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Correlations among ERCC1, XPB, UBE2I, EGF, TAL2 and ILF3 revealed by gene signatures of histological subtypes of patients with epithelial ovarian cancer.

Oncology Reports 2012 January
The aim of this study was to better understand the mechanisms of tumor development and disease progression in human epithelial ovarian cancer. Fifty genes were screened for gene signature; 20 expressed genes were assessed in tumor and normal samples of EOC patients by RT-PCR. Expression of UBE2I, EGF, TAL2 and ILF3 was validated by qPCR on the ABI Prism 7000 Detection System. ERCC1 and XPB expression was previously determined by RT-PCR in these specimens. Statistical analyses include two-sided Kruskal-Wallis test, pairwise comparison, Pearson correlation coefficient and paired t-test. In comparison to normal samples, 6 genes demonstrated distinct expression patterns in tumor tissues, with high expression observed for ERCC1, XPB and ILF3 (p=0.001, 0.0007 and 0.002, respectively) and low expression observed for TAL2 and EGF (both p<0.0001). This differential expression pattern between normal and tumor tissues may reflect in part the development of ovarian cancer. Significant differences in expression patterns of these genes in clear cell, endometrioid, mucinous and serous ovarian cancer were observed. Comparison of expression of any two EOC subtypes revealed multiple gene involvement in histopathological differentiation and cancer progression. A positive association was found between ERCC1 and XPB expression (r=0.53, p<0.0001) and between TAL2 and EGF expression (r=0.817, p<0.0001) suggesting the existence of gene linkage in these tumors. The differences in expression patterns of studied genes between tumors and normal specimens, between histological subtypes and correlations among studied genes, may indicate their involvement in tumor growth and disease progression in human epithelial ovarian cancer. Further investigation of these genes may enable better understanding of the molecular mechanism of tumorigenesis and identification of potential biomarkers.

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