Literature DB >> 22418857

Prognostic influence of BCL2 expression in breast cancer.

Ki-Tae Hwang1, Jung Woo Woo, Hee Chul Shin, Han Suk Kim, Soo Kyung Ahn, Hyeong-Gon Moon, Wonshik Han, In Ae Park, Dong-Young Noh.   

Abstract

Although BCL2 has occasionally been suggested as a candidate prognostic factor for breast cancer, it is still not accepted as a prognostic factor. We attempted to validate the role of BCL2 as a prognostic factor of breast cancer. Data on 7,230 primary breast cancer patients from the Seoul National University Hospital Breast Care Center were analyzed. Three current prognostic models, including the St. Gallen model, the Nottingham prognostic index (NPI) model and the TNM model, were used for analysis of the prognostic influence of BCL2. The positive BCL2 group showed more favorable features with regard to clinicopathologic parameters than the BCL2 negative group and a strong correlation was observed between BCL2 and the hormonal receptor. The positive BCL2 group showed better prognosis in overall survival and disease free survival (log-rank test, both p < 0.001), even in all subgroups, than the BCL2 negative group. BCL2 was a significant prognostic factor in both univariate (hazard ratio [HR], 0.361; 95% confidence interval (CI), 0.306-0.426; p < 0.001) and multivariate analyses (HR, 0.417; 95% CI, 0.417-0.705; p < 0.001). BCL2 had a strong influence on the established prognostic models, including the St. Gallen model, the NPI model and the TNM model. BCL2 was a powerful independent prognostic factor for breast cancer and had a strong influence on the current prognostic models. Favorable clinicopathologic features and a strong correlation with the hormonal receptor are suggested as the causes of superior survival in patients with BCL2 positive breast cancer.
Copyright © 2012 UICC.

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Year:  2012        PMID: 22418857     DOI: 10.1002/ijc.27539

Source DB:  PubMed          Journal:  Int J Cancer        ISSN: 0020-7136            Impact factor:   7.396


  26 in total

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8.  Prognostic influences of BCL1 and BCL2 expression on disease-free survival in breast cancer.

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10.  Identification of breast cancer patients based on human signaling network motifs.

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