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http://dx.doi.org/10.5808/GI.2018.16.4.e32

Ovarian Cancer Prognostic Prediction Model Using RNA Sequencing Data  

Jeong, Seokho (Department of Statistics, Seoul National University)
Mok, Lydia (Interdisciplinary Program in Bioinformatics, Seoul National University)
Kim, Se Ik (Department of Obstetrics and Gynecology, Seoul National University College of Medicine)
Ahn, TaeJin (Department of Life Science, Handong Global University)
Song, Yong-Sang (Department of Obstetrics and Gynecology, Seoul National University College of Medicine)
Park, Taesung (Department of Statistics, Seoul National University)
Publication Information
Genomics & Informatics / v.16, no.4, 2018 , pp. 32.1-32.7 More about this Journal
Abstract
Ovarian cancer is one of the leading causes of cancer-related deaths in gynecological malignancies. Over 70% of ovarian cancer cases are high-grade serous ovarian cancers 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 making an accurate prediction of the prognosis is not easy because of the highly heterogeneous nature of ovarian cancer. To improve the patient's prognosis through proper treatment, we present a prognostic prediction model by integrating high-dimensional RNA sequencing data with their clinical data through the following steps: gene filtration, pre-screening, gene marker selection, 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
ovarian neoplasms; penalized Cox regression; prediction model; RNA sequencing data;
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