Title: Predicting survival outcomes in ovarian cancer using gene expression data

Authors: TaeJin Ahn; Nayeon Kang; Yonggab Kim; Se Ik Kim; Yong-Sang Song; Taesung Park

Addresses: Department of Life Science, Handong Global University, Pohang, South Korea ' Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, South Korea ' Department of Statistics, Seoul National University, Seoul, South Korea ' Department of Obstetrics and Gynaecology, Seoul National University College of Medicine, Seoul, South Korea ' Department of Obstetrics and Gynaecology, Seoul National University College of Medicine, Seoul, South Korea ' Department of Statistics, Seoul National University, Seoul, South Korea

Abstract: About 70% of ovarian cancer types are High-Grade Serous Ovarian Cancer (HGSOC). Early stage HGSOC has a survival rate of more than 90%, but most diagnoses reoccur that the overall survival rate is only 35%. To detect early ovarian cancer, many studies have attempted to identify HGSOC-associated genes. In this study, we endeavoured to identify HGSOC related genes from RNA-seq data in The Cancer Genome Atlas (TCGA). We further suggest that stable extraction of genes could overcome difficulties regarding the reproducibility of existing RNA-seq data using a new gene selection strategy by Leave-One-Out Cross Validation (LOOCV). This strategy showed better performance than a previous method, when evaluating the same data set. Using this method, we could also infer biologic functions of selected genes, but instead, subsets of samples associated with different subsets of genes. These findings suggest that multiple signalling pathways contribute to ovarian cancer patient survival.

Keywords: RNA-seq; ovarian cancer; TCGA; the cancer genome atlas; survival analysis.

DOI: 10.1504/IJDMB.2018.098943

International Journal of Data Mining and Bioinformatics, 2018 Vol.21 No.4, pp.339 - 351

Received: 12 Jan 2019
Accepted: 12 Jan 2019

Published online: 30 Mar 2019 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article