training as well as the validation dataset. Conclusion. Our results indicate that the SVM-based model developed by us can facilitate accurate diagnosis of type 2 diabetes.

Background: Glutathione S-Transferase P 1 (GSTP-1) gene plays an important physiological role in the body. The present study was conducted to identify the clinical implication of GSTP-1 gene polymorphism on the prognosis of patients with high-grade glioma (HGG) who received temozolomide plus radiotherapy adjuvant treatment. Methods: This study recruited a total of 186 patients with HGG who were treated with temozolomide plus radiotherapy adjuvant regimen (retrospectively). Baseline clinical characteristics were obtained and the prognostic data of the patients were collected. Peripheral blood specimen of patients was preserved for genotyping of GSTP-1 polymorphism during hospitalization. Correlation analysis was carried out accordingly. Additionally, fresh peripheral blood specimens that were available for mRNA expression analysis were collected for the mRNA expression analysis. Results:The median progression-free survival (PFS) and overall survival (OS) of the 186 patients with HGG who received temozolomide plus radiotherapy regimen was 8.5 months (95% CI: 5.95-11.05) and 15.5 months (95% CI: 11.49-19.51), respectively. The prevalence of 313A>G among 186 patients with glioma was AA genotype: 126 cases (67.7%), AG genotype: 54 cases (29.1%), GG genotype: 6 cases (3.2%), minor allele frequency of 313A>G was 0.18. Association analysis suggested that the median PFS of patients with AA and AG/GG genotypes was 11.2 and 5.0 months, respectively (χ 2 =11.17, P=0.001). Furthermore, the median OS of patients with AA and AG/GG genotypes was 18.9 and 10.5 months, respectively (χ 2 =12.684, P<0.001). Besides, when adjusted for PFS in multivariate Cox regression analysis, AG/GG genotype was an independent factor for PFS (HR=0.48, P=0.006). The mRNA expression results indicated that mRNA expression of GSTP-1 in patients with AG/GG genotypes of 313A>G was significantly higher than that of patients with AA genotype (P<0.001). Conclusion: GSTP-1 polymorphism 313A>G might be used as a potential biomarker to predict the prognosis of patients with HGG who received temozolomide plus radiotherapy adjuvant treatment.

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