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目的:借助于蛋白质指纹技术及MATLAB软件探索预测FOLFOX4方案治疗大肠癌耐药的可能性。方法:选择12例曾手术并接受FOLFOX4方案化疗并有确切疗效的大肠癌患者,应用CM10弱阳离子芯片结合表面增强飞行时间质谱(SELDI-TOF-MS)技术于化疗前检测患者血清样本的蛋白质谱,动态观察该方案化疗后2周至半年内,根据实体瘤近期疗效标准分为用药稳定组(SD)(7例)和无效组(PD)(5例),应用Biomarker Wizard软件得出两组间有统计学意义的差异指纹。用MATLAB软件进行多项式曲线拟合得出每个差异指纹的拟合曲线及曲线方程。结果:术后稳定组与无效组相比有3个蛋白质峰有显著差异性,M/Z分别为1204、2868、4176,其中与稳定组相比,无效组上调的峰M/Z为2868,下调的峰M/Z为1204和4176。用MATLAB软件进行多项式曲线拟合得出每个差异指纹的曲线且曲线方程均呈线性的函数关系。结论:MATLAB软件根据实测值获得的曲线方程及曲线呈有意义的线性函数关系,因此借助于蛋白质指纹技术预测FOLFOX4方案治疗大肠癌的耐药是可行的。以此为平台扩大病例数进一步验证即可获得能应用于临床的预测指标。
Objective: To explore the possibility of FOLFOX4 regimen in the treatment of drug-resistant colon carcinoma by means of protein fingerprinting and MATLAB software. Methods: Twelve patients with colorectal cancer who had undergone FOLFOX4 regimen and had definite therapeutic effect were selected. CM10 weak cation chip combined with surface-enhanced time of flight mass spectrometry (SELDI-TOF-MS) was used to detect the protein profiles of serum samples before chemotherapy , Dynamic observation of the program chemotherapy within 2 weeks to 6 months, according to the solid tumor short-term efficacy criteria were divided into medication stable group (SD) (7 cases) and invalid group (PD) (5 cases) There are statistically significant differences in fingerprints. Using MATLAB software for polynomial curve fitting to obtain the fitting curve and curve equation of each differential fingerprint. Results: There were significant differences in the three protein peaks between the stable group and the ineffective group with M / Z of 1204, 2868 and 4176, respectively. Compared with the stable group, the up-peak M / Z of the ineffective group was 2868, The downgraded peaks M / Z are 1204 and 4176. Polynomial curve fitting was done with MATLAB software to get the curve of each differential fingerprint and the curve equation showed a linear function. Conclusion: The curve equation and curve obtained by MATLAB software have a significant linear function, so it is feasible to predict the drug resistance of FOLFOX4 in the treatment of colorectal cancer by means of protein fingerprinting. As a platform to expand the number of cases further verification can be used to predict clinical indicators.