Prioritization of cancer marker candidates based on the immunohistochemistry staining images deposited in the human protein atlas.
Prioritization of cancer marker candidates based on the immunohistochemistry staining images deposited in the human protein atlas.
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DOI：
10.1371/journal.pone.0081079
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发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Wu KP
中科院分区：
文献类型：
--
作者：
Chiang SC;Han CL;Yu KH;Chen YJ;Wu KP
肿瘤标志物发现是高通量定量蛋白质组学研究的一个新兴课题。然而，组学技术通常会产生一长串候选标记，这需要一个劳动密集型的过滤过程，以筛选潜在的有用标记。具体来说，各种参数，如与敏感性相关的标志物在目标癌症类型中的过表达水平，以及标志物在癌症组中的特异性，都是最关键的考虑因素。在这种过滤过程中，基于免疫组织化学（IHC）染色图像的蛋白质表达谱是一种常用的技术。为了系统地研究不同癌症与正常组织和细胞类型中的蛋白质表达，人类蛋白质图谱是一个最全面的资源，因为它包括数百万高分辨率的免疫组化图像和专家策划的注释。为了便于从大规模组学数据集中筛选潜在的候选生物标志物，在本研究中，我们提出了一种量化配对癌/正常组织和癌/正常细胞类型的IHC注释的评分方法。我们综合计算了人类蛋白图谱（Human Protein Atlas）中所有17219个检测抗体的累积IHC图像，得到了涵盖20种不同类型癌症的457110个分数。统计测试证明了提出的评分方法对癌症特异性蛋白质进行优先排序的能力。20种癌症类型的前100个潜在标记候选物被优先排序，具有统计学意义。此外，对结直肠癌（CRC）患者配对癌组织和邻近正常组织中鉴定的1482种膜蛋白进行了模型研究。所提出的评分方法证明了成功的优先级排序，并确定了四个CRC标记，包括两个最广泛使用的标记，即CEACAM5和CEACAM6。这些结果证明了这种评分方法在癌症标志物发现和开发方面的潜力。所有的计算分数都可以在http://bal.ym.edu.tw/hpa/上找到。
Cancer marker discovery is an emerging topic in high-throughput quantitative proteomics. However, the omics technology usually generates a long list of marker candidates that requires a labor-intensive filtering process in order to screen for potentially useful markers. Specifically, various parameters, such as the level of overexpression of the marker in the cancer type of interest, which is related to sensitivity, and the specificity of the marker among cancer groups, are the most critical considerations. Protein expression profiling on the basis of immunohistochemistry (IHC) staining images is a technique commonly used during such filtering procedures. To systematically investigate the protein expression in different cancer versus normal tissues and cell types, the Human Protein Atlas is a most comprehensive resource because it includes millions of high-resolution IHC images with expert-curated annotations. To facilitate the filtering of potential biomarker candidates from large-scale omics datasets, in this study we have proposed a scoring approach for quantifying IHC annotation of paired cancerous/normal tissues and cancerous/normal cell types. We have comprehensively calculated the scores of all the 17219 tested antibodies deposited in the Human Protein Atlas based on their accumulated IHC images and obtained 457110 scores covering 20 different types of cancers. Statistical tests demonstrate the ability of the proposed scoring approach to prioritize cancer-specific proteins. Top 100 potential marker candidates were prioritized for the 20 cancer types with statistical significance. In addition, a model study was carried out of 1482 membrane proteins identified from a quantitative comparison of paired cancerous and adjacent normal tissues from patients with colorectal cancer (CRC). The proposed scoring approach demonstrated successful prioritization and identified four CRC markers, including two of the most widely used, namely CEACAM5 and CEACAM6. These results demonstrate the potential of this scoring approach in terms of cancer marker discovery and development. All the calculated scores are available at http://bal.ym.edu.tw/hpa/.
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影响因子：
11.2
作者：
Coffelt SB;Scandurro AB
通讯作者：
Scandurro AB
DOI：
10.1074/mcp.m110.003087
发表时间：
2011-04
期刊：
Molecular & cellular proteomics : MCP
影响因子：
--
作者：
Han CL;Chen JS;Chan EC;Wu CP;Yu KH;Chen KT;Tsou CC;Tsai CF;Chien CW;Kuo YB;Lin PY;Yu JS;Hsueh C;Chen MC;Chan CC;Chang YS;Chen YJ
通讯作者：
Chen YJ
影响因子：
8.8
作者：
Duncan, R.;Carpenter, B.;Main, L. C.;Telfer, C.;Murray, G. I.
通讯作者：
Murray, G. I.
影响因子：
2.8
作者：
Hensel, Jonathan A.;Chanda, Diptiman;Kumar, Sanjay;Sawant, Anandi;Grizzle, William E.;Siegal, Gene P.;Ponnazhagan, Selvarangan
通讯作者：
Ponnazhagan, Selvarangan
影响因子：
2.1
作者：
An, LL;Ma, XT;Wu, KF
通讯作者：
Wu, KF