Diagnosis of Early Relapse in Ovarian Cancer Using Serum Proteomic Profiling

Jung Hun Oh[1] (joh@cse.uta.edu)
Jean Gao[1] (gao@cse.uta.edu)
Animesh Nandi[2] (Animesh.Nandi@UTSouthwestern.edu)
Prem Gurnani[2] (Prem.Gurnani@UTSouthwestern.edu)
Lynne Knowles[3] (Lynne.Knowles@UTSouthwestern.edu)
John Schorge[3] (John.Schorge@UTSouthwestern.edu)
Kevin P. Rosenblatt[2] (Kevin.Rosenblatt@UTSouthwestern.edu)

[1]Department of Computer Science and Engineering, The University of Texas, Arlington, TX 76019, USA
[2]Department of Pathology, Division of Translational Pathology, UT Southwestern Medical Center, Dallas, TX 75390, USA
[3]Department of Obstetrics and Gynaecology, Division of Gynaecologic Oncology, UT Southwestern Medical Center, Dallas, TX 75390, USA


Surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry data has been increasingly analyzed for identifying biomarkers to help early detection of the disease. Ovarian cancer commonly recurs at the rate of 75% within a few months or several years later after standard treatment. Since recurrent ovarian cancer is relatively difficult to be diagnosed and small tumors generally respond better to treatment, new methods for the detection of early relapse in ovarian cancer are urgently needed. Here, we propose a new algorithm SVM-MB/RFE (SVM-Markov Blanket/Recursive Feature Elimination) based on SVM-RFE, which identifies biomarkers for predicting the early recurrence of ovarian cancer. In this approach, we first apply t-test for feature pruning and then binning using 5-fold cross validation. Finally, 58 peaks are obtained from 27000 of the raw data. Such dramatically reduced features relax the computational burden in the next step of our algorithm. We compare the performance of three feature selection algorithms and demonstrate that SVM-MB/RFE outperforms other methods.

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Japanese Society for Bioinformatics