Anti-DOK5 monoclonal antibody, clone FQS0034(C)

Epigenomic and genomic changes affect gene expression and contribute to tumor development. The histone modifications trimethylated histone H3 lysine 4 (H3K4me3) and lysine 27 (H3K27me3) are epigenetic regulators associated to active and silenced genes, respectively and alterations of these modifications have been observed in cancer. Furthermore, genomic aberrations such as DNA copy number changes are common events in tumors. Pheochromocytoma is a rare endocrine tumor of the adrenal gland that mostly occurs sporadic with unknown epigenetic/genetic cause. The majority of cases are benign. Here we aimed to combine the genome-wide profiling of H3K4me3 and H3K27me3, obtained by the ChIP-chip methodology, and DNA copy number data with global gene expression examination in a malignant pheochromocytoma sample. The integrated analysis of the tumor expression levels, in relation to normal adrenal medulla, indicated that either histone modifications or chromosomal alterations, or both, have great impact on the expression of a substantial fraction of the genes in the investigated sample. Candidate tumor suppressor genes identified with decreased expression, a H3K27me3 mark and/or in regions of deletion were for instance TGIF1, DSC3, TNFRSF10B, RASSF2, HOXA9, PTPRE and CDH11. More genes were found with increased expression, a H3K4me3 mark, and/or in regions of gain. Potential oncogenes detected among those were GNAS, INSM1, DOK5, ETV1, RET, NTRK1, IGF2, and the H3K27 trimethylase gene EZH2. Our approach to associate histone methylations and DNA copy number changes to gene expression revealed apparent impact on global gene transcription, and enabled the identification of candidate tumor genes for further exploration.

Breast cancer metastasis is a demanding problem in clinical treatment of patients with breast cancer. It is necessary to examine the mechanisms of metastasis for developing therapies. Classification of the aggressiveness of breast cancer is an important issue in biological study and for clinical decisions. Although aggressive and non-aggressive breast cancer cells can be easily distinguished among different cell lines, it is very difficult to distinguish in clinical practice. The aim of the current study was to use the gene expression analysis from breast cancer cell lines to predict clinical outcomes of patients with breast cancer. Weighted gene co-expression network analysis (WGCNA) is a powerful method to account for correlations between genes and extract co-expressed modules of genes from large expression datasets. Therefore, WGCNA was applied to explore the differences in sub-networks between aggressive and non-aggressive breast cancer cell lines. The greatest difference topological overlap networks in both groups include potential information to understand the mechanisms of aggressiveness. The results show that the blue and red modules were significantly associated with the biological processes of aggressiveness. The sub-network, which consisted of TMEM47, GJC1, ANXA3, TWIST1 and C19orf33 in the blue module, was associated with an aggressive phenotype. The sub-network of LOC100653217, CXCL12, SULF1, DOK5 and DKK3 in the red module was associated with a non-aggressive phenotype. In order to validate the hazard ratio of these genes, the prognostic index was constructed to integrate them and examined using data from the Cancer Genomic Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Patients with breast cancer from TCGA in the high-risk group had a significantly shorter overall survival time compared with patients in the low-risk group (hazard ratio=1.231, 95% confidence interval=1.058-1.433, P=0.0071, by the Wald test). A similar result was produced from the GEO database. The findings may provide a novel strategy for measuring cancer aggressiveness in patients with breast cancer.