Angiogenesis and expression of PDGF-C, VEGF, CD105 and HIF-1α in human glioblastoma.

Glioblastoma (GBM), the most frequent and aggressive brain tumor, is characterized by marked angiogenesis directly related to invasiveness and poor prognosis. Hypoxia is considered to be an important stimulus for angiogenesis by inducing hypoxia-inducible factor 1-alpha (HIF-1α) overexpression that activates platelet-derived growth factor (PDGF) and VEGF. The aim of this study is to analyze the expression of PDGF-C, VEGF in endothelial and tumor cells of GBM and their relation to HIF-1α expression. Two hundred and eight GBM cases were studied by tissue microarray immunohistochemical preparation. Expression of HIF-1α, VEGF and PDGF-C was observed in 184 (88.5%), 131 (63%) and 160 (76.9%) tumor cases, respectively. The numbers of vessels were quantified by CD34, PDGF-C, VEGF and CD105 staining, and were in median 20, 16, 5 and 6, respectively. The GBMs that showed positive or negative expression for HIF-1α showed a median vascular density of 30 and 14, respectively, for CD34 (P < 0.015). Positive expression for HIF-1α was correlated with VEGF and PDGF-C expression in tumors (P < 0.001). There was a significant correlation between VEGF and PDGF-C expression in the cytoplasm of GBM tumor cells (P < 0.0001). We showed that VEGF expression in tumor cells was correlated with its expression in blood vessels (P < 0.0001). Endothelial cells with PDGF-C and VEGF positive expression were also positive for CD105 and their nuclei for Ki-67, confirming the neoangiogenic and proliferative influence of VEGF and PDGF-C. VEGF nuclear staining in tumor cells (P = 0.002) as well as nuclear staining for HIF-1α and VEGF (P = 0.005) correlated with survival. In summary, our present findings of the concomitant upregulation of PDGF-C with VEGF in GBM tumor cells and vessels further reinforce the benefit of using combined anti-angiogenic approaches to potentially improve the therapeutic response for GBM.

Cluster analysis of an extensive human breast cancer cell line protein expression map database.

In the current study, the protein expression maps (PEMs) of 26 breast cancer cell lines and three cell lines derived from normal breast or benign disease tissue were visualised by high resolution two-dimensional gel electrophoresis. Analysis of this data was performed with ChiClust and ChiMap, two analytical bioinformatics tools that are described here. These tools are designed to facilitate recognition of specific patterns shared by two or more (a series) PEMs. Both tools use PEMs that were matched by an image analysis program and locally written programs to create a match table that is saved in an object relational database. The ChiClust tool uses clustering and subclustering methods to extract statistically significant protein expression patterns from a large series of PEMs. The ChiMap tool calculates a differential value (either as percentage change or a fold change) and represents these graphically. All such differentials or just those identified using ChiClust can be submitted to ChiMap. These methods are not dependent on any particular commercial image analysis program, and the whole software package gives an integrated procedure for the comparison and analysis of a series of PEMs. The ChiClust tool was used here to order the breast cell lines into groups according to biological characteristics including morphology in vitro and tumour forming ability in vivo. ChiMap was then used to highlight eight major protein feature-changes detected between breast cancer cell lines that either do or do not proliferate in nude mice. Mass spectrometry was used to identify the proteins. The possible role of these proteins in cancer is discussed.