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. 2008 Sep 15;123(6):1327-38.
doi: 10.1002/ijc.23660.

Identification of new genes associated with breast cancer progression by gene expression analysis of predefined sets of neoplastic tissues

Daniela Cimino  1 , Luca Fuso, Christian Sfiligoi, Nicoletta Biglia, Riccardo Ponzone, Furio Maggiorotto, Giandomenico Russo, Luigi Cicatiello, Alessandro Weisz, Daniela Taverna, Piero Sismondi, Michele De Bortoli
Affiliations

Identification of new genes associated with breast cancer progression by gene expression analysis of predefined sets of neoplastic tissues

Daniela Cimino et al. Int J Cancer. .

Abstract

Gene expression profiles were studied by microarray analysis in 2 sets of archival breast cancer tissues from patients with distinct clinical outcome. Seventy-seven differentially expressed genes were identified when comparing 30 cases with relapse and 30 cases without relapse within 72 months from surgery. These genes had a specific ontological distribution and some of them have been linked to breast cancer in previous studies: AIB1, the two keratin genes KRT5 and KRT15, RAF1, WIF1 and MSH6. Seven out of 77 differentially expressed genes were selected and analyzed by qRT-PCR in 127 cases of breast cancer. The expression levels of 6 upregulated genes (CKMT1B, DDX21, PRKDC, PTPN1, SLPI, YWHAE) showed a significant association to both disease-free and overall survival. Multivariate analysis using the significant factors (i.e., estrogen receptor and lymph node status) as covariates confirmed the association with survival. There was no correlation between the expression level of these genes and other clinical parameters. In contrast, SERPINA3, the only downregulated gene examined, was not associated with survival, but correlated with steroid receptor status. An indirect validation of our genes was provided by calculating their association with survival in 3 publicly available microarray datasets. CKMT1B expression was an independent prognostic marker in all 3 datasets, whereas other genes confirmed their association with disease-free survival in at least 1 dataset. This work provides a novel set of genes that could be used as independent prognostic markers and potential drug targets for breast cancer.

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