Towards an automated pipeline to model a complex-network-driven analysis of microRNAs in cancer: a TCGA-BRCA case study
Resumo
We present the Transparent Reproducible Pipeline (TRP), a core component of our framework for systematizing the comparative analysis of cancer microRNA networks. The TRP is an open, stepwise pipeline for modeling these networks. It provides transparency in materializing intermediary artifacts as tables with schemas, affording explicit semantics and annotation-based provenance. It also offers reproducibility through its open-source code and comprehensive documentation, all accessible without restrictions. To apply and validate the TRP, we conducted a controlled study on breast cancer based on the Breast Invasive Carcinoma (BRCA) project of The Cancer Genome Atlas (TCGA), achieving promising results.
Palavras-chave:
Breast Cancer, MicroRNAs, Network Medicine, Network Science, TCGA-BRCA
Referências
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Hornberg, J. J., Bruggeman, F. J., Westerhoff, H. V., and Lankelma, J. (2006). Cancer: A systems biology disease. Biosystems, 83:81–90.
Jacobsen, A., Silber, J., Harinath, G., Huse, J. T., Schultz, N., and Sander, C. (2013). Analysis of microrna-target interactions across diverse cancer types. Nature Structural & Molecular Biology, 20:1325–1332.
Laubenbacher, R., Hower, V., Jarrah, A., Torti, S. V., Shulaev, V., Mendes, P., Torti, F. M., and Akman, S. (2009). A systems biology view of cancer. Biochimica et Biophysica Acta (BBA) - Reviews on Cancer, 1796:129–139.
Lee, J., Kim, H. E., Song, Y.-S., Cho, E. Y., and Lee, A. (2019). mir-106b-5p and mir-17-5p could predict recurrence and progression in breast ductal carcinoma in situ based on the transforming growth factor-beta pathway. Breast cancer research and treatment, 176:119–130.
Li, N., Miao, Y., Shan, Y., Liu, B., Li, Y., Zhao, L., and Jia, L. (2017). Mir-106b and mir-93 regulate cell progression by suppression of pten via pi3k/akt pathway in breast cancer. Cell death & disease, 8:e2796.
Malhotra, G. K., Zhao, X., Band, H., and Band, V. (2010). Histological, molecular and functional subtypes of breast cancers. Cancer Biology & Therapy, 10:955–960.
Na, Y.-J. and Kim, J. H. (2013). Understanding cooperativity of micrornas via microrna association networks. BMC Genomics, 14:S17.
Network, C. G. A. (2012). Comprehensive molecular portraits of human breast tumours. Nature, 490:61–70.
Network, C. G. A. R., Weinstein, J. N., Collisson, E. A., Mills, G. B., Shaw, K. R. M., Ozenberger, B. A., Ellrott, K., Shmulevich, I., Sander, C., and Stuart, J. M. (2013). The cancer genome atlas pan-cancer analysis project. Nature genetics, 45:1113–20.
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., and Ideker, T. (2003). Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Research, 13:2498–2504.
Sticht, C., Torre, C. D. L., Parveen, A., and Gretz, N. (2018). mirwalk: An online resource for prediction of microrna binding sites. PLOS ONE, 13:e0206239.
Sung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., and 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, 71:209–249.
Vulliard, L. and Menche, J. (2021). Complex Networks in Health and Disease, volume 1-3, pages 26–33. Elsevier.
Xu, K., Zhang, P., Zhang, J., Quan, H., Wang, J., and Liang, Y. (2021). Identification of potential micro-messenger rnas (mirna–mrna) interaction network of osteosarcoma. Bioengineered, 12:3275–3293.
Barabási, A.-L., Gulbahce, N., and Loscalzo, J. (2011). Network medicine: a network-based approach to human disease. Nature reviews. Genetics, 12:56–68.
Chen, Y., Chen, L., Lun, A. L., Baldoni, P., and Smyth, G. (2025). edger v4: powerful differential analysis of sequencing data with expanded functionality and improved support for small counts and larger datasets. Nucleic Acids Research, 53:13–14.
da F. Costa, L., Rodrigues, F. A., Travieso, G., and Boas, P. R. V. (2007). Characterization of complex networks: A survey of measurements. Advances in Physics, 56:167–242.
Dragomir, M., Mafra, A. C. P., Dias, S. M. G., Vasilescu, C., and Calin, G. A. (2018). Using microrna networks to understand cancer. International Journal of Molecular Sciences, 19:1871.
Fu, J., Tang, W., Du, P., Wang, G., Chen, W., Li, J., Zhu, Y., Gao, J., and Cui, L. (2012). Identifying microrna-mrna regulatory network in colorectal cancer by a combination of expression profile and bioinformatics analysis. BMC Systems Biology, 6:68.
Gysi, D. M. and Barabási, A.-L. (2023). Noncoding rnas improve the predictive power of network medicine. Proceedings of the National Academy of Sciences of the United States of America, 120:e2301342120.
Hayes, J., Peruzzi, P. P., and Lawler, S. (2014). Micrornas in cancer: biomarkers, functions and therapy. Trends in molecular medicine, 20:460–9.
Hornberg, J. J., Bruggeman, F. J., Westerhoff, H. V., and Lankelma, J. (2006). Cancer: A systems biology disease. Biosystems, 83:81–90.
Jacobsen, A., Silber, J., Harinath, G., Huse, J. T., Schultz, N., and Sander, C. (2013). Analysis of microrna-target interactions across diverse cancer types. Nature Structural & Molecular Biology, 20:1325–1332.
Laubenbacher, R., Hower, V., Jarrah, A., Torti, S. V., Shulaev, V., Mendes, P., Torti, F. M., and Akman, S. (2009). A systems biology view of cancer. Biochimica et Biophysica Acta (BBA) - Reviews on Cancer, 1796:129–139.
Lee, J., Kim, H. E., Song, Y.-S., Cho, E. Y., and Lee, A. (2019). mir-106b-5p and mir-17-5p could predict recurrence and progression in breast ductal carcinoma in situ based on the transforming growth factor-beta pathway. Breast cancer research and treatment, 176:119–130.
Li, N., Miao, Y., Shan, Y., Liu, B., Li, Y., Zhao, L., and Jia, L. (2017). Mir-106b and mir-93 regulate cell progression by suppression of pten via pi3k/akt pathway in breast cancer. Cell death & disease, 8:e2796.
Malhotra, G. K., Zhao, X., Band, H., and Band, V. (2010). Histological, molecular and functional subtypes of breast cancers. Cancer Biology & Therapy, 10:955–960.
Na, Y.-J. and Kim, J. H. (2013). Understanding cooperativity of micrornas via microrna association networks. BMC Genomics, 14:S17.
Network, C. G. A. (2012). Comprehensive molecular portraits of human breast tumours. Nature, 490:61–70.
Network, C. G. A. R., Weinstein, J. N., Collisson, E. A., Mills, G. B., Shaw, K. R. M., Ozenberger, B. A., Ellrott, K., Shmulevich, I., Sander, C., and Stuart, J. M. (2013). The cancer genome atlas pan-cancer analysis project. Nature genetics, 45:1113–20.
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., and Ideker, T. (2003). Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Research, 13:2498–2504.
Sticht, C., Torre, C. D. L., Parveen, A., and Gretz, N. (2018). mirwalk: An online resource for prediction of microrna binding sites. PLOS ONE, 13:e0206239.
Sung, H., Ferlay, J., Siegel, R. L., Laversanne, M., Soerjomataram, I., Jemal, A., and 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, 71:209–249.
Vulliard, L. and Menche, J. (2021). Complex Networks in Health and Disease, volume 1-3, pages 26–33. Elsevier.
Xu, K., Zhang, P., Zhang, J., Quan, H., Wang, J., and Liang, Y. (2021). Identification of potential micro-messenger rnas (mirna–mrna) interaction network of osteosarcoma. Bioengineered, 12:3275–3293.
Publicado
29/09/2025
Como Citar
ROBERTA, Mylena; GERALDO, Murilo Vieira; SANTANCHÈ, André.
Towards an automated pipeline to model a complex-network-driven analysis of microRNAs in cancer: a TCGA-BRCA case study. In: SIMPÓSIO BRASILEIRO DE BIOINFORMÁTICA (BSB), 18. , 2025, Fortaleza/CE.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 186-197.
ISSN 2316-1248.
DOI: https://doi.org/10.5753/bsb.2025.15126.
