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Antibody microarray as a proteomic tool for effective diagnosis and prediction of prognosis in cancer

Srinivasan, Harish

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Effective prediction of diagnosis and prognosis in cancer is an important step for selection of a suitable treatment regimen. In this dissertation, the importance of using antibody microarray for effective diagnosis and prognosis of cancer was studied using bladder and gastric cancers. In the first part of study, establishment of a protein signature to predict recurrence of non-muscle invasive bladder cancer was aimed at. Antibodies against cancer-related proteins were spotted and proteins from recurrent and non-recurrent non-muscle invasive bladder cancer tissues incubated. The protein profiles of the samples were analyzed for statistical significance and differential expression of proteins among the cancer groups. After a series of analysis using bioinformatic tools, a 20 protein-signature predicting recurrence of non-muscle invasive bladder cancer was identified along with important molecular mechanisms underlying recurrence. High grade gastric adenocarcinomas are often lethal with metastasis and frequent recurrence. The second study concentrated on more personalized cancer medicine by direct comparison of healthy controls and gastric adenocarcinoma tissues from the same patient. Antibody microarray was used to study the protein profiles of gastric cancer by incubating protein samples from healthy controls in tandem with the cancer protein from the same patient. Statistically and clinically significant proteins were identified including a 16 protein signature for betterment of individual-based cancer treatment regimen. Identified biomarkers included known therapeutic targets such as VEGFA, S100A9 and newly identified markers like OCLN and TIA1. The analyses on two cancer types revealed two different protein signatures with high specificity and sensitivity. Moreover, our findings were clinically relevant and superior to many other approved available methods for diagnosis and prognosis.

Item Type: Dissertation
Supervisor: Steinbeisser, Prof. Dr. Herbert
Publisher: Harish Srinivasan
Place of Publication: Heidelberg, Germany
Date of thesis defense: 21 January 2014
Date Deposited: 28 Jan 2014 10:08
Date: 2014
Faculties / Institutes: The Faculty of Bio Sciences > Dean's Office of the Faculty of Bio Sciences
Subjects: 500 Natural sciences and mathematics
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