Xu Wen-Hong, Chen Yi-Ding, Hu Yue, Yu Jie-Kai, Wu Xian-Guo, Jiang Tie-Jun, Zheng Shu, Zhang Su-Zhan
Department of Oncology, Cancer Institute, Clinical Laboratory, Second Affiliated Hospital of Zhejiang University College of Medicine, Hangzhou 310009, China.
Zhonghua Zhong Liu Za Zhi. 2006 Oct;28(10):753-7.
To detect the serum proteomic patterns by using SELDI-TOF-MS and CM10 ProteinChip techniques in colorectal cancer (CRC) patients, and to evaluate the significance of the proteomic patterns in colorectal cancer staging.
A total of 76 serum samples were obtained from CRC patients at different clinical stages, including Dukes A (n = 10), Dukes B (n = 19), Dukes C (n = 16) and Dukes D (n = 31). Different stage models were developed and validated by bioinformatics methods of support vector machines, discriminant analysis and time-sequence analysis.
The model I formed by six proteins of peaks at m/z 2759.6, 2964.7, 2048.0, 4795.9, 4139.8 and 37 761.6 could do the best as potential biomarkers to distinguish local CRC patients (Dukes A and Dukes B) from regional CRC patients (Dukes C ) with an accuracy of 86.7%. The model II formed by 3 proteins of peaks at m/z 6885.3, 2058.3 and 8567.8 could do the best to distinguish locoregional CRC patients (Dukes A, B and C) from systematic CRC patients (Dukes D) with an accuracy of 75.0%. The mode III could distinguish Dukes A from Dukes B with an accuracy of 86.2% (25/29). The model IV could distinguish Dukes A from Dukes C with an accuracy of 84.6% (22/26). The model V could distinguish Dukes B from Dukes C with an accuracy of 85.7% (30/35). The model VI could distinguish Dukes B from Dukes D with an accuracy of 80.0% (40/50). The model VII could distinguish Dukes C from Dukes D with an accuracy of 78.7% (37/47). Different stage groups could be distinguished by the two-dimensional scattered spots figure obviously.
Our findings indicate that this method can well be used in preoperative staging of colorectal cancers and the screened tumor markers may serve for guidance of integrating treatment of colorectal cancers.
应用表面增强激光解吸电离飞行时间质谱(SELDI-TOF-MS)及CM10蛋白质芯片技术检测结直肠癌(CRC)患者血清蛋白质组图谱,并评估蛋白质组图谱在结直肠癌分期中的意义。
收集不同临床分期的CRC患者血清样本76份,包括Dukes A期(n = 10)、Dukes B期(n = 19)、Dukes C期(n = 16)和Dukes D期(n = 31)。采用支持向量机、判别分析和时间序列分析等生物信息学方法建立并验证不同分期模型。
由质荷比(m/z)为2759.6、2964.7、2048.0、4795.9、4139.8和37761.6的6种蛋白峰构成的模型I作为潜在生物标志物区分局部CRC患者(Dukes A和Dukes B)与区域CRC患者(Dukes C)的效果最佳,准确率为86.7%。由m/z为6885.3、2058.3和8567.8的3种蛋白峰构成的模型II区分局部区域性CRC患者(Dukes A、B和C)与系统性CRC患者(Dukes D)的效果最佳,准确率为75.0%。模型III区分Dukes A与Dukes B的准确率为86.2%(25/29)。模型IV区分Dukes A与Dukes C的准确率为84.6%(22/26)。模型V区分Dukes B与Dukes C的准确率为85.7%(30/35)。模型VI区分Dukes B与Dukes D的准确率为80.0%(40/50)。模型VII区分Dukes C与Dukes D的准确率为78.7%(37/47)。不同分期组可通过二维散点图明显区分。
我们的研究结果表明,该方法可很好地用于结直肠癌的术前分期,筛选出的肿瘤标志物可为结直肠癌的综合治疗提供指导。