A Bioinformatics Analysis of Ovarian Cancer Data Using Machine Learning

Author:

Schilling Vincent12ORCID,Beyerlein Peter3,Chien Jeremy2ORCID

Affiliation:

1. Department of Engineering and Natural Sciences, Technical University of Applied Sciences Wildau, 15745 Wildau, Germany

2. Department of Biochemistry and Molecular Medicine, University of California, Davis, CA 95817, USA

3. Ibiomics UG, 14193 Berlin, Germany

Abstract

The identification of biomarkers is crucial for cancer diagnosis, understanding the underlying biological mechanisms, and developing targeted therapies. In this study, we propose a machine learning approach to predict ovarian cancer patients’ outcomes and platinum resistance status using publicly available gene expression data. Six classical machine-learning algorithms are compared on their predictive performance. Those with the highest score are analyzed by their feature importance using the SHAP algorithm. We were able to select multiple genes that correlated with the outcome and platinum resistance status of the patients and validated those using Kaplan–Meier plots. In comparison to similar approaches, the performance of the models was higher, and different genes using feature importance analysis were identified. The most promising identified genes that could be used as biomarkers are TMEFF2, ACSM3, SLC4A1, and ALDH4A1.

Publisher

MDPI AG

Subject

Computational Mathematics,Computational Theory and Mathematics,Numerical Analysis,Theoretical Computer Science

Reference76 articles.

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2. (2023, March 28). Surgery for Recurrent Ovarian Cancer May Help Selected Patients-NCI, Available online: https://www.cancer.gov/news-events/cancer-currents-blog/2022/ovarian-cancer-return-surgery-desktop-iii.

3. Ovarian Cancer Recurrence: Is the Definition of Platinum Resistance Modified by PARPi and Other Intervening Treatments? The Evolving Landscape in the Management of Platinum-Resistant Ovarian Cancer;Flynn;Cancer Drug Resist.,2022

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5. (2023, April 26). How to Check for Ovarian Cancer|Ovarian Cancer Screening. Available online: https://www.cancer.org/cancer/ovarian-cancer/detection-diagnosis-staging/detection.html.

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