Matthews, H.K., Bertoli, C., and de Bruin, R.A., Cell cycle control in cancer, Nat. Rev. Mol. Cell Biol., 2022, vol. 23, no. 1, pp. 74–88. https://doi.org/10.1038/s41580-021-00404-3

Article  CAS  PubMed  Google Scholar 

Bray, F., Ferlay, J., Soerjomataram, I., et al., Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries, CA: Cancer J. Clin., 2018, vol. 68, no. 6, pp. 394–424. https://doi.org/10.3322/caac.21492

Article  PubMed  Google Scholar 

Sung, H., Ferlay, J., Siegel, R.L., et al., Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries, CA: Cancer J. Clin., 2021, vol. 71, no. 3, pp. 209–249. https://doi.org/10.3322/caac.21660

Article  CAS  PubMed  Google Scholar 

Anderson, B.O., Ilbawi, A.M., Fidarova, E., et al., The Global Breast Cancer Initiative: a strategic collaboration to strengthen health care for non-communicable diseases, Lancet Oncol., 2021, vol. 22, no. 5, pp. 578–581. https://doi.org/10.1016/S1470-2045(21)00071-1

Article  PubMed  Google Scholar 

Boersma, B.J., Reimers, M., Yi, M., et al., A stromal gene signature associated with inflammatory breast cancer, Int. J. Cancer, 2008, vol. 122, no. 6, pp. 1324–1332. https://doi.org/10.1002/ijc.23237

Article  CAS  PubMed  Google Scholar 

Boussen, H., Bouzaiene, H., Ben Hassouna, J., et al., Inflammatory breast cancer in Tunisia: epidemiological and clinical trends, Cancer, 2010, vol. 116, no. 11, pp. 2730–2725. https://doi.org/10.1002/cncr.25175

Article  PubMed  Google Scholar 

Ueno, N.T., Fernandez, J.R.E., Cristofanilli, M., et al., International consensus on the clinical management of inflammatory breast cancer from the Morgan Welch Inflammatory Breast Cancer Research Program 10th Anniversary Conference, J. Cancer, 2018, vol. 9, no. 8, p. 1413. https://doi.org/10.7150/jca.23969

Article  Google Scholar 

Bertucci, F., Houlgatte, R., Nguyen, C., et al., Gene expression profiling of cancer by use of DNA arrays: how far from the clinic?, Lancet Oncol., 2001, vol. 2, no. 11, pp. 674–682. https://doi.org/10.1093/hmg/9.20.2981

Article  CAS  PubMed  Google Scholar 

Alpaugh, M.L., Tomlinson, J.S., Ye, Y., and Barsky, S.H., Relationship of sialyl-Lewis(x/a) underexpression and E-cadherin overexpression in the lymphovascular embolus of inflammatory breast carcinoma, Am. J. Pathol., 2002, vol. 161, no. 2, pp. 619–628. https://doi.org/10.1016/S0002-9440(10)64217-4

Article  CAS  PubMed  PubMed Central  Google Scholar 

Silvera, D., Arju, R., Darvishian, F., et al., Essential role for eIF4GI overexpression in the pathogenesis of inflammatory breast cancer, Nat. Cell Biol., 2009, vol. 11, no. 7, pp. 903–908. https://doi.org/10.1038/ncb1900

Article  CAS  PubMed  Google Scholar 

van der Auwera, I., van Laere, S.J., van den Eynden, G.G., et al., Increased angiogenesis and lymphangiogenesis in inflammatory versus noninflammatory breast cancer by real-time reverse transcriptase-PCR gene expression quantification, Clin. Cancer Res., 2004, vol. 10, no. 23, pp. 7965–7971. https://doi.org/10.1158/1078-0432.CCR-04-0063

Article  CAS  PubMed  Google Scholar 

Polley, M.YC., Freidlin, B., Korn, E.L., et al., Statistical and practical considerations for clinical evaluation of predictive biomarkers, J. Natl. Cancer Inst., 2013, vol. 105, no. 22, pp. 1677–1683. https://doi.org/10.1093/jnci/djt282

Article  PubMed  PubMed Central  Google Scholar 

Wang, X., Semba, T., Phi, L.T.H., et al., Targeting signaling pathways in inflammatory breast cancer, Cancers, 2020, vol. 12, no. 9, p. 2479. https://doi.org/10.3390/cancers12092479

Article  CAS  PubMed  PubMed Central  Google Scholar 

Erickson, B.J., Korfiatis, P., Akkus, Z., and Kline, T.L., Machine learning for medical imaging, Radiographics, 2017, vol. 37, no. 2, pp. 505–515. https://doi.org/10.1148/rg.2017160130

Article  PubMed  Google Scholar 

Kourou, K., Exarchos, T.P., Exarchos, K.P., et al., Machine learning applications in cancer prognosis and prediction, Comput. Struct. Biotechnol. J., 2015, vol. 13, no. 1, pp. 8–17. https://doi.org/10.1016/j.csbj.2014.11.005

Article  CAS  PubMed  Google Scholar 

Hastie, T., Tibshirani, R., Friedman, J.H., and Friedman, J.H., The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer-Verlag, 2009. https://doi.org/10.1007/978-0-387-84858-7

Fallon, B., Ma, J., Allan, K., et al., Opportunities for prevention and intervention with young children: lessons from the Canadian incidence study of reported child abuse and neglect, Child Adolesc. Psychiatry Ment. Health, 2013, vol. 7, no. 1, pp. 1–13. https://doi.org/10.1186/1753-2000-7-4

Article  Google Scholar 

Breiman, L. and Cutler, R., Random forests machine learning, J. Clin. Microbiol., 2001, vol. 2, no. 1, pp. 199–228. https://doi.org/10.1023/A:1010933404324

Article  Google Scholar 

Martin, D.N., Boersma, B.J., Yi, M., et al., Differences in the tumor microenvironment between African-American and European-American breast cancer patients, PLoS One, 2009, vol. 4, no. 2, p. e4531. https://doi.org/10.1371/journal.pone.0004531

Article  CAS  PubMed  PubMed Central  Google Scholar 

Saeys, Y., Inza, I., and Larranaga, P., A review of feature selection techniques in bioinformatics, Bioinformatics, 2007, vol. 23, no. 19, pp. 2507–2517. https://doi.org/10.1093/bioinformatics/btm344

Article  CAS  PubMed  Google Scholar 

Monika Kumar, M. and Kumar, M., XGBoost: 2D-object recognition using shape descriptors and extreme gradient boosting classifier, Comput. Methods Data Eng.: Proc. ICMDE, 2020, vol. 1, no. 1, pp. 207–222. https://doi.org/10.1007/978-981-15-6876-3_16

Powers, D.M., Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation, arXiv, 2020, vol. 1, no. 1, pp. 37–63. https://doi.org/10.48550/arXiv.2010.16061

Bieche, I., Lerebours, F., and Tozlu, S.l., et al., Molecular profiling of inflammatory breast cancer: identification of a poor-prognosis gene expression signature, Clin. Cancer Res., 2004, vol. 10, no. 20, pp. 6789–6795. https://doi.org/10.1158/1078-0432.CCR-04-0306

Article  CAS  PubMed  Google Scholar 

Jansen, M.P.H.M., Sas, L., Sieuwerts, A.M., et al., Decreased expression of ABAT and STC2 hallmarks ER-positive inflammatory breast cancer and endocrine therapy resistance in advanced disease, Mol. Oncol., 2015, vol. 9, no. 6, pp. 1218–1233. https://doi.org/10.1016/j.molonc.2015.02.006

Article  CAS  PubMed  PubMed Central  Google Scholar 

Kolapalli, S.P., Sahu, R., Chauhan, N.R., et al., RNA-binding RING E3-ligase DZIP3/hRUL138 stabilizes cyclin D1 to drive cell-cycle and cancer progression, Cancer Res., 2021, vol. 81, no. 2, pp. 315–331. https://doi.org/10.1158/0008-5472.CAN-20-1871

Article  CAS  PubMed  Google Scholar 

Alifanov, V.V., Tashireva, L.A., Zavyalova, M.V., and Perelmuter, V.M., LIMCH1 as a new potential metastasis predictor in breast cancer, Asian Pac. J. Cancer Prev., 2022, vol. 23, no. 11, pp. 3947–3952. https://doi.org/10.31557/APJCP.2022.23.11.3947

Article  CAS  PubMed  PubMed Central  Google Scholar 

Zhao, J., Liu, J., Wu, N., et al., ANGPTL4 overexpression is associated with progression and poor prognosis in breast cancer, Oncol. Lett., 2020, vol. 20, no. 3, pp. 2499–2505. https://doi.org/10.3892/ol.2020.11768

Article  CAS  PubMed  PubMed Central  Google Scholar 

Zhou, X., Jafari, N., Deng, S., and Huang, C., The function of ubiquitin protein ligase E3A and its roles in human diseases, J. Biochem. Mol. Biol. Res., 2015, vol. 1, no. 1, pp. 14–18. https://doi.org/10.6051/j.issn.2313-7177.2015.01.2

Article  Google Scholar 

Gao, X., Mohsin, S.K., Gatalica, Z., et al., Decreased expression of E6-associated protein in breast and prostate carcinomas, Endocrinology, 2005, vol. 146, no. 4, pp. 1707–1712. https://doi.org/10.1210/en.2004-1198

Article  CAS  PubMed  Google Scholar 

Moslehi, S., Rabiei, N., Soltanian, A.R., and Mamani, M., Application of machine learning models based on decision trees in classifying the factors affecting mortality of COVID-19 patients in Hamadan, Iran, BMC Med. Inf. Dec. Making, 2022, vol. 22, no. 192, pp. 1–12. https://doi.org/10.1186/s12911-022-01939-x

Article  Google Scholar