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Genomics Inform > Volume 16(4); 2018 > Article
DOI: https://doi.org/10.5808/GI.2018.16.4.e32    Published online December 28, 2018.
Ovarian Cancer Prognostic Prediction Model Using RNA Sequencing Data
Seokho Jeong1, Lydia Mok2, Yong-Sang Song3, TaeJin Ahn4, Taesung Park1,2
1Department of Statistics, Seoul National University, Seoul 08826, Korea
2Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea
3Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul 05505, Korea
4Department of Life Science, Handong Global University 37554, Pohang, Korea
Corresponding author:  Taesung Park
Tel: +82-2-880-9168   Fax: +82-2-880-6693   Email: tspark@snu.ac.kr
Received: December 10, 2018   Accepted: December 16, 2018
Abstract
Ovarian cancer is one of the leading causes of cancer-related deaths in gynecologic malignancies. Over 70 % of ovarian cancer cases are high-grade serous ovarian cancers (HGSC) and have high death rates due to their resistance to chemotherapy. Despite advances in surgical and pharmaceutical therapies, overall survival rates are not good and accurate prediction of prognosis is not easy because of the highly heterogeneous nature of ovarian cancer. To improve patient’s prognosis through proper treatment, we present a prognostic prediction model by integrating the high dimensional RNA sequencing data with their clinical data through the following steps: (1) gene filtration, (2) pre-screening, (3) gene marker selection (4) integrated study of selected gene markers and prediction model building. These steps of the prognostic prediction model can be applied to other types of cancer besides ovarian cancer.
Keywords: RNA-sequencing data, Ovarian Cancer, Penalized Cox regression, Prediction model


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