Siti Suhana, Abdullah Soheimi (2012) Proteomic analysis of urinary proteins from patients with ovarian cancer and cervical cancer / Siti Suhana Abdullah Soheimi. Masters thesis, University of Malaya.
Abstract
Diagnosis of ovarian carcinoma is in urgent need for new complementary biomarkers for early stage detection. In order to find an alternative procedure to replace an uncomfortable conventional method in detecting cervical cancer, a proteomic approach in screening the urinary proteins were employed. Proteins that are aberrantly excreted in the urine of cancer patients are excellent biomarker candidates for development of new non-invasive protocol for early diagnosis and screening purposes. In the present study, urine samples from patients with ovarian carcinoma and cervical cancer were analysed by two-dimensional gel electrophoresis (2-DE) and the profiles generated were compared to those similarly obtained from age-matched cancer negative women. These samples were also subjected to SELDI-TOF-MS as a complimentary technique for 2-DE especially on screening of aberrantly expressed of low molecular weight proteins. Significant reduced levels of CD59, kininogen-1 and a 39 kDa fragment of inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4), and enhanced excretion of a 19 kDa fragment of albumin, were detected in the urine of patients with ovarian carcinoma compared to the control subjects. These proteins, with exception of kininogen-1, were also detected in the urine of patients with cervical cancer as compared to the control subjects. The different altered levels of the proteins were confirmed by Western blotting using antisera and a lectin that bind to the respective proteins. When the samples were analysed with SELDI-TOF-MS, one protein peak with m/z of 15802 was detected in ovarian cancer cohort, but not in cervical cancer and control. The peaks m/z 7528.78 and m/z 8828.8 were found to be significantly absent in ovarian cancer and cervical cancer, respectively. Interestingly, protein peak at m/z 15802 with a p-value less than 0.05 had a potential to be a good biomarker with 100% sensitivity and 89.4% specificity for the learning set obtained from the classification tree in the Biomarker Pattern Software (BPS).
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