Abstract
An increasing number of studies have shown a close link between the development of breast cancer (BRCA) and molecular signatures. Currently, certain of them have been identified and confirmed as biomarkers for the early diagnosis and prognosis evaluation of BRCA. Nevertheless, identifying biomarkers with high sensitivity and specificity remains exceedingly challenging. In this paper, we aim to identify BRCA biomarkers from high-throughput data by proposing a multi-objective optimization method. Our method involves constructing differential gene regulatory networks based on gene expression profiles of various phenotypes. We extract all pathways from BRCA elite genes to differentially expressed genes to capture the information flow between key genes. In addition, we have constructed a set of virtual nodes and edges that represent the differentially expressed genes reaching the virtual nodes. This enables us to simulate the genetic information transmission process. Using the maximum flow minimum cut theorem, we extract the dysfunctional modules within the identified causal pathways. Ultimately, we derive a globally optimal solution with diversity based on a multi-objective optimization algorithm, which represents a potential biomarker set for BRCA diagnosis. The experimental results validate that the proposed disease diagnosis model is more accurate than previous methods. It is expected to effectively reduce the cost of our clinical trials and be beneficial in identifying therapeutic targets for BRCA.
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References
Waks, A.G., Winer, E.P.: Breast cancer treatment: a review. JAMA 321, 288–300 (2019)
Alsheikhy, A.A., Said, Y., Shawly, T., Alzahrani, A.K. Lahza, H.: Biomedical diagnosis of breast cancer using deep learning and multiple classifiers. Diagnostics 12 (2022)
Milosevic, M., Jankovic, D., Milenkovic, A., Stojanov, D.: Early diagnosis and detection of breast cancer. Technol. Health Care 26, 729–759 (2018)
Strimbu, K., Tavel, J.A.: What are biomarkers? Curr Opin HIV AIDS 5, 463–466 (2010)
Rehman, O., Zhuang, H., Muhamed Ali, A., Ibrahim, A. & Li, Z.: Validation of miRNAs as Breast Cancer Biomarkers with a Machine Learning Approach. Cancers 11, (2019)
Kong, Y., Yu, T.: A graph-embedded deep feedforward network for disease outcome classification and feature selection using gene expression data. Bioinformatics 34, 3727–3737 (2018)
Cai, Z., et al.: Classification of lung cancer using ensemble-based feature selection and machine learning methods. Mol. BioSyst. 11, 791–800 (2015)
Wang, Y., Liu, Z.-P.: Identifying biomarkers for breast cancer by gene regulatory network rewiring. BMC Bioinformatics 22, 1–15 (2022)
Liu, Z.-P., Wu, C., Miao, H., Wu, H.: RegNetwork: an integrated database of transcriptional and post-transcriptional regulatory networks in human and mouse. Database 2015, bav095 (2015)
Li, L., Liu, Z.-P.: Detecting prognostic biomarkers of breast cancer by regularized Cox proportional hazards models. J. Transl. Med. 19, 514 (2021)
Aghdam, R., Ganjali, M., Eslahchi, C.: IPCA-CMI: an algorithm for inferring gene regulatory networks based on a combination of PCA-CMI and MIT score. PLoS ONE 9, e92600 (2014)
Karlebach, G., Shamir, R.: Modelling and analysis of gene regulatory networks. Nat. Rev. Mol. Cell Biol. 9, 770–780 (2008)
Zhang, X., et al.: Inferring gene regulatory networks from gene expression data by path consistency algorithm based on conditional mutual information. Bioinformatics 28, 98–104 (2012)
Tai, X.-C., Deng, L.-J., Yin, K.: A multigrid algorithm for maxflow and min-cut problems with applications to multiphase image segmentation. J. Sci. Comput. 87(3), 1–22 (2021). https://doi.org/10.1007/s10915-021-01458-3
Yuan, J., Bae, E., Tai, X.-C.: A study on continuous max-flow and min-cut approaches. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2217–2224. IEEE (2010)
Deb, K.: Multi-objective optimisation using evolutionary algorithms: an introduction. In: Multi-objective Evolutionary Optimisation for Product Design and Manufacturing, pp. 3–34 (2011)
Srinivas, N., Deb, K.: Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evol. Comput. 2, 221–248 (1994)
Shang, H., Liu, Z.-P.: Network-based prioritization of cancer biomarkers by phenotype-driven module detection and ranking. Comput. Struct. Biotechnol. J. 20, 206–217 (2022)
Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Mach. Learn. 46, 389–422 (2002)
Huang, H.-H., Liu, X.-Y., Liang, Y.: Feature selection and cancer classification via sparse logistic regression with the hybrid L1/2+ 2 regularization. PLoS ONE 11, e0149675 (2016)
Acknowledgments
This work was partially supported by National Natural Science Foundation of China (No. 61973190); National Key Research and Development Program of China (Nos. 2022YFA1004801, 2020YFA0712402); the Fundamental Research Funds for the Central Universities (No. 2022JC008) and the program of Qilu Young Scholar of Shandong University.
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Yang, J., Wang, C., Sun, D., Liu, ZP. (2023). Multi-objective Optimization-Based Approach for Detection of Breast Cancer Biomarkers. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science, vol 14088. Springer, Singapore. https://doi.org/10.1007/978-981-99-4749-2_61
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DOI: https://doi.org/10.1007/978-981-99-4749-2_61
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