Background 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. Methods 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. Results 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. Conclusions 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.

中文翻译：

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使用多重细胞因子阵列发现子宫内膜异位症生物标志物。

背景慢性盆腔疼痛在初次检查时经常被忽视，因为这种“模糊”症状的原因有很多。然而，这种疼痛通常会掩盖子宫内膜异位症，这是一种不易被识别为问题原因的阴燃性疾病。因此，子宫内膜异位症已被证明是一种潜在的长期疾病，并且由于其模糊的症状和缺乏任何非侵入性检测技术而常常无法确诊。只有在出现更严重的症状（严重的骨盆疼痛、阴道流血过多或不孕）后，主治医师才能最终发现该疾病。由于子宫内膜异位症的性质和复杂性，研究蛋白质水平变化的高通量方法可能有助于阐明疾病的新生物标志物，并提供线索以帮助了解其发展和进展。方本病的病因。然后使用 K-最近邻和分裂点评分分析分析蛋白质水平。结果 该技术在 0.81-0.94 的置信区间下确定了 14 个标记的细胞因子谱，曲线下面积为 0.874。我们的训练集进一步验证了面板的重要性，对疾病样本的特异性和敏感性。结论 这些发现显示了多重阵列在破译用于疾病检测的新生物标志物组方面的实用性和可靠性，并可能为了解这种神秘疾病提供线索。