Identifying Protein Biomarkers for Ovarian Cancer
Research Abstract
Ovarian cancer is the leading cause of gynecologic cancer deaths among American women according to the American Cancer Society. The reason is attributed to the fact that most cases get diagnosed at late stages of cancer at which point chances of successful treatment are very low. Currently, there is no effective non-invasive method to screen for ovarian cancer. Surgery is the only reliable way to distinguish between benign and malignant disease, resulting in several unnecessary surgeries. The objective of this research is to develop an optimal biomarker prediction model to diagnose ovarian cancer. The model will help develop strategies for detection that can be both highly accurate and minimally invasive. Protein Fractionation- 2 Dimension (PF2D) is a system of hardware, software and chemistry that can be used to identify protein levels from both urine and blood samples. However, there are major challenges in analyzing the data resulting from PF2D. Some of these challenges are removal of noise, baseline correction, spectra normalization, peak extraction, protein biomarkers identification and finally pattern generation for classification. For this research, PF2D data from 10 urine samples of cases paired with their corresponding controls are being analyzed. We address the above mentioned challenges by developing optimization and data mining models that can efficiently classify cases and controls.
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