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Title: Identifying biomarkers for non-invasive diagnosis of endometriosis
Author: Irungu, S. N.
ISNI:       0000 0004 8498 5344
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2016
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Endometriosis is a gynaecological disorder occurring when endometrial cells are shed through the fallopian tubes and implant on surfaces in the abdomen and pelvis. There they form lesions that respond to hormones of the cycle and stimulate inflammation. Women with endometriosis experience painful debilitating periods, pain on intercourse and defecation, and may have difficulties conceiving. It is a common disorder, affecting 5-10% of women of reproductive age. Diagnosis of endometriosis is difficult and is often delayed by 5-11 years. Symptoms do not correlate with disease severity and imaging techniques are only sensitive for diagnosing ovarian endometriomas. Definitive diagnosis is surgical, requiring laparoscopy under general anaesthetic, exposing patients to potentially serious complications. With these facts in mind, the aim of this project was to identify biomarkers for the non-invasive diagnosis of endometriosis. This was achieved by defining the protein expression profiles of tissue samples collected from women diagnosed with endometriosis and from control patients who underwent surgery for investigation of chronic pelvic pain or who underwent prophylactic surgery because of familial cancer history. Discovery work involved the use of complementary, quantitative proteomic profiling by 2D difference gel electrophoresis and multiplex mass tagging linked to liquid chromatography-based separation and tandem mass spectrometry. Selected candidate biomarkers (LUM, CPM, TNC, TPM2 and PAEP) were verified using ELISA in serum samples collected from the same women. Biomarkers reported in the literature were also tested. Diagnostic performance of each marker was established. The best single marker in discriminating endometriosis and controls was CA125 (AUC=0.724, P=0.002). Multi-marker models were also constructed and the best model in discriminating between endometriosis and healthy controls by cross-validation was CA125, ICAM (AUC=0.744). CA125, ICAM, FST model (AUC=0.75) gave the performance in discriminating between endometriosis and both controls by cross-validation.
Supervisor: Timms, J. F. ; Saridogan, E. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available