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BCSNP-ML: A Novel Breast Cancer Prediction Model Base on LightGBM and Estrogen Metabolic Enzyme Genes

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Proceedings of the 2nd International Conference on Internet of Things, Communication and Intelligent Technology (IoTCIT 2023)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1197))

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Abstract

Estrogen-related metabolic enzyme gene polymorphisms have been demonstrated to be linked to breast cancer, and in this paper, a novel noninvasive breast cancer prediction model was developed utilizing machine learning algorithms incorporating estrogen metabolic enzyme gene single nucleotide polymorphisms (SNPs). To precisely forecast the susceptibility to breast cancer,, the coded data of 14 SNPs from enrolled breast patients and normal women were randomly shuffled, with 80% of the data designated as training data, the remaining 20% reserved as the test group to be validated. Single factor analysis was performed to screen independent risk factors, and subsequent application of Breast Cancer with Single Nucleotide Polymorphisms - Machine Learning model (BCSNP-ML) prediction model was completed using Light Gradient Boosting Machine (LightGBM) algorithm. A total of 14 SNPs variables from 280 subjects were utilized in this study. Single factor analysis indicated that a meaningful association between SULT1A1 rs1042028, CYP1A1 rs1048943, CYP1B1 rs1056827, CYP1A1 rs1056836 and the incidence of breast cancer, with 14 variables demonstrates a notable area under the receiver operating characteristic curve (AUROC) of 0.809. The AUROC of the BCSNP-ML model constructed by four variables was 0.831. Additionally, BCSNP-ML is visualized and interpretated in the paper using SHapley Additive exPlanations analysis to further validate that the model exhibits great potential as a robust tool for clinical forecasting of breast cancer.

T. Zheng and S. Geng—The authors contributed equally to this work.

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Acknowledgments

This research was funded by the Opening Project of Jiangsu Key Laboratory of Xuzhou Medical University (XZSYSKF2021030), the Affiliated Hospital of Xuzhou Medical University Faculty Research Project (2022ZL26), the Science and Technology Plan Social Development Key Project of Xuzhou (KC21172) and the National Natural Science Foundation of China (81402765). The authors thank Zhao Feng from the Jiangsu College of Nursing for providing support and guidance during the experiment. The authors would also like to thank Professor Zhang Xiaoqiang from the China University of Mining and Technology and Professor Gong Ping from the Xuzhou Medical University for their detailed revisions and suggestions on our paper.

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Authors and Affiliations

  1. School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China

    Tianlei Zheng & Deqiang Cheng

  2. Health Management Center, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China

    Tianlei Zheng, Shi Geng, Na Yang & Lei Zhao

  3. Pharmacy Department, Xuzhou Rehabilitation Hospital of Xuzhou Medical University, Xuzhou, China

    Wei Yan

  4. Department of Pediatrics, Affiliated Hospital of Xuzhou Medical University, Xuzhou, China

    Fengjun Guan

  5. Department of Obstetrics and Gynecology, Xuzhou Central Hospital, Xuzhou Clinical School of Xuzhou Medical University, Xuzhou, China

    Bei Zhang

  6. Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy, College of Pharmacy, Xuzhou Medical University, Xuzhou, China

    Xueyan Zhou

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  1. Tianlei Zheng
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  2. Shi Geng
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Correspondence to Deqiang Cheng .

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Editors and Affiliations

  1. Central South University, Changsha, Hunan, China

    Jian Dong

  2. School of Information Engineering, Shenzhen University, Shenzhen, Guangdong, China

    Long Zhang

  3. School of Information and Control Engineering, China University of Mining and Technolog, Xuzhou, Jiangsu, China

    Deqiang Cheng

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Zheng, T. et al. (2024). BCSNP-ML: A Novel Breast Cancer Prediction Model Base on LightGBM and Estrogen Metabolic Enzyme Genes. In: Dong, J., Zhang, L., Cheng, D. (eds) Proceedings of the 2nd International Conference on Internet of Things, Communication and Intelligent Technology. IoTCIT 2023. Lecture Notes in Electrical Engineering, vol 1197. Springer, Singapore. https://doi.org/10.1007/978-981-97-2757-5_66

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  • DOI: https://doi.org/10.1007/978-981-97-2757-5_66

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-2756-8

  • Online ISBN: 978-981-97-2757-5

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