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首页> 外文期刊>Journal of Molecular Biochemistry >Evaluation of auto-antibody serum biomarkers for breast cancer screening and in silico analysis of sero-reactive proteins
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Evaluation of auto-antibody serum biomarkers for breast cancer screening and in silico analysis of sero-reactive proteins

机译：用于乳腺癌筛查和血清反应蛋白计算机分析的自身抗体血清生物标记物的评估

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Aberrantly expressed proteins in tumours evoke an immunological response. These immunogenic proteins can serve as potential biomarkers for the early diagnosis of cancers. In this study, we performed a candidate marker screen on macroarrays containing 38,016 human proteins, derived from a human fetal-brain expression library, with the pools of sera from breast cancer patients (1 pool of benign samples, 3 pools of ductal carcinoma and 2 pools of lobular carcinoma) and 1 pool of sera from healthy women. A panel of 642 sero-reactive clones were deduced from these macroarray experiments which include 284 in-frame clones. Over-representation analyses of the sero-reactive in-frame clones enabled the identification of the sets of genes over-expressed in various pathways of the functional categories (KEGG, Transpath, Pfam and GO). Protein microarrays, generated using the His-tag proteins derived from the macroarray experiments, were used to evaluate the sera from breast cancer patients (24 malignant, 16 benign) and 20 control individuals. Using the PAM algorithm we elucidated?a panel of 50 clones which enabled the correct classification prediction of 93% of the breast-nodule positive group (benign & malignant) sera from healthy individuals’ sera with 100% sensitivity and 85% specificity. This was followed by over-representation analysis of the significant clones derived from the class prediction.
机译：肿瘤中异常表达的蛋白质会引起免疫反应。这些免疫原性蛋白可以作为癌症早期诊断的潜在生物标记。在这项研究中，我们对包含来自人类胎脑表达文库的38,016种人类蛋白的大阵列进行了候选标记筛选，这些蛋白来自乳腺癌患者的血清（1例良性样品，3例导管癌和2例乳腺癌）。合并小叶癌）和1份健康女性血清。从这些大阵列实验中推导出一组642个血清反应性克隆，其中包括284个框内克隆。血清反应性框内克隆的过度表达分析能够鉴定在功能类别（KEGG，Transpath，Pfam和GO）的各种途径中过表达的基因集。使用源自大阵列实验的His-tag蛋白生成的蛋白质微阵列用于评估乳腺癌患者（24名恶性，16名良性）和20名对照个体的血清。通过使用PAM算法，我们阐明了一个由50个克隆组成的小组，这些克隆能够以100％的敏感性和85％的特异性对来自健康个体血清的93％乳腺结节阳性组（良性和恶性）血清进行正确的分类预测。接下来是对来自类预测的重要克隆的过度表达分析。

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