4.5 Article

Microarrays of tumor cell derived proteins uncover a distinct pattern of prostate cancer serum immunoreactivity

期刊

PROTEOMICS
卷 3, 期 11, 页码 2200-2207

出版社

WILEY-V C H VERLAG GMBH
DOI: 10.1002/pmic.200300611

关键词

protein microarrays; tumor antigens; two-dimensional chromatography

向作者/读者索取更多资源

The broad characterization of the immune responses elicited by tumors has valuable applications in diagnostics and basic research. We present here the use of microarrays of tumor-derived proteins to profile the antibody repertoire in the sera of prostate cancer patients and controls. Two-dimensional liquid chromatography was used to separate proteins from the prostate cancer cell line LNCaP into 1760 fractions. These fractions were spotted in microarrays on coated microscope slides, and the microarrays were incubated individually with serum samples from 25 men with prostate cancer and. controls. The amount of immunoglobulin bound to each fraction by each serum 25 male.. sample was quantified. Statistical analysis revealed that 38 of the fractions had significantly higher levels of immunoglobulin binding in the prostate cancer samples compared to the controls. Two fractions showed higher binding in the control samples. The significantly higher immunoglobulin reactivity from the prostate cancer samples may reflect a strong immune response to the tumors in the prostate cancer patients. We used multivariate analysis to classify the samples as either prostate cancer or control. In a crossvalidation study, recursive partitioning classified the samples with 84% accuracy. A decision tree with two levels of partitioning classified the samples with 98% accuracy. Additional studies will allow further characterization of tumor antigens in prostate cancer and their significance for diagnosis. These results suggest that microarrays of fractionated proteins could be a powerful tool for tumor antigen discovery and cancer diagnosis.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.5
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据