摘要
Background Endometriosis is a common gynecological disease. This study aimed to screen proteins that were expressed differently in patients with endometriosis versus normal controls using proteomic techniques, surface-enhanced laser desorption/ionization time-of-flight mass spectrometry （SELDI-TOF-MS）.Methods Protein chip SELDI-TOF-MS combines the advantages of microarray and mass spectrometry, and can screen latent markers in sera of patients with endometriosis. Serum samples from patients and normal volunteers were analyzed by SELDI-TOF-MS. Results After comparing the serum protein spectra of 36 patients with 24 normal controls, 24 differently expressed potential biomarkers （P 〈0.01） were identified. Using Biomarker Pattern software, we established a tree model of the 60 serum protein spectra. When using the three bJomarkers to classify the samples, the sensitivity for diagnosing endometriosis was 91.7%, specificity was 95.8%, and coincidence rate was 93.3%. Then we used serum samples from 12 patients and 8 normal controls to validate the tree model and report the sensitivity for diagnosing endometriosis was 91.7%, specificity was 75%, and coincidence rate was 85%. Conclusions SELDI-TOF-MS may be a useful tool in high-risk population screening for endometriosis. The identification and application of the biomarkers need to further study.
Background Endometriosis is a common gynecological disease. This study aimed to screen proteins that were expressed differently in patients with endometriosis versus normal controls using proteomic techniques, surface-enhanced laser desorption/ionization time-of-flight mass spectrometry （SELDI-TOF-MS）.Methods Protein chip SELDI-TOF-MS combines the advantages of microarray and mass spectrometry, and can screen latent markers in sera of patients with endometriosis. Serum samples from patients and normal volunteers were analyzed by SELDI-TOF-MS. Results After comparing the serum protein spectra of 36 patients with 24 normal controls, 24 differently expressed potential biomarkers （P 〈0.01） were identified. Using Biomarker Pattern software, we established a tree model of the 60 serum protein spectra. When using the three bJomarkers to classify the samples, the sensitivity for diagnosing endometriosis was 91.7%, specificity was 95.8%, and coincidence rate was 93.3%. Then we used serum samples from 12 patients and 8 normal controls to validate the tree model and report the sensitivity for diagnosing endometriosis was 91.7%, specificity was 75%, and coincidence rate was 85%. Conclusions SELDI-TOF-MS may be a useful tool in high-risk population screening for endometriosis. The identification and application of the biomarkers need to further study.
基金
This study was supported by the grants from Beijing Municipal Science ＆ Technology Commission （No.H030930040230） and the National Natural Science Foundation of China （No.30772319）.
