Department of Chemistry and Institutes of Biomedical Sciences of Shanghai Medical School, Fudan University, 138 Yi Xueyuan Road, Shanghai, 200032, PR China.
Analyst. 2013 Aug 21;138(16):4505-11. doi: 10.1039/c3an00517h. Epub 2013 Jun 10.
Secretomics is receiving more and more considerable attention due to the key roles of secreted proteins in cancer. Most of the potential biomarkers for clinical diagnosis and treatment of cancer are secreted proteins. However, the low concentration of secreted proteins and contaminants released from dead cells are a great challenge to secretomic profiling studies. Although some bioinformatics tools such as SecretomeP and SignalP can help to annotate or predict secreted proteins, they also cause false positive or negative rates of identification especially for nonclassical secreted proteins. Therefore, an iTRAQ based quantitative proteomics strategy was set up in this work and applied in the secretomics study of metastatic HCC cell lines. A total of 94 proteins were identified as secreted and 31 of them were newly found in our data. Compared with the known secreted proteins participating in inter-cellular signalling, most of the newly identified secreted proteins were metabolic enzymes, such as PKM2 and EHHADH, whose functions focused on the synthesis/metabolism of glucose, fatty acids and amino acids. Exploring their secretion would help to further study their bio-functions in conditioned media and the effects on the interactions of cancer cells and the microenvironment. Differences between the secretomes of the two metastatic HCC cell lines were also explored in the same experiment. This strategy showed its superiority in accurately identifying secreted proteins as well as monitoring their variation under different biological conditions.
由于分泌蛋白在癌症中的关键作用,分泌组学受到了越来越多的关注。大多数用于癌症临床诊断和治疗的潜在生物标志物都是分泌蛋白。然而,分泌蛋白的浓度低,以及死细胞释放的污染物,这对分泌组学分析研究是一个巨大的挑战。尽管一些生物信息学工具,如 SecretomeP 和 SignalP,可以帮助注释或预测分泌蛋白,但它们也会导致识别的假阳性或假阴性率,特别是对于非经典分泌蛋白。因此,本工作建立了基于 iTRAQ 的定量蛋白质组学策略,并将其应用于转移性 HCC 细胞系的分泌组学研究。共鉴定出 94 种分泌蛋白,其中 31 种是在本研究中首次发现的。与参与细胞间信号传递的已知分泌蛋白相比,大多数新鉴定的分泌蛋白是代谢酶,如 PKM2 和 EHHADH,它们的功能主要集中在葡萄糖、脂肪酸和氨基酸的合成/代谢上。探索它们的分泌将有助于进一步研究它们在条件培养基中的生物功能以及对癌细胞与微环境相互作用的影响。在同一实验中还探索了两种转移性 HCC 细胞系分泌组之间的差异。该策略在准确识别分泌蛋白及其在不同生物条件下的变化方面表现出了优越性。