Department of General Surgery, The First Affiliated Hospital, Zhengzhou University, Zhengzhou, 450052, Henan, People's Republic of China.
J Cancer Res Clin Oncol. 2010 Aug;136(8):1243-54. doi: 10.1007/s00432-010-0775-1. Epub 2010 Mar 17.
Noninvasive and convenient biomarkers for early diagnosis of breast cancer remain an urgent need. The aim of this study was to discover and identify potential protein biomarkers specific for breast cancer.
Two hundred and eighty-two (282) serum samples with 124 breast cancer and 158 controls were randomly divided into a training set and a blind-testing set. Serum proteomic profiles were analyzed using SELDI-TOF-MS. Candidate biomarkers were purified by HPLC, identified by LC-MS/MS and validated using ProteinChip immunoassays and western blot technique.
A total of 3 peaks (m/z with 6,630, 8,139 and 8,942 Da) were screened out by support vector machine to construct the classification model with high discriminatory power in the training set. The sensitivity and specificity of the model were 96.45 and 94.87%, respectively, in the blind-testing set. The candidate biomarker with m/z of 6,630 Da was found to be down-regulated in breast cancer patients, and was identified as apolipoprotein C-I. Another two candidate biomarkers (8,139, 8,942 Da) were found up-regulated in breast cancer and identified as C-terminal-truncated form of C3a and complement component C3a, respectively. In addition, the level of apolipoprotein C-I progressively decreased with the clinical stages I, II, III and IV, and the expression of C-terminal-truncated form of C3a and complement component C3a gradually increased in higher stages.
We have identified a set of biomarkers that could discriminate breast cancer from non-cancer controls. An efficient strategy, including SELDI-TOF-MS analysis, HPLC purification, MALDI-TOF-MS trace and LC-MS/MS identification, has been proved very successful.
寻找非侵入性和方便的生物标志物，用于早期诊断乳腺癌仍然是一个迫切的需求。本研究旨在发现和鉴定乳腺癌特异性的潜在蛋白质生物标志物。
随机将 282 份血清样本（包括 124 份乳腺癌样本和 158 份对照样本）分为训练集和验证集。采用 SELDI-TOF-MS 分析血清蛋白质组图谱。采用 HPLC 对候选生物标志物进行纯化，采用 LC-MS/MS 鉴定，采用 ProteinChip 免疫分析和 Western blot 技术进行验证。
通过支持向量机筛选出 3 个峰（m/z 分别为 6630、8139 和 8942 Da），构建了具有高判别能力的分类模型，在训练集中的灵敏度和特异性分别为 96.45%和 94.87%。在验证集中，模型的灵敏度和特异性分别为 96.45%和 94.87%。m/z 为 6630 Da 的候选生物标志物在乳腺癌患者中下调，并被鉴定为载脂蛋白 C-I。另外两个候选生物标志物（8139、8942 Da）在乳腺癌中上调，并分别鉴定为 C 端截断的 C3a 和补体成分 C3a。此外，载脂蛋白 C-I 的水平随着临床分期 I、II、III 和 IV 的进展而逐渐降低，C 端截断的 C3a 和补体成分 C3a 的表达逐渐增加。
我们已经确定了一组能够区分乳腺癌和非癌对照的生物标志物。SELDI-TOF-MS 分析、HPLC 纯化、MALDI-TOF-MS 追踪和 LC-MS/MS 鉴定的有效策略被证明非常成功。
