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Alterations in eIF3-p48/INT6 gene expression have been implicated in murine and human mammary carcinogenesis. We examined levels of INT6 protein in human tumors and determined that breast and colon tumors clustered into distinct groups based on levels of INT6 expression and clinicopathological variables. We performed multiplex tissue immunoblotting of breast, colon, lung, and ovarian tumor tissues and found that INT6 protein levels positively correlated with those of TID1, Patched, p53, c-Jun, and phosphorylated-c-Jun proteins in a tissue-specific manner. INT6 and TID1 showed significant positive correlation in all tissue types tested. These findings were confirmed by immunohistochemical staining of INT6 and TID1. Further evidence supporting a cooperative role for INT6 and TID1 is the presence of endogenous INT6 and TID1 proteins as complexes. We detected co-immunoprecipitation between INT6 and TID1, as well as between INT6 and Patched. These findings suggest potential integrated roles for INT6, TID1, and Patched proteins in cell growth, development, and tumorigenesis. Additionally, these data suggest that the combination of INT6, TID1, and Patched protein levels may be useful biomarkers for the development of diagnostic assays. [ABSTRACT FROM AUTHOR]

Reliable quantitative evaluation of molecular pathways is critical for both drug discovery and treatment monitoring. We have modified the CAM assay to quantitatively measure vascular density, endothelial proliferation, and changes in protein expression in response to anti-angiogenic and pro-angiogenic agents. This improved CAM assay can correlate changes in vascular density with changes seen on a molecular level. We expect that these described modifications will result in a single in vivo assay system, which will improve the ability to investigate molecular mechanisms underlying the angiogenic response. [ABSTRACT FROM AUTHOR]

Measurement of gene-expression profiles using microarray technology is becoming increasingly popular among the biomedical research community. Although there has been great progress in this field, investigators are still confronted with a difficult question after completing their experiments: how to validate the large data sets that are generated? This review summarizes current approaches to verifying global expression results, discusses the caveats that must be considered, and describes some methods that are being developed to address outstanding problems. [ABSTRACT FROM AUTHOR]