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MB-WCX联合MALDI-TOF MS建立结直肠癌血清诊断模型

Established of Serum Diagnostic Model for Colorectal Cancer Patients Using MB-WCX and MALDI-TOF MS

  • 摘要:
    目的 运用弱阳离子磁珠(magnetic beads based weak cation exchange, MB-WCX)联合基质辅助激光解吸离子飞行时间质谱(matrix assisted laser desorption ionization time of flight mass spectrometry, MALDI-TOF MS)建立结直肠癌血清蛋白组学诊断模型。
    方法 收集我院正常对照(健康体检者)、结直肠癌术前及术后患者血清标本各72例,弱阳离子磁珠分离血清多肽,MALDI-TOF MS建立正常对照、结直肠癌术前及术后患者血清蛋白表达谱,ClinProt Tools 2.0软件分析差异表达峰并建立诊断模型,液相色谱-电喷雾离子化质谱(liquid chromatography-eletronic spray ionization mass/mass, LC-ESI-MS/MS)鉴定差异表达蛋白。
    结果 对比分析正常对照、结直肠癌术前及术后血清蛋白图谱,共发现80个差异表达峰,12个峰差异具有统计学意义(均P < 0.01),与对照组相比,其中9个差异峰在结直肠癌术前的血清蛋白图谱中显示升高,术后显示降低,3个峰在结肠癌术前的血清蛋白图谱中显示降低,术后显示升高。遗传算法(genetic algorithm, GA)模型诊断结直肠癌的敏感度和特异性分别为99.31%和96.49%。GA模型中m/z: 2663.36、m/z: 4793.17和m/z: 5343.48的差异表达峰经鉴定分别为纤维蛋白原α前体亚型1(isoform 1 of Fibrinogen alpha chain precursor, FGA)、组蛋白赖氨酸甲基转移酶SETD7(histone-lysine N-methyltransferase SETD7, SETD7)和黏蛋白5AC(Mucin-5AC precursor, MUC5AC)。
    结论 血清蛋白质谱模型能够准确区分正常对照与结直肠癌患者,但尚需更进一步研究证实。

     

    Abstract:
    Objective Serum protein expression profiling was examined using magnetic bead-based matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF-MS) to establish a serum proteomic diagnostic model for colorectal cancer.
    Methods Serum samples of normal control (CRTL, n=72), colorectal cancer (pre-operation CRC, n=72, and post-operation CRC, n=72) were collected from 2014-9-1 to 2016-9-1. Peptidome of all samples were extracted by magnetic-bead-based weak cation-exchange chromatography (MB-WCX) and detected by calibrated Autoflex Ⅲ MALDI-TOF-MS. Peptide mass fingerprinting were analyzed by ClinProtTools 2.0 software, and the differentially-expressioned peptides were further identified using LC-ESI-MS/MS.
    Results MALDI-TOF-MS identified 80 peaks (m/z), in which 12 peaks showed significant differences among CRTL, pre-operation and post-operation CRC patients (P < 0.01). 9 peaks were up-regulated and 3 peaks were down-regulated in CRC compared with CRTL, and these peaks showed a tendency to CRTL after operation. Based on the GA model, CRC patients could be discriminated from CRTL with 99.31% sensitivity and 96.49% specificity. Moreover, 3 peaks (m/z: 2663.36, m/z: 4793.17 and m/z: 5343.48) of the GA model were identified as protein FGA, SETD7 and MUC5AC respectively.
    Conclusion The serum proteomic diagnostic model could accurately distinguish between CRTL and CRC, but it needs further research.

     

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