An antisense transcript mediates MALAT1 response in human 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: Long non-coding RNAs (lncRNAs) represent a substantial portion of the human transcriptome. LncRNAs present a very stringent cell-type/tissue specificity being potential candidates for therapeutical applications during aging and disease. As example, targeting of MALAT1, a highly conserved lncRNA originally identified in metastatic non-small cell lung cancer, has shown promising results in cancer regression. Nevertheless, the regulation and specificity of MALAT1 have not been directly addressed. Interestingly, MALAT1 locus is spanned by an antisense transcript named TALAM1. Methods: Here using a collection of breast cancer cells and in vitro and in vivo migration assays we characterized the dynamics of expression and demonstrated that TALAM1 regulates and synergizes with MALAT1 during tumorigenesis. Results: Down-regulation of TALAM1 was shown to greatly impact on the capacity of breast cancer cells to migrate in vitro or to populate the lungs of immunocompromised mice. Additionally, we demonstrated that TALAM1 cooperates with MALAT1 in the regulation of the properties guiding breast cancer aggressiveness and malignancy. Conclusions: By characterizing this sense/anti-sense pair we uncovered the complexity of MALAT1 locus regulation, describing new potential candidates for cancer targeting.This work was supported by Fundação para a Ciência e Tecnologia (FCT) (PTDC/BIM-MED/0032/2014); UID/BIM/50005/2019, project funded by Fundação para a Ciência e a Tecnologia (FCT)/ Ministério da Ciência, Tecnologia e Ensino Superior (MCTES) through Fundos do Orçamento de Estado; LISBOA-01-0145-FEDER-016394, projeto cofinanciado pelo FEDER através POR Lisboa 2020 - Programa Operacional Regional de Lisboa, do PORTUGAL 2020 e pela Fundação para a Ciência e a Tecnologia; LISBOA-01-0145-FEDER-028534, projeto cofinanciado pelo FEDER através POR Lisboa 2020 - Programa Operacional Regional de Lisboa, do PORTUGAL 2020 e pela Fundação para a Ciência e a Tecnologia. B.B.J. was an FCT Investigator (IF/00166/2014). C.V. was a Gulbenkian Foundation Fellow. S.N.-P. was recipient of an individual FCT postdoctoral fellowship (SFRH/BPD/91159/2012).info:eu-repo/semantics/publishedVersio

Level Up Your Sneaker Game : Applying machine learning techniques to support data-driven investment decisions in the sneaker resale market

Sneaker resale has become a worldwide phenomenon. The resale market is growing, expected to potentially reach up to $30 bn by 2030. More and more people want to take part in making fortunes out of shifting high valued Nike SB sneakers, rare Air Jordans or eccentric Yeezys. Notably, traditional customer roles are changing: consumers are no longer only buying sneakers for wearing them themselves, but are also engaging in resale activities. Additionally, new market participants are entering the game with the sole aim of making profits as large as possible from buying and then reselling brand new shoes. The purpose of this thesis is to provide insight into how machine learning methods can support data-driven investment decisions in the sneaker resale market. Two different reseller personas will be introduced, together with a description of scenarios and questions these might encounter. Using data from StockX.com, the leading marketplace for sneaker resale, various machine learning techniques will be applied to arrive at founded investment decision for these two personas. To meet the needs of the different personas, this thesis makes use of both simpler methods, such as linear and logistic regression as well as KNN and regression trees, and more complex methods such as Random Forest and XGBoost models. The authors chose a practical approach with the analysis of different scenarios, aiming to allow sneakerheads, who engage in and are hence interested in information on resale markets, to profit from the insights. The research shows that both simple and complex methods can be useful in these decisions, reaching high accuracy values as well as oftentimes good predictions. It also shows that the sneaker resale price is influenced by a myriad of factors, and that especially celebrity collaborations seem to have high influence on resale value of sneakers