Ovarian Cancer Prognostic Prediction Model Using RNA Sequencing Data
- Author:
Seokho JEONG
1
;
Lydia MOK
;
Se Ik KIM
;
TaeJin AHN
;
Yong Sang SONG
;
Taesung PARK
Author Information
- Publication Type:Original Article
- Keywords: ovarian neoplasms; penalized Cox regression; prediction model; RNA sequencing data
- MeSH: Drug Therapy; Filtration; Mortality; Ovarian Neoplasms; Prognosis; RNA; Sequence Analysis, RNA; Survival Rate
- From:Genomics & Informatics 2018;16(4):e32-
- CountryRepublic of Korea
- Language:English
- 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.
