Discovering endometriosis biomarkers with multiplex cytokine arrays

Abstract

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.

Keywords: Arrays; Biomarkers; Cytokines; Endometriosis; Multiplex array.

Fig. 1

K-nearest neighbor analysis of 14 protein biomarker panel comparing endometriosis and healthy controls. The sensitivity, specificity and accuracy were 82.8%, 48.1% and 68.0%, respectively

Fig. 2

Dot histogram plot of our 14-marker panel split-point score classification of plasma from healthy control (n = 52) and endometriosis (n = 70). A cutoff score ≥ 9 was set. Samples from endometriosis patients should have a score ≥ 9, whereas normal plasma samples should have a score < 9. Based on the score cutoff, the sensitivity, specificity and accuracy were 90%, 67.3% and 80.3%, respectively

Fig. 3

Receiver operating characteristic (ROC) curve for 14-marker panel logistic regression scores in 122 sample data set of endometriosis and healthy control groups. The area under ROC curve (AUC) was 0.874, and its 95% CI was 0.81–0.94