FOXC1 Is a Potential Prognostic Biomarker with Functional Significance in Basal-like Breast Cancer

Department of Surgical Oncology, John Wayne Cancer Institute, Santa Monica, California 90404, USA.
Cancer Research (Impact Factor: 9.33). 05/2010; 70(10):3870-6. DOI: 10.1158/0008-5472.CAN-09-4120
Source: PubMed


Gene expression signatures for a basal-like breast cancer (BLBC) subtype have been associated with poor clinical outcomes, but a molecular basis for this disease remains unclear. Here, we report overexpression of the transcription factor FOXC1 as a consistent feature of BLBC compared with other molecular subtypes of breast cancer. Elevated FOXC1 expression predicted poor overall survival in BLBC (P = 0.0001), independently of other clinicopathologic prognostic factors including lymph node status, along with a higher incidence of brain metastasis (P = 0.02) and a shorter brain metastasis-free survival in lymph node-negative patients (P < 0.0001). Ectopic overexpression of FOXC1 in breast cancer cells increased cell proliferation, migration, and invasion, whereas shRNA-mediated FOXC1 knockdown yielded opposite effects. Our findings identify FOXC1 as a theranostic biomarker that is specific for BLBC, offering not only a potential prognostic candidate but also a potential molecular therapeutic target in this breast cancer subtype.

Download full-text


Available from: Ying Qu, Mar 02, 2014
  • Source
    • "Humans with inherited haploinsufficiency of FOXC1 due to mutation or deletion exhibit the Axenfeld-Rieger syndrome, characterized by similar features to the murine knockout (Nishimura et al., 1998; Kume et al., 1998). High FOXC1 expression is associated with poor prognosis in breast and liver cancer (Ray et al., 2010; Xia et al., 2013), and its forced expression promotes an epithelialto-mesenchymal transition and enhanced proliferation, migration , invasion, and drug resistance, through downstream mediators such as NF-kB and NEDD9 (Bloushtain-Qimron et al., 2008; Wang et al., 2012; Xia et al., 2013). Interestingly, Foxc1 is highly expressed by Cxcl12-expressing adipo-osteogenic progenitors in mouse bone marrow (BM), and its deletion ablates hematopoietic stem cell (HSC) niches leading to reduced BM cellularity (Omatsu et al., 2014). "

    Cancer Cell 09/2015; · 23.52 Impact Factor
  • Source
    • "In HeLa cells, transcription factors JunB33 and FOXC134 were restricted in the perinuclear fraction; however, in MDA-MB-435 cells, these proteins were abundant in the nuclear fraction as well (Figures 2D and 3C). Nuclear FOXC1 in MDA-MB-435 also had a slow migratory component, but the perinuclear protein shows only one fast migratory band. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Cells have been described under the microscope as organelles containing cytoplasm and the nucleus. However, an unnoted structure exists between the cytoplasm and the nucleoplasm of eukaryotic cells. In addition to the nuclear envelope, there exists a perinuclear region (PNR or perinucleus) with unknown composition and function. Until now, an investigation of the role of the perinucleus has been restricted by the absence of a PNR isolation method. This manuscript describes a perinucleus isolation technique on the basis of its unique compact organization. The perinucleus was found to contain approximately 15 to 18% of the total proteins of the mammalian cell, almost half of the proteins of nuclei. Using four different normal and cancer cell lines, it was shown that the composition of PNR is highly dynamic. Application of the method showed that translocation of the p53 tumor-suppressor protein to the perinucleus in immortalized MEF cells is correlated with the translocation of p53-stabilizing protein, nucleophosmin (B23), to the PNR. Herein, the concept of the perinuclear region is advanced as a formal, identifiable structure. The roles of the perinucleus in maintaining genome integrity, regulation of gene expression and understanding of malignant transformation are discussed.
    Scientific Reports 07/2014; 4:4923. DOI:10.1038/srep04923 · 5.58 Impact Factor
  • Source
    • "Cluster 4 (blue-green) contained some cell cycle genes (CDC16, TFDP1). Cluster 5 (light-blue) contained FOXC1, a gene with regulatory and prognostic relevance in triple-negative breast cancer [41]. Cluster 6 (green) contained many genes that are related to ATPase activity (ATP5J, DHX15, CCT8, PSMC6 and ATP5O). "
    [Show abstract] [Hide abstract]
    ABSTRACT: Analysis of genome-wide data is often carried out using standard methods such as differential expression analysis, clustering analysis and heatmaps. Beyond that, differential correlation analysis was suggested to identify changes in the correlation patterns between disease states. The detection of differential correlation is a demanding task, as the number of entries in the gene-by-gene correlation matrix is large. Currently, there is no gold standard for the detection of differential correlation and statistical validation. We developed two untargeted algorithms (DCloc and DCglob) that identify differential correlation patterns by comparing the local or global topology of correlation networks. Construction of networks from correlation structures requires fixing of a correlation threshold. Instead of a single cutoff, the algorithms systematically investigate a series of correlation thresholds and permit to detect different kinds of correlation changes at the same level of significance: strong changes of a few genes and moderate changes of many genes. Comparing the correlation structure of 208 ER- breast carcinomas and 208 ER+ breast carcinomas, DCloc detected 770 differentially correlated genes with a FDR of 12.8%, while DCglob detected 630 differentially correlated genes with a FDR of 12.1%. In two-fold cross-validation, the reproducibility of the list of the top 5% differentially correlated genes in 140 ERtumors and in 140 ER+ tumors was 49% for DCloc and 33% for DCglob. We developed two correlation network topology based algorithms for the detection of differential correlations in different disease states. Clusters of differentially correlated genes could be interpreted biologically and included the marker genes hydroxyprostaglandin dehydrogenase (PGDH) and acyl-CoA synthetase medium chain 1 (ACSM1) of invasive apocrine carcinomas that were differentially correlated, but not differentially expressed. Using random subsampling and cross-validation, DCloc and DCglob were shown to identify specific and reproducible lists of differentially correlated genes.
    BMC Systems Biology 08/2013; 7(1):78. DOI:10.1186/1752-0509-7-78 · 2.44 Impact Factor
Show more