Genomics Inform.  2018 Dec;16(4):e32. 10.5808/GI.2018.16.4.e32.

Ovarian Cancer Prognostic Prediction Model Using RNA Sequencing Data

Affiliations
  • 1Department of Statistics, Seoul National University, Seoul 08826, Korea. tspark@stats.snu.ac.kr
  • 2Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea.
  • 3Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Seoul 03080, Korea.
  • 4Department of Life Science, Handong Global University, Pohang 37554, Korea.

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.

Keyword

ovarian neoplasms; penalized Cox regression; prediction model; RNA sequencing data

MeSH Terms

Drug Therapy
Filtration
Mortality
Ovarian Neoplasms*
Prognosis
RNA*
Sequence Analysis, RNA*
Survival Rate
RNA
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