使用LC-MS/MS和iTRAQ标记定量口腔癌转移患者血清蛋白。

Lifeng Zhang, Jiang Jiang, Martha Arellano, Lei Zhang, Xinmin Yan, David T Wong, Shen Hu
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引用次数: 12

摘要

转移是口腔鳞状细胞癌(OSCC)进展中的一个关键事件。在这项研究中,我们使用液相色谱-串联质谱(LC-MS/MS)对非转移性(无淋巴结转移)和转移性OSCC患者的血清蛋白进行了定量分析,并使用iTRAQ标记(相对和绝对定量等压标记)。为了消除高丰度的蛋白质,首先用SDS-PAGE分离血清样品,只切除低丰度的蛋白质带,用于随后的凝胶内胰蛋白酶消化。然后从每个样品凝胶中提取所得肽,分别用iTRAQ试剂114(对照)、116(非转移)和117(转移)标记。然后,将标记的样品组合,并使用线性离子阱(LIT) MS与脉冲Q碰撞诱导解离(PQD)进行LC-MS/MS分析。该方法共鉴定和定量了64个蛋白。我们的研究表明,iTRAQ标记和PQD的LIT-MS是一种有价值的血清蛋白定量方法。我们还证明了非转移性OSCC和转移性OSCC之间存在差异表达的血清蛋白,这可能进一步被验证为转移性OSCC的生物标志物。然而,为了全面量化低丰度的血清蛋白,在定量分析OSCC血清蛋白质组学之前,需要一种更有效的方法来消耗高丰度的蛋白。
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Quantification of Serum Proteins of Metastatic Oral Cancer Patients Using LC-MS/MS and iTRAQ Labeling.

Metastasis is a critical event in oral squamous cell carcinoma (OSCC) progression. In this study, we have performed quantitative analysis of serum proteins from non-metastatic (lymph-node metastasis free) and metastatic OSCC patients using liquid chromatography-tandem mass spectrometry (LC-MS/MS) with iTRAQ labeling (isobaric tagging for relative and absolute quantitation). To eliminate highly abundant proteins, the serum samples were initially separated by SDS-PAGE and only low abundant protein bands were excised for subsequent in-gel tryptic digestion. The resulting peptides were then extracted from each sample gels and labeled with iTRAQ reagent 114 (control), 116 (non-metastatic) and 117 (metastatic), respectively. Afterwards, the labeled samples were combined and subjected to LC-MS/MS analysis using linear ion trap (LIT) MS with pulsed Q collision induced dissociation (PQD). A total of 64 proteins were identified and quantified by this approach. Our study showed that iTRAQ labeling and LIT-MS with PQD is a valuable approach to quantification of serum proteins. We also demonstrated the presence of differentially expressed serum proteins between non-metastatic and metastatic OSCCs that may be further validated as biomarkers for metastatic OSCC. However, in order to comprehensively quantify low abundant serum proteins, a more efficient approach is needed to deplete highly abundant proteins prior to quantitative serum proteome analysis of OSCC.

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