Reduced metastasis-suppressor gene mRNA-expression in breast cancer brain metastases
ABSTRACT Brain metastases are an increasingly common complication in breast cancer patients. The Metastasis Suppressor Genes (MSG) Nm23, KISS1, KAI1, BRMS1, and Mkk4 have been associated with the metastatic potential of breast cancer in vitro and in vivo.
The mRNA expression of Nm23, KISS1, KAI1, BRMS1, and Mkk4 in fresh frozen tissue samples of brain metastases from ductal invasive breast cancer specimens was examined in relation to primary tumors. In a first step, mRNA expression screening was carried out using a semi-quantitative RT-PCR approach, in a second step quantitative real-time RT-PCR was performed on selected specimens. By immunohistochemical staining, gene products were visualized on the protein level.
Semi-quantitative RT-PCR revealed reduced mRNA expression of Nm23, KISS1, KAI1, BRMS, and Mkk4 in brain metastases. Results for KISS1, KAI1, BRMS, and Mkk4 were confirmed by real-time RT-PCR. In detail, mRNA expression reduction in breast cancer brain metastases was tenfold. Expression of MSG could be confirmed by immunohistochemical staining on protein level.
Our investigations revealed significantly reduced mRNA expression of metastases suppressor genes KISS1, KAI1, BRMS1, and Mkk4 in breast cancer brain metastasis. Particularly, in the case of KISS1 and Mkk4, an important role for future treatment of patients with breast cancer brain metastatic lesions can be assumed.
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ABSTRACT: Breast cancer metastasis suppressor 1 (BRMS1) is a metastasis suppressor gene in several solid tumors. However, the expression and function of BRMS1 in glioma have not been reported. In this study, we investigated whether BRMS1 play a role in glioma pathogenesis. Using the tissue microarray technology, we found that BRMS1 expression is significantly decreased in glioma compared with tumor adjacent normal brain tissue (P<0.01, χ2 test) and reduced BRMS1 staining is associated with WHO stages (P<0.05, χ2 test). We also found that BRMS1 was significantly downregulated in glioma cell lines compared to normal human astrocytes (P<0.01, χ2 test). Furthermore, we demonstrated that BRMS1 overexpression inhibited glioma cell invasion by suppressing uPA, NF-κB, MMP-2 expression and MMP-2 enzyme activity. Moreover, our data showed that overexpression of BRMS1 inhibited glioma cell migration and adhesion capacity compared with the control group through the Src-FAK pathway. Taken together, this study suggested that BRMS1 has a role in glioma development and progression by regulating invasion, migration and adhesion activities of cancer cells.PLoS ONE 05/2014; 9(5):e98544. DOI:10.1371/journal.pone.0098544 · 3.53 Impact Factor
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ABSTRACT: Background Our goal is to validate the Memorial Sloan-Kettering Cancer Center (MSKCC) nomogram and Stanford Online Calculator (SOC) for predicting non-sentinel lymph node (NSLN) metastasis in Chinese patients, and develop a new model for better prediction of NSLN metastasis. Methods The MSKCC nomogram and SOC were used to calculate the probability of NSLN metastasis in 120 breast cancer patients. Univariate and multivariate analyses were performed to evaluate the relationship between NSLN metastasis and clinicopathologic factors, using the medical records of the first 80 breast cancer patients. A new model predicting NSLN metastasis was developed from the 80 patients. Results The MSKCC and SOC predicted NSLN metastasis in a series of 120 patients with an area under the receiver operating characteristic curve (AUC) of 0.688 and 0.734, respectively. For predicted probability cut-off points of 10%, the false-negative (FN) rates of MSKCC and SOC were both 4.4%, and the negative predictive value (NPV) 75.0% and 90.0%, respectively. Tumor size, Kiss-1 expression in positive SLN and size of SLN metastasis were independently associated with NSLN metastasis (p<0.05). A new model (Peking University People's Hospital, PKUPH) was developed using these three variables. The MSKCC, SOC and PKUPH predicted NSLN metastasis in the second 40 patients from the 120 patients with an AUC of 0.624, 0.679 and 0.795, respectively. Conclusion MSKCC nomogram and SOC did not perform as well as their original researches in Chinese patients. As a new predictor, Kiss-1 expression in positive SLN correlated independently with NSLN metastasis strongly. PKUPH model achieved higher accuracy than MSKCC and SOC in predicting NSLN metastasis in Chinese patients.PLoS ONE 08/2014; 9(8):e104117. DOI:10.1371/journal.pone.0104117 · 3.53 Impact Factor
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ABSTRACT: Breast cancer has been reported to account for the maximum cases among all female cancers till date. In order to gain a deeper insight into the complexities of the disease, we analyze the breast cancer network and its normal counterpart at the proteomic level. While the short range correlations in the eigenvalues exhibiting universality provide an evidence of importance of random connections in the underlying networks, the long range correlations along with the localization properties reveal insightful structural patterns involving functionally important proteins. The analysis provides a benchmark for designing drugs which can target a subgraph instead of individual proteins.Scientific Reports 06/2014; 4. DOI:10.1038/srep06368 · 5.08 Impact Factor