Molecular Medicine Research Center, Chang Gung University, Tao-Yuan 333, Taiwan.
Mol Cell Proteomics. 2010 Jun;9(6):1100-17. doi: 10.1074/mcp.M900398-MCP200. Epub 2010 Feb 1.
Although cancer cell secretome profiling is a promising strategy used to identify potential body fluid-accessible cancer biomarkers, questions remain regarding the depth to which the cancer cell secretome can be mined and the efficiency with which researchers can select useful candidates from the growing list of identified proteins. Therefore, we analyzed the secretomes of 23 human cancer cell lines derived from 11 cancer types using one-dimensional SDS-PAGE and nano-LC-MS/MS performed on an LTQ-Orbitrap mass spectrometer to generate a more comprehensive cancer cell secretome. A total of 31,180 proteins was detected, accounting for 4,584 non-redundant proteins, with an average of 1,300 proteins identified per cell line. Using protein secretion-predictive algorithms, 55.8% of the proteins appeared to be released or shed from cells. The identified proteins were selected as potential marker candidates according to three strategies: (i) proteins apparently secreted by one cancer type but not by others (cancer type-specific marker candidates), (ii) proteins released by most cancer cell lines (pan-cancer marker candidates), and (iii) proteins putatively linked to cancer-relevant pathways. We then examined protein expression profiles in the Human Protein Atlas to identify biomarker candidates that were simultaneously detected in the secretomes and highly expressed in cancer tissues. This analysis yielded 6-137 marker candidates selective for each tumor type and 94 potential pan-cancer markers. Among these, we selectively validated monocyte differentiation antigen CD14 (for liver cancer), stromal cell-derived factor 1 (for lung cancer), and cathepsin L1 and interferon-induced 17-kDa protein (for nasopharyngeal carcinoma) as potential serological cancer markers. In summary, the proteins identified from the secretomes of 23 cancer cell lines and the Human Protein Atlas represent a focused reservoir of potential cancer biomarkers.
尽管癌细胞分泌组谱分析是一种很有前途的策略，可用于鉴定潜在的体液可及性癌症生物标志物，但仍存在一些问题，例如癌细胞分泌组可以挖掘的深度以及研究人员从不断增加的已鉴定蛋白质中选择有用候选物的效率。因此，我们使用一维 SDS-PAGE 和纳升 LC-MS/MS 在 LTQ-Orbitrap 质谱仪上对来自 11 种癌症类型的 23 个人类癌细胞系的分泌组进行了分析，以生成更全面的癌细胞分泌组。共检测到 31,180 种蛋白质，占 4,584 种非冗余蛋白质，平均每种细胞系可鉴定出 1,300 种蛋白质。使用蛋白质分泌预测算法，55.8%的蛋白质似乎是从细胞中释放或脱落的。根据三种策略选择鉴定出的蛋白质作为潜在的标记候选物：（i）一种癌症类型特有的但其他类型没有的蛋白质（癌症类型特异性标记候选物），（ii）大多数癌细胞系释放的蛋白质（泛癌症标记候选物），以及（iii）推测与癌症相关途径有关的蛋白质。然后，我们在人类蛋白质图谱中检查蛋白质表达谱，以鉴定同时在分泌组中检测到并在癌症组织中高度表达的生物标志物候选物。这种分析为每种肿瘤类型产生了 6-137 个标记候选物和 94 个潜在的泛癌症标记物。其中，我们选择性地验证了单核细胞分化抗原 CD14（用于肝癌）、基质细胞衍生因子 1（用于肺癌）以及组织蛋白酶 L1 和干扰素诱导的 17-kDa 蛋白（用于鼻咽癌）作为潜在的血清学癌症标志物。总之，从 23 种癌细胞系的分泌组和人类蛋白质图谱中鉴定出的蛋白质代表了一个潜在癌症生物标志物的集中资源库。
