1
Arnold, M., Morgan, E., Rumgay, H., Mafra, A., Singh, D., Laversanne, M., Vignat, J., Gralow, J. R., Cardoso, F., Siesling, S., & Soerjomataram, I. (2022). Current and future burden of breast cancer: Global statistics for 2020 and 2040. The Breast, 66, 15–23.
2
Sung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., & Bray, F. (2021). Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer Journal for Clinicians, 71(3), 209–249.
3
Gautama, W. (2022). Breast Cancer in Indonesia in 2022: 30 Years of Marching in Place. Indonesian Journal of Cancer, 16(1), 1.
4
Ferlay, J., Colombet, M., Soerjomataram, I., Parkin, D. M., Piñeros, M., Znaor, A., & Bray, F. (2021). Cancer statistics for the year 2020: An overview. International Journal of Cancer, 149(4), 778–789.
5
Widiana, I. K., & Irawan, H. (2020). Clinical and Subtypes of Breast Cancer in Indonesia. Asian Pacific Journal of Cancer Care, 5(4), 281–285.
6
Oluogun, W. A., Adedokun, K. A., Oyenike, M. A., & Adeyeba, O. A. (2019). Histological classification, grading, staging, and prognostic indexing of female breast cancer in an African population: A 10-year retrospective study. International Journal of Health Sciences, 13(4), 3–9.
7
Yang, L., Wang, Y., Cai, H., Wang, S., Shen, Y., & Ke, C. (2020). Application of metabolomics in the diagnosis of breast cancer: a systematic review. Journal of Cancer, 11(9).
8
He, Z., Chen, Z., Tan, M., Elingarami, S., Liu, Y., Li, T., Deng, Y., He, N., Li, S., Fu, J., & Li, W. (2020). A review on methods for diagnosis of breast cancer cells and tissues. Cell Proliferation, 53(7).
9
Lopez-Gonzalez, L., Sanchez Cendra, A., Sanchez Cendra, C., Roberts Cervantes, E. D., Espinosa, J. C., Pekarek, T., Fraile-Martinez, O., GarcÃa-Montero, C., Rodriguez-Slocker, A. M., Jiménez-Ãlvarez, L., Guijarro, L. G., Aguado-Henche, S., Monserrat, J., Alvarez-Mon, M., Pekarek, L., Ortega, M. A., & Diaz-Pedrero, R. (2024). Exploring Biomarkers in Breast Cancer: Hallmarks of Diagnosis, Treatment, and Follow-Up in Clinical Practice. Medicina, 60(1), 168.
10
Wei, Y., Jasbi, P., Shi, X., Turner, C., Hrovat, J., Liu, L., Rabena, Y., Porter, P., & Gu, H. (2021). Early Breast Cancer Detection Using Untargeted and Targeted Metabolomics. Journal of Proteome Research, 20(6), 3124–3133
11
Neves Rebello Alves, L., Dummer Meira, D., Poppe Merigueti, L., Correia Casotti, M., do Prado Ventorim, D., Ferreira Figueiredo Almeida, J., Pereira de Sousa, V., Cindra Sant’Ana, M., Gonçalves Coutinho da Cruz, R., Santos Louro, L., Mendonça Santana, G., Erik Santos Louro, T., Evangelista Salazar, R., Ribeiro Campos da Silva, D., Stefani Siqueira Zetum, A., Silva dos Reis Trabach, R., Imbroisi Valle Errera, F., de Paula, F., de Vargas Wolfgramm dos Santos, E., … Drumond Louro, I. (2023). Biomarkers in Breast Cancer: An Old Story with a New End. Genes, 14(7), 1364.
12
Bhushan, A., Gonsalves, A., & Menon, J. U. (2021). Current State of Breast Cancer Diagnosis, Treatment, and Theranostics. Pharmaceutics, 13(5), 723.
13
Silva, C., Perestrelo, R., Silva, P., Tomás, H., & Câmara, J. S. (2019). Breast Cancer Metabolomics: From Analytical Platforms to Multivariate Data Analysis. A Review. Metabolites, 9(5), 102.
14
Subramani, R., Poudel, S., Smith, K. D., Estrada, A., & Lakshmanaswamy, R. (2022). Metabolomics of Breast Cancer: A Review. Metabolites, 12(7), 643.
15
An, R., Yu, H., Wang, Y., Lu, J., Gao, Y., Xie, X., & Zhang, J. (2022). Integrative analysis of plasma metabolomics and proteomics reveals the metabolic landscape of breast cancer. Cancer & Metabolism, 10(1), 13.
16
Gupta, A., Siddiqui, Z., Sagar, G., Rao, K. V. S., & Saquib, N. (2023). A non-invasive method for concurrent detection of multiple early-stage cancers in women. Scientific Reports, 13(1), 19083.
17
Hanahan, D. (2022). Hallmarks of Cancer: New Dimensions. Cancer Discovery, 12(1), 31–46.
18
Danzi, F., Pacchiana, R., Mafficini, A., Scupoli, M. T., Scarpa, A., Donadelli, M., & Fiore, A. (2023). To metabolomics and beyond: a technological portfolio to investigate cancer metabolism. Signal Transduction and Targeted Therapy, 8(1), 137.
19
Bel’skaya, L. V., Gundyrev, I. A., & Solomatin, D. V. (2023). The Role of Amino Acids in the Diagnosis, Risk Assessment, and Treatment of Breast Cancer: A Review. Current Issues in Molecular Biology, 45(9), 7513–7537.
20
Ruan, X., Wang, Y., Zhou, L., Zheng, Q., Hao, H., & He, D. (2022). Evaluation of Untargeted Metabolomic Strategy for the Discovery of Biomarker of Breast Cancer. Frontiers in Pharmacology, 13.
21
DÃaz-Beltrán, L., González-Olmedo, C., Luque-Caro, N., DÃaz, C., MartÃn-Blázquez, A., Fernández-Navarro, M., Ortega-Granados, A. L., Gálvez-Montosa, F., Vicente, F., Pérez del Palacio, J., & Sánchez-Rovira, P. (2021). Human Plasma Metabolomics for Biomarker Discovery: Targeting the Molecular Subtypes in Breast Cancer. Cancers, 13(1), 147.
22
Chen, Z., Li, Z., Li, H., & Jiang, Y. (2019). Metabolomics: A promising diagnostic and therapeutic implement for breast cancer. In OncoTargets and Therapy (Vol. 12, pp. 6797–6811). Dove Medical Press Ltd.
23
Jacob, M., Lopata, A. L., Dasouki, M., & Abdel Rahman, A. M. (2019). Metabolomics toward personalized medicine. Mass Spectrometry Reviews, 38(3), 221–238.
24
Lelli, V., Belardo, A., & Maria Timperio, A. (2021). From Targeted Quantification to Untargeted Metabolomics. In Metabolomics - Methodology and Applications in Medical Sciences and Life Sciences. IntechOpen.
25
Gong, S., Wang, Q., Huang, J., Huang, R., Chen, S., Cheng, X., Liu, L., Dai, X., Zhong, Y., Fan, C., & Liao, Z. (2024). LC-MS/MS platform-based serum untargeted screening reveals the diagnostic biomarker panel and molecular mechanism of breast cancer. Methods, 222, 100–111.
26
Zou, Y., Song, D., Cai, Y., Liang, K., Fu, J., & Zhang, H. (2024). Comprehensive untargeted serum metabolomics identifies biomarkers and metabolic pathways in breast cancer.
27
Shoaib, A. S. M., Nishat, N., Raasetti, M., & Arif, I. (2024). Integrative Machine Learning Approaches For Multi-Omics Data Analysis In Cancer Research. Global Mainstream Journal, 1(2), 26–39.
28
Ngan, H.-L., Lam, K.-Y., Li, Z., Zhang, J., & Cai, Z. (2023). Machine learning facilitates the application of mass spectrometry-based metabolomics to clinical analysis: A review of early diagnosis of high mortality rate cancers. TrAC Trends in Analytical Chemistry, 168.
29
Kuang, A., Kouznetsova, V. L., Kesari, S., & Tsigelny, I. F. (2023). Diagnostics of Thyroid Cancer Using Machine Learning and Metabolomics. Metabolites, 14(1), 11.
30
Sugimoto, M., Hikichi, S., Takada, M., & Toi, M. (2023). Machine learning techniques for breast cancer diagnosis and treatment: a narrative review. Annals of Breast Surgery, 7, 7–7.
31
Albrecht, W. (2019). Highlight report: Role of choline phospholipid metabolism in tumor progression. EXCLI Journal, 18, 1097–1098.
32
Ma, Y., Zhang, S., Jin, Z., & Shi, M. (2020). Lipid-mediated regulation of the cancer-immune crosstalk. Pharmacological Research, 161, 105131.
33
Yoneten, K. K., Kasap, M., Akpinar, G., Gunes, A., Gurel, B., & Utkan, N. Z. (2019). Comparative Proteome Analysis of Breast Cancer Tissues Highlights the Importance of Glycerol-3-phosphate Dehydrogenase 1 and Monoacylglycerol Lipase in Breast Cancer Metabolism. Cancer Genomics - Proteomics, 16(5), 377–397.
34
Lin, Y., Yang, Z., Li, J., Sun, Y., Zhang, X., Qu, Z., Luo, Y., & Zhang, L. (2022). Effects of glutamate and aspartate on prostate cancer and breast cancer: a Mendelian randomization study. BMC Genomics, 23(1), 213.
35
Ullah Khan, S., & Ullah Khan, M. (2022). The Role of Amino Acid Metabolic Reprogramming in Tumor Development and Immunotherapy. Biochemistry and Molecular Biology, 7(1), 6
36
Zhang, J., Chen, M., Yang, Y., Liu, Z., Guo, W., Xiang, P., Zeng, Z., Wang, D., & Xiong, W. (2024). Amino acid metabolic reprogramming in the tumor microenvironment and its implication for cancer therapy. Journal of Cellular Physiology.
37
Sun, W., Zhao, E., & Cui, H. (2023). Target enzymes in serineâ€glycineâ€oneâ€carbon metabolic pathway for cancer therapy. International Journal of Cancer, 152(12), 2446–2463.
38
Lieu, E. L., Nguyen, T., Rhyne, S., & Kim, J. (2020). Amino acids in cancer. Experimental & Molecular Medicine, 52(1), 15–30.
39
Da Cunha, P. A., Nitusca, D., Canto, L. M. Do, Varghese, R. S., Ressom, H. W., Willey, S., Marian, C., & Haddad, B. R. (2022). Metabolomic Analysis of Plasma from Breast Cancer Patients Using Ultra-High-Performance Liquid Chromatography Coupled with Mass Spectrometry: An Untargeted Study. Metabolites, 12(5).
40
Panigoro, S. S., Kurniawan, A., Ramadhan, R., Sukartini, N., Herqutanto, H., Paramita, R. I., & Sandra, F. (2023). Amino Acid Profile of Luminal A and B Subtypes Breast Cancer. The Indonesian Biomedical Journal, 15(3), 269–276.
41
Jaune-Pons, E., & Vasseur, S. (2020). Role of amino acids in regulation of ROS balance in cancer. Archives of Biochemistry and Biophysics, 689, 108438.
42
Beatty, A., Fink, L. S., Singh, T., Strigun, A., Peter, E., Ferrer, C. M., Nicolas, E., Cai, K. Q., Moran, T. P., Reginato, M. J., Rennefahrt, U., & Peterson, J. R. (2023). Data from Metabolite Profiling Reveals the Glutathione Biosynthetic Pathway as a Therapeutic Target in Triple-Negative Breast Cancer.
43
Niepmann, M. (2024). Importance of Michaelis Constants for Cancer Cell Redox Balance and Lactate Secretion—Revisiting the Warburg Effect. Cancers, 16(13), 2290.
44
Barba, I., Carrillo-Bosch, L., & Seoane, J. (2024). Targeting the Warburg Effect in Cancer: Where Do We Stand? International Journal of Molecular Sciences, 25(6), 3142.
45
Zakic, T., Kalezic, A., Drvendzija, Z., Udicki, M., Ivkovic Kapicl, T., Srdic Galic, B., Korac, A., Jankovic, A., & Korac, B. (2024). Breast Cancer: Mitochondria-Centered Metabolic Alterations in Tumor and Associated Adipose Tissue. Cells, 13(2), 155.
46
Zhang, W., & Lang, R. (2023). Succinate metabolism: a promising therapeutic target for inflammation, ischemia/reperfusion injury and cancer. Frontiers in Cell and Developmental Biology, 11.
47
Moreira-Barbosa, C., Matos, A., Fernandes, R., Mendes-Ferreira, M., Rodrigues, R., Cruz, T., Costa, Â. M., Cardoso, A. P., Ghilardi, C., Oliveira, M. J., & Ribeiro, R. (2023). The role of fatty acids metabolism on cancer progression and therapeutics development. In Bioactive Lipids (pp. 101–132).
48
He, M., Xu, S., Yan, F., Ruan, J., & Zhang, X. (2023). Fatty Acid Metabolism: A New Perspective in Breast Cancer Precision Therapy. Frontiers in Bioscience-Landmark, 28(12).
49
Du, A., Wang, Z., Huang, T., Xue, S., Jiang, C., Qiu, G., & Yuan, K. (2023). Fatty acids in cancer: Metabolic functions and potential treatment. MedComm – Oncology, 2(1).
50
Yang, S., Xie, Y., Zhang, T., Deng, L., Liao, L., Hu, S., Zhang, Y., Zhang, F., & Li, D. (2023). Inositol monophosphatase 1 ( IMPA1 ) promotes tripleâ€negative breast cancer progression through regulating mTOR pathway and EMT process. Cancer Medicine, 12(2), 1602–1615.
51
Kleppe, A. (2022). Area under the curve may hide poor generalisation to external datasets. ESMO Open, 7(2), 100429.
52
Parodi, S., Verda, D., Bagnasco, F., & Muselli, M. (2022). The clinical meaning of the area under a receiver operating characteristic curve for the evaluation of the performance of disease markers. Epidemiology and Health, 44, e2022088.
53
Chowdhury, M. Z. I., & Turin, T. C. (2020). Precision health through prediction modelling: factors to consider before implementing a prediction model in clinical practice. Journal of Primary Health Care, 12(1), 3.
54
Monaghan, T. F., Rahman, S. N., Agudelo, C. W., Wein, A. J., Lazar, J. M., Everaert, K., & Dmochowski, R. R. (2021). Foundational Statistical Principles in Medical Research: Sensitivity, Specificity, Positive Predictive Value, and Negative Predictive Value. Medicina, 57(5), 503.
55
Bhuta, N., & Raut, R. (2023). Breast Cancer Classification Using ANN and ML Techniques. 2023 7th International Conference On Computing, Communication, Control And Automation (ICCUBEA), 1–5.
56
Sidey-Gibbons, J. A. M., & Sidey-Gibbons, C. J. (2019). Machine learning in medicine: a practical introduction. BMC Medical Research Methodology, 19(1), 64.
57
Liu, X., Song, C., Huang, F., Fu, H., Xiao, W., & Zhang, W. (2022). GraphCDR: a graph neural network method with contrastive learning for cancer drug response prediction. Briefings in Bioinformatics, 23(1).
58
Sakellaropoulos, T., Vougas, K., Narang, S., Koinis, F., Kotsinas, A., Polyzos, A., Moss, T. J., Piha-Paul, S., Zhou, H., Kardala, E., Damianidou, E., Alexopoulos, L. G., Aifantis, I., Townsend, P. A., Panayiotidis, M. I., Sfikakis, P., Bartek, J., Fitzgerald, R. C., Thanos, D., … Gorgoulis, V. G. (2019). A Deep Learning Framework for Predicting Response to Therapy in Cancer. Cell Reports, 29(11), 3367-3373.e4.
59
Sun, T. (2024). Breast cancer prediction based on multiple machine learning algorithms. Highlights in Science, Engineering and Technology, 92, 241–247.
60
Vaida, M. L., Arumalla, K., Tatikonda, P., Popuri, B., Bux, R., Tappia, P. S., Huang, G., Haince, J.-F., & Ford, W. R. (2024). Identifying Robust Biomarker Panels for Breast Cancer Screening.
61
Wang, J., Ma, F., Sun, X., Wang, J., Guo, F., Liu, B., Wang, W., Li, Q., & Xu, B. (2022). Peripheral lipidomics analyses with ensemble machine learning predict response to neoadjuvant therapy in breast cancer. Journal of Clinical Oncology, 40(16_suppl), 582–582.
62
Md Zahidul Islam, Md Nasiruddin, Shuvo Dutta, Rajesh Sikder, Chowdhury Badrul Huda, & Md Rasibul Islam. (2024). A Comparative Assessment of Machine Learning Algorithms for Detecting and Diagnosing Breast Cancer. Journal of Computer Science and Technology Studies, 6(2), 121–135.
63
Rahman, S., Siregar, D., Syah, R. B. Y., Setiawan, H., Maulana, A. E., & Hamsiah. (2023). The Effective Breast Cancer Classification with the Random Forest Algorithm. 2023 International Conference of Computer Science and Information Technology (ICOSNIKOM), 1–5.
64
Mirza, Z., Ansari, M. S., Iqbal, M. S., Ahmad, N., Alganmi, N., Banjar, H., Al-Qahtani, M. H., & Karim, S. (2023). Identification of Novel Diagnostic and Prognostic Gene Signature Biomarkers for Breast Cancer Using Artificial Intelligence and Machine Learning Assisted Transcriptomics Analysis. Cancers, 15(12), 3237.
