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Cancers 2019, 11(2), 270; https://doi.org/10.3390/cancers11020270

Developing a Prognostic Gene Panel of Epithelial Ovarian Cancer Patients by a Machine Learning Model

1
Institute of Epidemiology and Preventive Medicine, Department of Public Health, National Taiwan University, Taipei 10055, Taiwan
2
Department of Pathology, College of Medicine, National Taiwan University, Taipei 10002, Taiwan
3
Department of Obstetrics and Gynecology, College of Medicine, National Taiwan University, Taipei 10041, Taiwan
4
Institute of Nuclear Energy Research, Atomic Energy Council, Executive Yuan, Taoyuan 32546, Taiwan
5
Department of Obstetrics and Gynecology, National Taiwan University Hospital Yunlin branch, Yunlin 64041, Taiwan
6
Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei 10002, Taiwan
7
Graduate Institute of Oncology, College of Medicine, National Taiwan University, Taipei 10002, Taiwan
*
Author to whom correspondence should be addressed.
Received: 11 January 2019 / Revised: 18 February 2019 / Accepted: 22 February 2019 / Published: 25 February 2019
(This article belongs to the Special Issue Application of Bioinformatics in Cancers)
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Abstract

Epithelial ovarian cancer patients usually relapse after primary management. We utilized the support vector machine algorithm to develop a model for the chemo-response using the Cancer Cell Line Encyclopedia (CCLE) and validated the model in The Cancer Genome Atlas (TCGA) and the GSE9891 dataset. Finally, we evaluated the feasibility of the model using ovarian cancer patients from our institute. The 10-gene predictive model demonstrated that the high response group had a longer recurrence-free survival (RFS) (log-rank test, p = 0.015 for TCGA, p = 0.013 for GSE9891 and p = 0.039 for NTUH) and overall survival (OS) (log-rank test, p = 0.002 for TCGA and p = 0.016 for NTUH). In a multivariate Cox hazard regression model, the predictive model (HR: 0.644, 95% CI: 0.436–0.952, p = 0.027) and residual tumor size < 1 cm (HR: 0.312, 95% CI: 0.170–0.573, p < 0.001) were significant factors for recurrence. The predictive model (HR: 0.511, 95% CI: 0.334–0.783, p = 0.002) and residual tumor size < 1 cm (HR: 0.252, 95% CI: 0.128–0.496, p < 0.001) were still significant factors for death. In conclusion, the patients of high response group stratified by the model had good response and favourable prognosis, whereas for the patients of medium to low response groups, introduction of other drugs or clinical trials might be beneficial. View Full-Text
Keywords: chemotherapy; microarray; ovarian cancer; predictive model; machine learning chemotherapy; microarray; ovarian cancer; predictive model; machine learning
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Lu, T.-P.; Kuo, K.-T.; Chen, C.-H.; Chang, M.-C.; Lin, H.-P.; Hu, Y.-H.; Chiang, Y.-C.; Cheng, W.-F.; Chen, C.-A. Developing a Prognostic Gene Panel of Epithelial Ovarian Cancer Patients by a Machine Learning Model. Cancers 2019, 11, 270.

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