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腋淋巴阴性乳腺癌分子标记与预后相关性研究
作者姓名:Zhang S  Yuan Y  Wang X
作者单位:浙江医科大学第二医院肿瘤科
摘    要:为探讨c-erbB-2、p53、雌激素受体(ER)、孕激素受体(PR)和DNA合成时相细胞百分率(SPF)对腋淋巴阴性乳腺癌(ANN)患者的预后提示价值,作者选择180例ANN患者,以9项可能影响预后的临床病理指标作为病例配对条件,按1:2配对得到27对(81例)ANN乳腺癌患者,分别测定其肿瘤c-erbB-2、p53、ER、PR蛋白表达和SPF,结合随访结果进行logrank检验及COX回归数学模型进行危险度分析。结果显示,在5项生物特性指标中,SPF对ANN乳腺癌患者预后的提示作用最强。ANN患者的5年无瘤生存率,SPF<10%者为92%,>10%者为52%(P<0.001)。SPF单独指标的复发危险度可达11.31。其余4项指标的复发危险度按次序分别为PR、ER、p53、c-erbB-2。作者认为多指标综合分析,PR和ER分别与SPF联合应用,可进一步了解复发危险度。

关 键 词:乳腺肿瘤  因素分析.统计学  受体.细胞表面  癌基因

Prognosis prediction of S-phase fraction and p53, c-erbB-2, estrogen receptor, progesterone receptor in axillary node-negative breast cancer
Zhang S,Yuan Y,Wang X.Prognosis prediction of S-phase fraction and p53, c-erbB-2, estrogen receptor, progesterone receptor in axillary node-negative breast cancer[J].Chinese Journal of Surgery,1997,35(8):475-477.
Authors:Zhang S  Yuan Y  Wang X
Institution:Department of Oncology, Second Hospital, Zhejiang Medical University, Hangzhou.
Abstract:The prognostic factors in 180 axillary node negative breast cancer patients with more than 5 year follow up were searched for with multiple regression. Based on the regression results, the cases with treatment failure in 5 year and the cases with 5 year disease free survival were matched at 1:2 ratio.Then the c erbB 2 protein, p53 Protein, estrogen receptor (ER), progesterone receptor (PR)and the S phase fraction (SPF) were measured in paraffin embedded breast cancer tissue. The results were analysed with log rank test and Cox Model.Among the biobehaviour factors measured,the SPF was the strongest prognosis predictor for ANN. The 5 year disease free survival rate of ANN with SPF<10% or>10% was 94% and 52% respectively ( P <0 001). The treatment failure relative risk of patients with SPF>10% ANN was 11 31. The relative risk of other four factors was respectively,PR 3 58 ( P <0 002), ER 2 93 ( P <0 05), p53 1 44 ( P >0 3), c erbB 2 1 38 ( P >0 5). The combination of PR or EP with SPF could make the relative risk even higher.
Keywords:Breast neoplasms    Models  statistical    Receptors  cell surface    Oncogenes  
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