Twiner: correlation-based regularization for identifying common cancer gene signatures
| Autor(a) principal: | |
|---|---|
| Data de Publicação: | 2019 |
| Outros Autores: | , |
| Tipo de documento: | Artigo |
| Idioma: | eng |
| Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
| Texto Completo: | http://hdl.handle.net/10451/54563 |
Resumo: | © The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
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Twiner: correlation-based regularization for identifying common cancer gene signaturesBreast invasive carcinomaGene networkProstate adenocarcinomaSparse logistic regressionTriple-negative breast cancer© The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.Background: Breast and prostate cancers are typical examples of hormone-dependent cancers, showing remarkable similarities at the hormone-related signaling pathways level, and exhibiting a high tropism to bone. While the identification of genes playing a specific role in each cancer type brings invaluable insights for gene therapy research by targeting disease-specific cell functions not accounted so far, identifying a common gene signature to breast and prostate cancers could unravel new targets to tackle shared hormone-dependent disease features, like bone relapse. This would potentially allow the development of new targeted therapies directed to genes regulating both cancer types, with a consequent positive impact in cancer management and health economics. Results: We address the challenge of extracting gene signatures from transcriptomic data of prostate adenocarcinoma (PRAD) and breast invasive carcinoma (BRCA) samples, particularly estrogen positive (ER+), and androgen positive (AR+) triple-negative breast cancer (TNBC), using sparse logistic regression. The introduction of gene network information based on the distances between BRCA and PRAD correlation matrices is investigated, through the proposed twin networks recovery (twiner) penalty, as a strategy to ensure similarly correlated gene features in two diseases to be less penalized during the feature selection procedure. Conclusions: Our analysis led to the identification of genes that show a similar correlation pattern in BRCA and PRAD transcriptomic data, and are selected as key players in the classification of breast and prostate samples into ER+ BRCA/AR+ TNBC/PRAD tumor and normal tissues, and also associated with survival time distributions. The results obtained are supported by the literature and are expected to unveil the similarities between the diseases, disclose common disease biomarkers, and help in the definition of new strategies for more effective therapies.This work was supported by national funds through Fundação para a Ciência e a Tecnologia (FCT) with references UID/EEA/50008/2019 (Instituto de Telecomunicações), UID/CEC/50021/2019 (INESC-ID), UID/EMS/50022/2019 (IDMEC, LAETA), PREDICT (PTDC/CCI-CIF/29877/2017), and PERSEIDS (PTDC/EMS-SIS/0642/2014).Springer NatureRepositório da Universidade de LisboaLopes, Marta B.Casimiro, SandraVinga, Susana2022-09-23T13:09:14Z20192019-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10451/54563engBMC Bioinformatics. 2019 Jun 25;20(1):35610.1186/s12859-019-2937-81471-2105info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-08T17:01:00Zoai:repositorio.ul.pt:10451/54563Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:05:22.325742Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
| dc.title.none.fl_str_mv |
Twiner: correlation-based regularization for identifying common cancer gene signatures |
| title |
Twiner: correlation-based regularization for identifying common cancer gene signatures |
| spellingShingle |
Twiner: correlation-based regularization for identifying common cancer gene signatures Lopes, Marta B. Breast invasive carcinoma Gene network Prostate adenocarcinoma Sparse logistic regression Triple-negative breast cancer |
| title_short |
Twiner: correlation-based regularization for identifying common cancer gene signatures |
| title_full |
Twiner: correlation-based regularization for identifying common cancer gene signatures |
| title_fullStr |
Twiner: correlation-based regularization for identifying common cancer gene signatures |
| title_full_unstemmed |
Twiner: correlation-based regularization for identifying common cancer gene signatures |
| title_sort |
Twiner: correlation-based regularization for identifying common cancer gene signatures |
| author |
Lopes, Marta B. |
| author_facet |
Lopes, Marta B. Casimiro, Sandra Vinga, Susana |
| author_role |
author |
| author2 |
Casimiro, Sandra Vinga, Susana |
| author2_role |
author author |
| dc.contributor.none.fl_str_mv |
Repositório da Universidade de Lisboa |
| dc.contributor.author.fl_str_mv |
Lopes, Marta B. Casimiro, Sandra Vinga, Susana |
| dc.subject.por.fl_str_mv |
Breast invasive carcinoma Gene network Prostate adenocarcinoma Sparse logistic regression Triple-negative breast cancer |
| topic |
Breast invasive carcinoma Gene network Prostate adenocarcinoma Sparse logistic regression Triple-negative breast cancer |
| description |
© The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
| publishDate |
2019 |
| dc.date.none.fl_str_mv |
2019 2019-01-01T00:00:00Z 2022-09-23T13:09:14Z |
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info:eu-repo/semantics/publishedVersion |
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info:eu-repo/semantics/article |
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article |
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publishedVersion |
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http://hdl.handle.net/10451/54563 |
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http://hdl.handle.net/10451/54563 |
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eng |
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eng |
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BMC Bioinformatics. 2019 Jun 25;20(1):356 10.1186/s12859-019-2937-8 1471-2105 |
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info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
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Springer Nature |
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Springer Nature |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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