Machine Learning Supports Long Noncoding RNAs as Expression Markers for Endometrial Carcinoma
Autor(a) principal: | |
---|---|
Data de Publicação: | 2020 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRGS |
Texto Completo: | http://hdl.handle.net/10183/274302 |
Resumo: | Uterine 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. |
id |
UFRGS-2_d8998dca810460d210ac0a9220a56b0e |
---|---|
oai_identifier_str |
oai:www.lume.ufrgs.br:10183/274302 |
network_acronym_str |
UFRGS-2 |
network_name_str |
Repositório Institucional da UFRGS |
repository_id_str |
|
spelling |
Mello, Ana Carolina de MoraesFreitas, Martiela Vaz deCoutinho, Laura BezerraLopes, Tiago FalcónMatte, Ursula da Silveira2024-03-28T06:23:11Z20202314-6141http://hdl.handle.net/10183/274302001162026Uterine 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.application/pdfengBiomed research international. New York. Vol. 2020 (2020), e3968279, 12 p.Cancer ginecologicoBiomarcadoresMachine Learning Supports Long Noncoding RNAs as Expression Markers for Endometrial CarcinomaEstrangeiroinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFRGSinstname:Universidade Federal do Rio Grande do Sul (UFRGS)instacron:UFRGSTEXT001162026.pdf.txt001162026.pdf.txtExtracted Texttext/plain58772http://www.lume.ufrgs.br/bitstream/10183/274302/2/001162026.pdf.txt412e366ad1072c2bba2c6d57874d0ba6MD52ORIGINAL001162026.pdfTexto completo (inglês)application/pdf1727494http://www.lume.ufrgs.br/bitstream/10183/274302/1/001162026.pdfb2184301ef388427d3961ec2f2ae5579MD5110183/2743022024-03-29 06:19:11.179582oai:www.lume.ufrgs.br:10183/274302Repositório de PublicaçõesPUBhttps://lume.ufrgs.br/oai/requestopendoar:2024-03-29T09:19:11Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS)false |
dc.title.pt_BR.fl_str_mv |
Machine Learning Supports Long Noncoding RNAs as Expression Markers for Endometrial Carcinoma |
title |
Machine Learning Supports Long Noncoding RNAs as Expression Markers for Endometrial Carcinoma |
spellingShingle |
Machine Learning Supports Long Noncoding RNAs as Expression Markers for Endometrial Carcinoma Mello, Ana Carolina de Moraes Cancer ginecologico Biomarcadores |
title_short |
Machine Learning Supports Long Noncoding RNAs as Expression Markers for Endometrial Carcinoma |
title_full |
Machine Learning Supports Long Noncoding RNAs as Expression Markers for Endometrial Carcinoma |
title_fullStr |
Machine Learning Supports Long Noncoding RNAs as Expression Markers for Endometrial Carcinoma |
title_full_unstemmed |
Machine Learning Supports Long Noncoding RNAs as Expression Markers for Endometrial Carcinoma |
title_sort |
Machine Learning Supports Long Noncoding RNAs as Expression Markers for Endometrial Carcinoma |
author |
Mello, Ana Carolina de Moraes |
author_facet |
Mello, Ana Carolina de Moraes Freitas, Martiela Vaz de Coutinho, Laura Bezerra Lopes, Tiago Falcón Matte, Ursula da Silveira |
author_role |
author |
author2 |
Freitas, Martiela Vaz de Coutinho, Laura Bezerra Lopes, Tiago Falcón Matte, Ursula da Silveira |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Mello, Ana Carolina de Moraes Freitas, Martiela Vaz de Coutinho, Laura Bezerra Lopes, Tiago Falcón Matte, Ursula da Silveira |
dc.subject.por.fl_str_mv |
Cancer ginecologico Biomarcadores |
topic |
Cancer ginecologico Biomarcadores |
description |
Uterine 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. |
publishDate |
2020 |
dc.date.issued.fl_str_mv |
2020 |
dc.date.accessioned.fl_str_mv |
2024-03-28T06:23:11Z |
dc.type.driver.fl_str_mv |
Estrangeiro info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10183/274302 |
dc.identifier.issn.pt_BR.fl_str_mv |
2314-6141 |
dc.identifier.nrb.pt_BR.fl_str_mv |
001162026 |
identifier_str_mv |
2314-6141 001162026 |
url |
http://hdl.handle.net/10183/274302 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.ispartof.pt_BR.fl_str_mv |
Biomed research international. New York. Vol. 2020 (2020), e3968279, 12 p. |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UFRGS instname:Universidade Federal do Rio Grande do Sul (UFRGS) instacron:UFRGS |
instname_str |
Universidade Federal do Rio Grande do Sul (UFRGS) |
instacron_str |
UFRGS |
institution |
UFRGS |
reponame_str |
Repositório Institucional da UFRGS |
collection |
Repositório Institucional da UFRGS |
bitstream.url.fl_str_mv |
http://www.lume.ufrgs.br/bitstream/10183/274302/2/001162026.pdf.txt http://www.lume.ufrgs.br/bitstream/10183/274302/1/001162026.pdf |
bitstream.checksum.fl_str_mv |
412e366ad1072c2bba2c6d57874d0ba6 b2184301ef388427d3961ec2f2ae5579 |
bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 |
repository.name.fl_str_mv |
Repositório Institucional da UFRGS - Universidade Federal do Rio Grande do Sul (UFRGS) |
repository.mail.fl_str_mv |
|
_version_ |
1801225114905214976 |