Weisheng Bao, Nezhat Ceana H, Huang Gordon F, Mao Ying-Qing, Sidell Neil, Huang Ruo-Pan
1RayBiotech, Inc, 3607 Parkway Lane, Peachtree Corners, GA 30092 USA.
2Nezhat Medical Center, 5555 Peachtree Dunwoody Rd #276, Atlanta, GA 30342 USA.
Clin Proteomics. 2019 Jul 11;16:28. doi: 10.1186/s12014-019-9248-y. eCollection 2019.
Chronic pelvic pain is often overlooked during primary examinations because of the numerous causes of such "vague" symptoms. However, this pain can often mask endometriosis, a smoldering disease that is not easily identified as a cause of the problem. As such, endometriosis has been shown to be a potentially long-term and often undiagnosed disease due to its vague symptoms and lack of any non-invasive testing technique. Only after more severe symptoms arise (severe pelvic pain, excessive vaginal bleeding, or infertility) is the disease finally uncovered by the attending physician. Due to the nature and complexity of endometriosis, high throughput approaches for investigating changes in protein levels may be useful for elucidating novel biomarkers of the disease and to provide clues to help understand its development and progression.
A large multiplex cytokine array which detects the expression levels of 260 proteins including cytokines, chemokines, growth factors, adhesion molecules, angiogenesis factors and other was used to probe biomarkers in plasma samples from endometriosis patients with the intent of detecting and/or understanding the cause of this disease. The protein levels were then analyzed using K-nearest neighbor and split-point score analysis.
This technique identified a 14-marker cytokine profile with the area under the curve of 0.874 under a confidence interval of 0.81-0.94. Our training set further validated the panel for significance, specificity, and sensitivity to the disease samples.
These findings show the utility and reliability of multiplex arrays in deciphering new biomarker panels for disease detection and may offer clues for understanding this mysterious disease.
慢性盆腔疼痛在初次检查时常常被忽视，因为这类“模糊”症状的病因众多。然而，这种疼痛常常会掩盖子宫内膜异位症，这是一种隐匿性疾病，不容易被确定为问题的病因。因此，由于其症状模糊且缺乏非侵入性检测技术，子宫内膜异位症已被证明是一种可能长期未被诊断的疾病。只有在出现更严重的症状（严重盆腔疼痛、阴道大量出血或不孕）后，主治医生才最终发现这种疾病。由于子宫内膜异位症的性质和复杂性，用于研究蛋白质水平变化的高通量方法可能有助于阐明该疾病的新型生物标志物，并为理解其发展和进展提供线索。
使用一种大型多重细胞因子阵列，该阵列可检测包括细胞因子、趋化因子、生长因子、黏附分子、血管生成因子等在内的260种蛋白质的表达水平，以探测子宫内膜异位症患者血浆样本中的生物标志物，目的是检测和/或了解这种疾病的病因。然后使用K近邻算法和分割点评分分析来分析蛋白质水平。
该技术识别出了一个由14种标志物组成的细胞因子谱，在0.81 - 0.94的置信区间下，曲线下面积为0.874。我们的训练集进一步验证了该检测组对疾病样本的显著性、特异性和敏感性。
这些发现表明多重阵列在解读用于疾病检测的新生物标志物检测组方面的实用性和可靠性，并可能为理解这种神秘疾病提供线索。
