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. 2008 Jun;101(3):253-8.
doi: 10.1016/j.ijgo.2008.01.018. Epub 2008 Mar 6.

Identifying biomarkers of endometriosis using serum protein fingerprinting and artificial neural networks

Liang Wang  1 , Wei Zheng, Lin Mu, Su-Zhan Zhang
Affiliations

Identifying biomarkers of endometriosis using serum protein fingerprinting and artificial neural networks

Liang Wang et al. Int J Gynaecol Obstet. 2008 Jun.

Abstract

Objectives: To use surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) protein chip array technology to detect proteomic patterns in the serum of women with endometriosis; build diagnostic models; and evaluate their clinical significance.

Methods: Serum samples from women with endometriosis and healthy women were studied using SELDI-TOF-MS protein chip technology. For every matched pair, two-thirds of the samples were used to look for different patterns and one-third was used for cross-validation.

Results: Five potential biomarkers were found and the diagnostic system distinguished endometriosis from validation samples with a sensitivity of 91.7% and a specificity of 90.0%.

Conclusion: This method shows great potential in identifying biomarkers to be used for endometriosis screening.

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