Abstract
Breast invasive carcinoma (BRCA) and prostate adenocarcinoma (PRAD) are two of the most common types of cancer in women and men, respectively. As hormone-dependent tumours, BRCA and PRAD share considerable underlying biological similarities worth being exploited. The disclosure of gene networks regulating both types of cancers would potentially allow the development of common therapies, greatly contributing to disease management and health economics. A methodology based on Bayesian network learning is proposed to unravel breast and prostate common gene signatures. BRCA and PRAD RNA-Seq data from The Cancer Genome Atlas (TCGA) measured over \({\sim }20000\) genes were used. A prior dimensionality reduction step based on sparse logistic regression with elastic net penalisation was employed to select a set of relevant genes and provide more interpretable results. The Bayesian networks obtained were validated against information from STRING, a database containing known gene interactions, showing high concordance.
Supported by the EU Horizon 2020 research and innovation program (grant No. 633974 - SOUND project), and the Portuguese Foundation for Science & Technology (FCT), through projects UID/EMS/50022/2019 (IDMEC, LAETA), UID/EEA/50008/2019 (IT), UID/CEC/50021/2019 (INESC-ID), PERSEIDS (PTDC/EMS-SIS/0642/2014), PREDICT (PTDC/CCI-CIF/29877/2017), NEUROCLINOMICS2 (PTDC/EEI-SII/1937/2014), and IF/00653/2012; also partially supported by internal IT projects QBigData and RAPID.
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Villa-Brito, J., Lopes, M.B., Carvalho, A.M., Vinga, S. (2020). Unravelling Breast and Prostate Common Gene Signatures by Bayesian Network Learning. In: Raposo, M., Ribeiro, P., Sério, S., Staiano, A., Ciaramella, A. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2018. Lecture Notes in Computer Science(), vol 11925. Springer, Cham. https://doi.org/10.1007/978-3-030-34585-3_25
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DOI: https://doi.org/10.1007/978-3-030-34585-3_25
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