Liu Daren, Cao Liping, Yu Jiekai, Que Risheng, Jiang Wenzhi, Zhou Yiming, Zhu Linhua
Department of Surgery, Second Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, PR China.
Pancreatology. 2009;9(1-2):127-35. doi: 10.1159/000178883. Epub 2008 Dec 13.
To develop a serum-specific protein fingerprint which is capable of differentiating samples from patients with pancreatic cancer and those with other pancreatic conditions.
We used SELDI-TOF-MS coupled with CM10 chips and bioinformatics tools to analyze a total of 118 serum samples in this study; 78 serum samples were analyzed to establish the diagnostic models and the other 40 samples were analyzed on the second day as an independent test set.
The analysis of this independent test set yielded a specificity of 91.6% and a sensitivity of 91.6% for pattern 1, which distinguished pancreatic adenocarcinoma (PC) from healthy individuals and a specificity of 80.0% and a sensitivity of 90.9% for pattern 2, which distinguished PC from chronic pancreatitis.
This study indicated that the SELDI-TOF-MS technique can facilitate the discovery of better serum tumor biomarkers and a combination of specific models is more accurate than a single model in diagnosis of PC.
开发一种血清特异性蛋白质指纹图谱，以区分胰腺癌患者和其他胰腺疾病患者的样本。
本研究使用表面增强激光解吸电离飞行时间质谱（SELDI-TOF-MS）结合CM10芯片和生物信息学工具分析了总共118份血清样本；分析78份血清样本以建立诊断模型，另外40份样本在第二天作为独立测试集进行分析。
对这个独立测试集的分析得出，模式1区分胰腺腺癌（PC）与健康个体时的特异性为91.6%，敏感性为91.6%；模式2区分PC与慢性胰腺炎时的特异性为80.0%，敏感性为90.9%。
本研究表明，SELDI-TOF-MS技术有助于发现更好的血清肿瘤生物标志物，并且在PC诊断中，特定模型的组合比单一模型更准确。
