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dc.contributor.authorMello, Ana Carolina de Moraespt_BR
dc.contributor.authorFreitas, Martiela Vaz dept_BR
dc.contributor.authorCoutinho, Laura Bezerrapt_BR
dc.contributor.authorLopes, Tiago Falcónpt_BR
dc.contributor.authorMatte, Ursula da Silveirapt_BR
dc.date.accessioned2024-03-28T06:23:11Zpt_BR
dc.date.issued2020pt_BR
dc.identifier.issn2314-6141pt_BR
dc.identifier.urihttp://hdl.handle.net/10183/274302pt_BR
dc.description.abstractUterine corpus endometrial carcinoma (UCEC) is the second most common type of gynecological tumor. Several research studies have recently shown the potential of different ncRNAs as biomarkers for prognostics and diagnosis in different types of cancers, including UCEC. Thus, we hypothesized that long noncoding RNAs (lncRNAs) could serve as efficient factors to discriminate solid primary (TP) and normal adjacent (NT) tissues in UCEC with high accuracy. We performed an in silico differential expression analysis comparing TP and NT from a set of samples downloaded from the Cancer Genome Atlas (TCGA) database, targeting highly differentially expressed lncRNAs that could potentially serve as gene expression markers. All analyses were performed in R software. The receiver operator characteristics (ROC) analyses and both supervised and unsupervised machine learning indicated a set of 14 lncRNAs that may serve as biomarkers for UCEC. Functions and putative pathways were assessed through a coexpression network and target enrichment analysis.en
dc.format.mimetypeapplication/pdfpt_BR
dc.language.isoengpt_BR
dc.relation.ispartofBiomed research international. New York. Vol. 2020 (2020), e3968279, 12 p.pt_BR
dc.rightsOpen Accessen
dc.subjectCancer ginecologicopt_BR
dc.subjectBiomarcadorespt_BR
dc.titleMachine Learning Supports Long Noncoding RNAs as Expression Markers for Endometrial Carcinomapt_BR
dc.typeArtigo de periódicopt_BR
dc.identifier.nrb001162026pt_BR
dc.type.originEstrangeiropt_BR


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