Clinical potential of proteomics in the diagnosis of ovarian cancer.
Clinical potential of proteomics in the diagnosis of ovarian cancer.
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DOI：
10.1586/14737159.2.4.312
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发表时间：
2002-07-01
影响因子：
5.1
通讯作者：
Petricoin, Emanuel F 3rd
中科院分区：
文献类型：
--
作者：
Ardekani, Ali M;Liotta, Lance A;Petricoin, Emanuel F 3rd
对卵巢癌特异性和敏感性标志物的需求至关重要。找到一种敏感和特异的检测方法对公共卫生具有重要的影响。目前，卵巢癌患者没有有效的筛查选择。CA-125是卵巢癌最广泛使用的生物标志物，其阳性预测值并不高，只有与其他诊断测试结合使用时才有效。然而，卵巢内发生的病理变化可能反映在血清中的生物标志物模式中。结合新的蛋白质组学技术产生的质谱，如表面增强激光解吸电离飞行时间（SELDI-TOF）和基于人工智能的信息算法，已被用于发现一小部分关键蛋白质值，并区分正常卵巢癌患者。血清蛋白质组学模式分析可能最终应用于医学筛查诊所，作为诊断性检查和评估的补充。
The need for specific and sensitive markers of ovarian cancer is critical. Finding a sensitive and specific test for its detection has an important public health impact. Currently, there are no effective screening options available for patients with ovarian cancer. CA-125, the most widely used biomarker for ovarian cancer, does not have a high positive predictive value and it is only effective when used in combination with other diagnostic tests. However, pathologic changes taking place within the ovary may be reflected in biomarker patterns in the serum. Combination of mass spectra generated by new proteomic technologies, such as surface-enhanced laser desorption ionization time-of-flight (SELDI-TOF) and artificial-intelligence-based informatic algorithms, have been used to discover a small set of key protein values and discriminate normal from ovarian cancer patients. Serum proteomic pattern analysis might be applied ultimately in medical screening clinics, as a supplement to the diagnostic work-up and evaluation.