Uncovering hidden genetic risk factors for breast and ovarian cancers in BRCA-negative women: a machine learning approach in the Saudi population

Nofe Alganmi; Arwa Bashanfar; Reem Alotaibi; Haneen Banjar; Sajjad Karim; Zeenat Mirza; Heba Abusamra; Manal Al-Attas; Shereen Turkistany; Adel Abuzenadah

doi:10.7717/peerj-cs.1942

Uncovering hidden genetic risk factors for breast and ovarian cancers in BRCA-negative women: a machine learning approach in the Saudi population

PeerJ Comput Sci. 2024 Apr 19:10:e1942. doi: 10.7717/peerj-cs.1942. eCollection 2024.

Authors

Nofe Alganmi^{1

2

3}, Arwa Bashanfar⁴, Reem Alotaibi⁴, Haneen Banjar^{1

2

3}, Sajjad Karim^{2

5}, Zeenat Mirza^{5

6}, Heba Abusamra², Manal Al-Attas², Shereen Turkistany⁷, Adel Abuzenadah^{2

5}

Affiliations

¹ Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.
² Center of Excellence in Genomic Medicine Research, King Abdulaziz University, Jeddah, Saudi Arabia.
³ Centre of Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah, Saudi Arabia.
⁴ Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.
⁵ Department of Medical Lab Technology, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia.
⁶ King Fahd Medical Research Center, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia.
⁷ Center of Innovation Personalized Medicine, King Abdulaziz University, Jeddah, Saudi Arabia.

Abstract

Breast and ovarian cancers are prevalent worldwide, with genetic factors such as BRCA1 and BRCA2 mutations playing a significant role. However, not all patients carry these mutations, making it challenging to identify risk factors. Researchers have turned to whole exome sequencing (WES) as a tool to identify genetic risk factors in BRCA-negative women. WES allows the sequencing of all protein-coding regions of an individual's genome, providing a comprehensive analysis that surpasses traditional gene-by-gene sequencing methods. This technology offers efficiency, cost-effectiveness and the potential to identify new genetic variants contributing to the susceptibility to the diseases. Interpreting WES data for disease-causing variants is challenging due to its complex nature. Machine learning techniques can uncover hidden genetic-variant patterns associated with cancer susceptibility. In this study, we used the extreme gradient boosting (XGBoost) and random forest (RF) algorithms to identify BRCA-related cancer high-risk genes specifically in the Saudi population. The experimental results exposed that the RF method scored superior performance with an accuracy of 88.16% and an area under the receiver-operator characteristic curve of 0.95. Using bioinformatics analysis tools, we explored the top features of the high-accuracy machine learning model that we built to enhance our knowledge of genetic interactions and find complex genetic patterns connected to the development of BRCA-related cancers. We were able to identify the significance of HLA gene variations in these WES datasets for BRCA-related patients. We find that immune response mechanisms play a major role in the development of BRCA-related cancer. It specifically highlights genes associated with antigen processing and presentation, such as HLA-B, HLA-A and HLA-DRB1 and their possible effects on tumour progression and immune evasion. In summary, by utilizing machine learning approaches, we have the potential to aid in the development of precision medicine approaches for early detection and personalized treatment strategies.

Keywords: BRCA; Bioinformatics; Breast cancer; Genetic risk factor; Machine learning; Ovarian cancer; WES.

Grants and funding

This work was supported by the Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia through the project number IFPNC-008-141-2020 and King Abdulaziz University, DSR, Jeddah, Saudi Arabia. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.