Anti-CD3D monoclonal antibody, clone DE4-I6 [FITC]

Hypoxia is a key determinant of tumor aggressiveness, yet little is known regarding hypoxic global gene regulation in vivo. We used the hypoxia marker EF5 coupled with laser-capture microdissection to isolate RNA from viable hypoxic and normoxic regions of 9L experimental gliomas. Through microarray analysis, we identified several mRNAs (including the HIF targets Vegf, Glut-1, and Hsp27) with increased levels under hypoxia compared with normoxia both in vitro and in vivo. However, we also found striking differences between the global in vitro and in vivo hypoxic mRNA profiles. Intriguingly, the mRNA levels of a substantial number of immunomodulatory and DNA repair proteins including CXCL9, CD3D, and RAD51 were found to be downregulated in hypoxic areas in vivo, consistent with a protumorigenic role of hypoxia in solid tumors. Immunohistochemical staining verified increased HSP27 and decreased RAD51 protein levels in hypoxic versus normoxic tumor regions. Moreover, CD8(+) T cells, which are recruited to tumors upon stimulation by CXCL9 and CXCL10, were largely excluded from viable hypoxic areas in vivo. This is the first study to analyze the influence of hypoxia on mRNA levels in vivo and can be readily adapted to obtain a comprehensive picture of hypoxic regulation of gene expression and its influence on biological functions in solid tumors. Cancer Res; 71(3); 779-89. (C)2011 AACR.

Purpose: To construct a cluster model or a gene signature for Stevens-Johnson syndrome (SJS) using pathways analysis in order to identify some potential biomarkers that may be used for early detection of SJS and epidermal necrolysis (TEN) manifestations. Methods: Gene expression profiles of GSE12829 were downloaded from Gene Expression Omnibus database. A total of 193 differentially expressed genes (DEGs) were obtained. We applied these genes to geneMANIA database, to remove ambiguous and duplicated genes, and after that, characterized the gene expression profiles using geneMANIA, DAVID, REACTOME, STRING and GENECODIS which are online software and databases. Results: Out of 193 genes, only 91 were used (after removing the ambiguous and duplicated genes) for topological analysis. It was found by geneMANIA database search that majority of these genes were co-expressed yielding 84.63 % co-expression. It was found that ten genes were in Physical interactions comprising almost 14.33 %. There were <1 % pathway and genetic interactions with values of 0.97 and 0.06 %, respectively. Final analyses revealed that there are two clusters of gene interactions and 13 genes were shown to be in evident relationship of interaction with regards to hypersensitivity. Conclusion: Analysis of differential gene expressions by topological and database approaches in the current study reveals 2 gene network clusters. These genes are CD3G, CD3E, CD3D, TK1, TOP2A, CDK1, CDKN3, CCNB1, and CCNF. There are 9 key protein interactions in hypersensitivity reactions and may serve as biomarkers for SJS and TEN. Pathways related gene clusters has been identified and a genetic model to predict SJS and TEN early incidence using these biomarker genes has been developed.