Identification of early stage recurrence endometrial cancer biomarkers using bioinformatics tools

Autores
Besso, María José; Montivero, Luciana; Lacunza, Ezequiel; Argibay, María Cecilia; Abba, Martín Carlos; Furlong, Laura Inés; Colas, Eva; Gil Moreno, Antonio; Reventos, Jaume; Bello, Ricardo; Vazquez Levin, Mónica Hebe
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Endometrial cancer (EC) is the sixth most common cancer in women worldwide. Early diagnosis is critical in recurrent EC management. The present study aimed to identify biomarkers of EC early recurrence using a workflow that combined text and data mining databases (DisGeNET, Gene Expression Omnibus), a prioritization algorithm to select a set of putative candidates (ToppGene), protein-protein interaction network analyses (Search Tool for the Retrieval of Interacting Genes, cytoHubba), association analysis of selected genes with clinicopathological parameters, and survival analysis (Kaplan-Meier and Cox proportional hazard ratio analyses) using a The Cancer Genome Atlas cohort. A total of 10 genes were identified, among which the targeting protein for Xklp2 (TPX2) was the most promising independent prognostic biomarker in stage I EC. TPX2 expression (mRNA and protein) was higher (P<0.0001 and P<0.001, respectively) in ETS variant transcription factor 5-overexpressing Hec1a and Ishikawa cells, a previously reported cell model of aggressive stage I EC. In EC biopsies, TPX2 mRNA expression levels were higher (P<0.05) in high grade tumors (grade 3) compared with grade 1-2 tumors (P<0.05), in tumors with deep myometrial invasion (>50% compared with <50%; P<0.01), and in intermediate-high recurrence risk tumors compared with low-risk tumors (P<0.05). Further validation studies in larger and independent EC cohorts will contribute to confirm the prognostic value of TPX2.
Facultad de Ciencias Médicas
Centro de Investigaciones Inmunológicas Básicas y Aplicadas
Materia
Medicina
endometrial cancer
Bioinformatics
biomarkers
recurrence
TPX2
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-nd/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/107852
Acceder
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network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Identification of early stage recurrence endometrial cancer biomarkers using bioinformatics toolsBesso, María JoséMontivero, LucianaLacunza, EzequielArgibay, María CeciliaAbba, Martín CarlosFurlong, Laura InésColas, EvaGil Moreno, AntonioReventos, JaumeBello, RicardoVazquez Levin, Mónica HebeMedicinaendometrial cancerBioinformaticsbiomarkersrecurrenceTPX2Endometrial cancer (EC) is the sixth most common cancer in women worldwide. Early diagnosis is critical in recurrent EC management. The present study aimed to identify biomarkers of EC early recurrence using a workflow that combined text and data mining databases (DisGeNET, Gene Expression Omnibus), a prioritization algorithm to select a set of putative candidates (ToppGene), protein-protein interaction network analyses (Search Tool for the Retrieval of Interacting Genes, cytoHubba), association analysis of selected genes with clinicopathological parameters, and survival analysis (Kaplan-Meier and Cox proportional hazard ratio analyses) using a The Cancer Genome Atlas cohort. A total of 10 genes were identified, among which the targeting protein for Xklp2 (<i>TPX2</i>) was the most promising independent prognostic biomarker in stage I EC. <i>TPX2</i> expression (mRNA and protein) was higher (P<0.0001 and P<0.001, respectively) in ETS variant transcription factor 5-overexpressing Hec1a and Ishikawa cells, a previously reported cell model of aggressive stage I EC. In EC biopsies, <i>TPX2</i> mRNA expression levels were higher (P<0.05) in high grade tumors (grade 3) compared with grade 1-2 tumors (P<0.05), in tumors with deep myometrial invasion (>50% compared with <50%; P<0.01), and in intermediate-high recurrence risk tumors compared with low-risk tumors (P<0.05). Further validation studies in larger and independent EC cohorts will contribute to confirm the prognostic value of <i>TPX2</i>.Facultad de Ciencias MédicasCentro de Investigaciones Inmunológicas Básicas y Aplicadas2020info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf873-886http://sedici.unlp.edu.ar/handle/10915/107852enginfo:eu-repo/semantics/altIdentifier/url/http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC7388212&blobtype=pdfinfo:eu-repo/semantics/altIdentifier/url/https://www.spandidos-publications.com/10.3892/or.2020.7648info:eu-repo/semantics/altIdentifier/issn/1791-2431info:eu-repo/semantics/altIdentifier/pmid/32705231info:eu-repo/semantics/altIdentifier/doi/10.3892/or.2020.7648info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0/Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-10-15T11:15:46Zoai:sedici.unlp.edu.ar:10915/107852Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-15 11:15:46.821SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Identification of early stage recurrence endometrial cancer biomarkers using bioinformatics tools
title Identification of early stage recurrence endometrial cancer biomarkers using bioinformatics tools
spellingShingle Identification of early stage recurrence endometrial cancer biomarkers using bioinformatics tools
Besso, María José
Medicina
endometrial cancer
Bioinformatics
biomarkers
recurrence
TPX2
title_short Identification of early stage recurrence endometrial cancer biomarkers using bioinformatics tools
title_full Identification of early stage recurrence endometrial cancer biomarkers using bioinformatics tools
title_fullStr Identification of early stage recurrence endometrial cancer biomarkers using bioinformatics tools
title_full_unstemmed Identification of early stage recurrence endometrial cancer biomarkers using bioinformatics tools
title_sort Identification of early stage recurrence endometrial cancer biomarkers using bioinformatics tools
dc.creator.none.fl_str_mv Besso, María José
Montivero, Luciana
Lacunza, Ezequiel
Argibay, María Cecilia
Abba, Martín Carlos
Furlong, Laura Inés
Colas, Eva
Gil Moreno, Antonio
Reventos, Jaume
Bello, Ricardo
Vazquez Levin, Mónica Hebe
author Besso, María José
author_facet Besso, María José
Montivero, Luciana
Lacunza, Ezequiel
Argibay, María Cecilia
Abba, Martín Carlos
Furlong, Laura Inés
Colas, Eva
Gil Moreno, Antonio
Reventos, Jaume
Bello, Ricardo
Vazquez Levin, Mónica Hebe
author_role author
author2 Montivero, Luciana
Lacunza, Ezequiel
Argibay, María Cecilia
Abba, Martín Carlos
Furlong, Laura Inés
Colas, Eva
Gil Moreno, Antonio
Reventos, Jaume
Bello, Ricardo
Vazquez Levin, Mónica Hebe
author2_role author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Medicina
endometrial cancer
Bioinformatics
biomarkers
recurrence
TPX2
topic Medicina
endometrial cancer
Bioinformatics
biomarkers
recurrence
TPX2
dc.description.none.fl_txt_mv Endometrial cancer (EC) is the sixth most common cancer in women worldwide. Early diagnosis is critical in recurrent EC management. The present study aimed to identify biomarkers of EC early recurrence using a workflow that combined text and data mining databases (DisGeNET, Gene Expression Omnibus), a prioritization algorithm to select a set of putative candidates (ToppGene), protein-protein interaction network analyses (Search Tool for the Retrieval of Interacting Genes, cytoHubba), association analysis of selected genes with clinicopathological parameters, and survival analysis (Kaplan-Meier and Cox proportional hazard ratio analyses) using a The Cancer Genome Atlas cohort. A total of 10 genes were identified, among which the targeting protein for Xklp2 (<i>TPX2</i>) was the most promising independent prognostic biomarker in stage I EC. <i>TPX2</i> expression (mRNA and protein) was higher (P<0.0001 and P<0.001, respectively) in ETS variant transcription factor 5-overexpressing Hec1a and Ishikawa cells, a previously reported cell model of aggressive stage I EC. In EC biopsies, <i>TPX2</i> mRNA expression levels were higher (P<0.05) in high grade tumors (grade 3) compared with grade 1-2 tumors (P<0.05), in tumors with deep myometrial invasion (>50% compared with <50%; P<0.01), and in intermediate-high recurrence risk tumors compared with low-risk tumors (P<0.05). Further validation studies in larger and independent EC cohorts will contribute to confirm the prognostic value of <i>TPX2</i>.
Facultad de Ciencias Médicas
Centro de Investigaciones Inmunológicas Básicas y Aplicadas
description Endometrial cancer (EC) is the sixth most common cancer in women worldwide. Early diagnosis is critical in recurrent EC management. The present study aimed to identify biomarkers of EC early recurrence using a workflow that combined text and data mining databases (DisGeNET, Gene Expression Omnibus), a prioritization algorithm to select a set of putative candidates (ToppGene), protein-protein interaction network analyses (Search Tool for the Retrieval of Interacting Genes, cytoHubba), association analysis of selected genes with clinicopathological parameters, and survival analysis (Kaplan-Meier and Cox proportional hazard ratio analyses) using a The Cancer Genome Atlas cohort. A total of 10 genes were identified, among which the targeting protein for Xklp2 (<i>TPX2</i>) was the most promising independent prognostic biomarker in stage I EC. <i>TPX2</i> expression (mRNA and protein) was higher (P<0.0001 and P<0.001, respectively) in ETS variant transcription factor 5-overexpressing Hec1a and Ishikawa cells, a previously reported cell model of aggressive stage I EC. In EC biopsies, <i>TPX2</i> mRNA expression levels were higher (P<0.05) in high grade tumors (grade 3) compared with grade 1-2 tumors (P<0.05), in tumors with deep myometrial invasion (>50% compared with <50%; P<0.01), and in intermediate-high recurrence risk tumors compared with low-risk tumors (P<0.05). Further validation studies in larger and independent EC cohorts will contribute to confirm the prognostic value of <i>TPX2</i>.
publishDate 2020
dc.date.none.fl_str_mv 2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/107852
url http://sedici.unlp.edu.ar/handle/10915/107852
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC7388212&blobtype=pdf
info:eu-repo/semantics/altIdentifier/url/https://www.spandidos-publications.com/10.3892/or.2020.7648
info:eu-repo/semantics/altIdentifier/issn/1791-2431
info:eu-repo/semantics/altIdentifier/pmid/32705231
info:eu-repo/semantics/altIdentifier/doi/10.3892/or.2020.7648
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-nd/4.0/
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
dc.format.none.fl_str_mv application/pdf
873-886
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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