Discovery of Conditionally Independent Networks Among Gene Expressions in Breast Cancer Using Fast Step Graph
Resumo
A heterogeneidade das causas do câncer de mama e as complexas interações gênicas que caracterizam essa neoplasia apresentam desafios significativos para a compreensão e o tratamento desta doença. Este estudo é motivado pela necessidade de identificar redes de genes interligados do cancer de mama, especificamente aquelas que representam relações de independência condicional. Para criar essas redes, propomos o uso do algoritmo Fast Step Graph, que pertence à família de Modelos Gráficos Gaussianos esparsos e de alta dimensionalidade, aplicado à base de dados de expressão gênica PAM50. Esta base de dados foi estratificada de acordo com os receptores de estrogênio e progesterona, elementos cruciais para o prognóstico e a terapia personalizada. A aplicação do algoritmo resultou na obtenção de quatro grafos que destacam as relações entre os genes envolvidos no câncer de mama. Esses achados apoiam a hipótese de que existem sub-redes gênicas específicas e contribuem para uma compreensão mais profunda das interações gênicas deste câncer, podendo oferecer novos insights para pesquisas futuras e novas estratégias terapêuticas.
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Razera, A., Rodrigo Santos, J., Gonçalves, L., Marques, L., Chao, B., Campos, D., and Carraro, E. (2023). Mybl2 gene as prognostic biomarker in breast cancer: A systematic review. Journal of Advances in Medicine and Medical Research, 35:101–107.
Song, S., Tian, B., Zhang, M., Gao, X., Jie, L., Liu, P., and Li, J. (2021). Diagnostic and prognostic value of thymidylate synthase expression in breast cancer. Clinical and Experimental Pharmacology and Physiology, 48(2):279–287.
Tsai, K., Koyejo, O., and Kolar, M. (2022). Joint gaussian graphical model estimation: A survey. Wiley Interdisciplinary Reviews: Computational Statistics, 14(6):e1582.
Valero, V. and Álvarez, R. H. (2013). Biologia molecular do câncer de mama. In Tratado de Oncologia, pages 2027–2052. Atheneu.
Yuan, M. and Lin, Y. (2007). Model selection and estimation in the gaussian graphical model. Biometrika, 94(1):19–35.
Zamar, R., Ruiz, M., Lafit, G., and Nogales, J. (2021). A stepwise approach for high-dimensional gaussian graphical models. Journal of Data Science, Statistics, and Visualisation, 1(2).
Bièche, I., Vacher, S., Lallemand, F., Tozlu-Kara, S., Bennani, H., Beuzelin, M., Driouch, K., Rouleau, E., Lerebours, F., Ripoche, H., Clairac, G., Spyratos, F., and Lidereau, R. (2011). Expression analysis of mitotic spindle checkpoint genes in breast carcinoma: Role of ndc80/hec1 in early breast tumorigenicity, and a two-gene signature for aneuploidy. Molecular cancer, 10:23.
Cai, T., Liu, W., and Luo, X. (2011). A constrained l1 minimization approach to sparse precision matrix estimation. Journal of the American Statistical Association, 106(494):594–607.
Dastsooz, H., Cereda, M., Donna, D., and Oliviero, S. (2019). A comprehensive bioinformatics analysis of ube2c in cancers. International Journal of Molecular Sciences, 20:2228.
Dawid, A. P. (1979). Conditional independence in statistical theory. Journal of the Royal Statistical Society Series B: Statistical Methodology, 41(1):1–15.
Friedman, J., Hastie, T., and Tibshirani, R. (2008). Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 9(3):432–441.
Hastie, T., Tibshirani, R., and Wainwright, M. (2015). Statistical learning with sparsity: the lasso and generalizations. CRC press.
Instituto Nacional de Câncer (2022). Dados e números sobre câncer de mama - relatório anual 2022. Relatório anual, Coordenação de Prevenção e Vigilância, Divisão de Detecção Precoce e Apoio à Organização de Rede, Rio de Janeiro, Brasil. Acesse: www.inca.gov.br/mama.
Koleck, T. and Conley, Y. (2016). Identification and prioritization of candidate genes for symptom variability in breast cancer survivors based on disease characteristics at the cellular level. Breast Cancer: Targets and Therapy, 8:29.
Koller, D. and Friedman, N. (2009). Probabilistic graphical models: Principles and techniques. MIT press.
Langfelder, P. and Horvath, S. (2008). Wgcna: an r package for weighted correlation network analysis. BMC Bioinformatics, 9(1):559.
Lauritzen, S. L. (1996). Graphical models, volume 17. Clarendon Press.
Li, J., Xu, X., and Peng, X. (2022). Ndc80 enhances cisplatin-resistance in triple-negative breast cancer. Archives of Medical Research, 53(4):378–387.
Liang, F. and Jia, B. (2023). Sparse Graphical Modeling for High Dimensional Data: A Paradigm of Conditional Independence Tests. CRC Press.
Liu, M. C., Pitcher, B. N., Mardis, E. R., Davies, S. R., Friedman, P. N., Snider, J. E., Vickery, T. L., Reed, J. P., DeSchryver, K., Singh, B., et al. (2016). Pam50 gene signatures and breast cancer prognosis with adjuvant anthracycline-and taxane-based chemotherapy: correlative analysis of c9741 (alliance). NPJ breast cancer, 2(1):1–8.
Long, J., Zhu, B., Tian, T., Ren, L., Tao, Y., Zhu, H., Li, D., and Xu, Y. (2023). Activation of ubec2 by transcription factor mybl2 affects dna damage and promotes gastric cancer progression and cisplatin resistance. Open Medicine, 18(1).
Maldonado, J. and Ruiz, S. (2022). Assessment of covariance selection methods in high-dimensional gaussian graphical models. Trends in Computational and Applied Mathematics, 23:583–593.
Mendonca-Neto, R., Reis, J., Okimoto, L., Fenyö, D., Silva, C., Nakamura, F., and Nakamura, E. (2022). Classification of breast cancer subtypes: A study based on representative genes. Journal of the Brazilian Computer Society, 28(1):59–68.
National Cancer Institute (2023). Clinical proteomic tumor analysis consortium (cptac). Technical report, U.S. Department of Health and Human Services. Acesse: [link].
Okimoto, L. Y. S., Mendonca-Neto, R., Nakamura, F. G., Nakamura, E. F., Fenyö, D., and Silva, C. T. (2024). Few-shot genes selection: subset of pam50 genes for breast cancer subtypes classifcation. BMC Bioinformatics, 25:92.
Parker, J. S., Mullins, M., Cheang, M. C., Leung, S., Voduc, D., Vickery, T., Davies, S., Fauron, C., He, X., Hu, Z., et al. (2009). Supervised risk predictor of breast cancer based on intrinsic subtypes. Journal of clinical oncology, 27(8):1160.
Polyak, K. (2011). Heterogeneity in breast cancer. The Journal of Clinical Investigation, 121(10):3786–3788.
Priedigkeit, N., Hartmaier, R. J., Chen, Y., Vareslija, D., Basudan, A., Watters, R. J., Thomas, R., Leone, J. P., Lucas, P. C., Bhargava, R., Hamilton, R. L., Chmielecki, J., Puhalla, S. L., Davidson, N. E., Oesterreich, S., Brufsky, A. M., Young, L., and Lee, A. V. (2017). Intrinsic subtype switching and acquired erbb2/her2 amplifications and mutations in breast cancer brain metastases. JAMA Oncology, 3(5):666–671.
Razera, A., Rodrigo Santos, J., Gonçalves, L., Marques, L., Chao, B., Campos, D., and Carraro, E. (2023). Mybl2 gene as prognostic biomarker in breast cancer: A systematic review. Journal of Advances in Medicine and Medical Research, 35:101–107.
Song, S., Tian, B., Zhang, M., Gao, X., Jie, L., Liu, P., and Li, J. (2021). Diagnostic and prognostic value of thymidylate synthase expression in breast cancer. Clinical and Experimental Pharmacology and Physiology, 48(2):279–287.
Tsai, K., Koyejo, O., and Kolar, M. (2022). Joint gaussian graphical model estimation: A survey. Wiley Interdisciplinary Reviews: Computational Statistics, 14(6):e1582.
Valero, V. and Álvarez, R. H. (2013). Biologia molecular do câncer de mama. In Tratado de Oncologia, pages 2027–2052. Atheneu.
Yuan, M. and Lin, Y. (2007). Model selection and estimation in the gaussian graphical model. Biometrika, 94(1):19–35.
Zamar, R., Ruiz, M., Lafit, G., and Nogales, J. (2021). A stepwise approach for high-dimensional gaussian graphical models. Journal of Data Science, Statistics, and Visualisation, 1(2).
Publicado
09/06/2025
Como Citar
RIVERA, Grecia C. G.; COLONNA, Juan G.; RUIZ, Marcelo.
Discovery of Conditionally Independent Networks Among Gene Expressions in Breast Cancer Using Fast Step Graph. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 25. , 2025, Porto Alegre/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 509-520.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2025.7499.
