Abstract
Purpose
Several markers for diagnosis of endometriosis that have been tested in clinical approaches so far lack in both sensitivity and specificity. Therefore, it is conceivable that a need exists to perform a cluster analysis in which the attributes of different markers are considered.
Methods
In this study, protein pattern profiling by surface-enhanced laser desorption/ionization time of flight (SELDI-TOF) mass spectrometry was performed to reveal differentially expressed pathognomonic proteins for endometriosis in peritoneal fluid samples of 106 symptomatic patients.
Results
At laparoscopy, 46 out of 106 (43.4%) patients exhibited endometriosis and 60 (56.6%) were proven disease-free. Using anionic affinity surfaces and a genetic algorithm for data analysis, 16 significantly different protein peaks were identified that allow for the prediction of endometriosis, with an overall recognition capability of 85.2%, exhibiting a sensitivity of 70.6% and a specificity of 80.8%.
Conclusions
Although lacking in sensitivity, this study provides highly significant information about differentially regulated proteins in endometriosis which might play a key role in the pathogenesis of this disease.
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