Identification of early stage recurrence endometrial cancer biomarkers using bioinformatics tools
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),...
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| Autores principales: | , , , , , , , , , , |
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| Formato: | Articulo |
| Lenguaje: | Inglés |
| Publicado: |
2020
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| Acceso en línea: | http://sedici.unlp.edu.ar/handle/10915/107852 http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC7388212&blobtype=pdf https://www.spandidos-publications.com/10.3892/or.2020.7648 |
| Aporte de: |
| id |
I19-R120-10915-107852 |
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| record_format |
dspace |
| institution |
Universidad Nacional de La Plata |
| institution_str |
I-19 |
| repository_str |
R-120 |
| collection |
SEDICI (UNLP) |
| language |
Inglés |
| topic |
Medicina endometrial cancer Bioinformatics biomarkers recurrence TPX2 |
| spellingShingle |
Medicina endometrial cancer Bioinformatics biomarkers recurrence TPX2 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 Identification of early stage recurrence endometrial cancer biomarkers using bioinformatics tools |
| topic_facet |
Medicina endometrial cancer Bioinformatics biomarkers recurrence TPX2 |
| 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>. |
| format |
Articulo Articulo |
| author |
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_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_sort |
Besso, María José |
| title |
Identification of early stage recurrence endometrial cancer biomarkers using bioinformatics tools |
| 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 |
| publishDate |
2020 |
| url |
http://sedici.unlp.edu.ar/handle/10915/107852 http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC7388212&blobtype=pdf https://www.spandidos-publications.com/10.3892/or.2020.7648 |
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