CDH1/E-cadherin and solid tumors. An updated gene-disease association analysis using bioinformatics tools

Abascal, María F.; Besso, María José; Rosso, Marina; Mencucci, María Victoria; Aparicio, Evangelina; Szapiro, Gala; Furlong, Laura I.; Vazquez‐Levin, Mónica H.

doi:10.1016/j.compbiolchem.2015.10.002

Cited by 8 publications

(6 citation statements)

References 69 publications

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“…DisGeNET platform has been used to study a variety of biomedical problems, which include investigating the molecular basis of specific diseases (33–36), annotating lists of genes produced by different types of omics and sequencing protocols (37–39), validating disease genes prediction methods (40–42), understanding disease mechanisms in the context of protein networks (43,44), gaining insight into drug action (45) and drug adverse reactions mechanisms (46), drug repurposing (47), exploring the molecular basis of disease comorbidities (48,49), assessing the performance of text-mining algorithms (50) and as part of other resources (51–53). …”

Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants

Piñero¹,

Bravo²,

et al. 2016

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The information about the genetic basis of human diseases lies at the heart of precision medicine and drug discovery. However, to realize its full potential to support these goals, several problems, such as fragmentation, heterogeneity, availability and different conceptualization of the data must be overcome. To provide the community with a resource free of these hurdles, we have developed DisGeNET (http://www.disgenet.org), one of the largest available collections of genes and variants involved in human diseases. DisGeNET integrates data from expert curated repositories, GWAS catalogues, animal models and the scientific literature. DisGeNET data are homogeneously annotated with controlled vocabularies and community-driven ontologies. Additionally, several original metrics are provided to assist the prioritization of genotype–phenotype relationships. The information is accessible through a web interface, a Cytoscape App, an RDF SPARQL endpoint, scripts in several programming languages and an R package. DisGeNET is a versatile platform that can be used for different research purposes including the investigation of the molecular underpinnings of specific human diseases and their comorbidities, the analysis of the properties of disease genes, the generation of hypothesis on drug therapeutic action and drug adverse effects, the validation of computationally predicted disease genes and the evaluation of text-mining methods performance.

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Section: Conclusion and Future Perspectivesmentioning

confidence: 99%

DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants

Piñero¹,

Bravo²,

et al. 2016

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“…DisGeNET has been used in several studies, including for a survey analysis of disease-associated genes (38), prediction of associations between diseases and genes and non-coding sequences (39,40), and associations between diseases (41,42). In the present study, DisGeNET analysis quickly and systematically yielded a total of 930 EC-related genes.…”

Section: Discussionmentioning

confidence: 91%

Identification of early stage recurrence endometrial cancer biomarkers using bioinformatics tools

et al. 2020

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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.

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“…CDH1 is a tumor suppressor gene that contains 16 exons (100 kb), located in the chromosome 16q22.1, and encodes for E-cadherin, a transmembrane glycoprotein of 120 kDa [ 30 ]. E-cadherin has three major structural domains: An extracellular domain forming the cell–cell adhesion (adherens junctions), a single transmembrane domain, and a cytoplasmic segment, that has binding domains for α, β-catenin, and p120 to form the cytoplasmic cell adhesion complex that interacts with the actin cytoskeleton [ 31 ].…”

Section: The Molecular Pathology Of the Epithelial-mesenchymal Tramentioning

confidence: 99%

“…The most common mutations are small insertions and deletions (35%) and large exon deletions, along with nonsense mutations (28%). Moreover, nonsense mutations (16%) and junction site mutations (16%) have been described [ 30 ].…”

Section: The Molecular Pathology Of the Epithelial-mesenchymal Tramentioning

confidence: 99%

Competing Endogenous RNA Networks in the Epithelial to Mesenchymal Transition in Diffuse-Type of Gastric Cancer

Landeros

Santoro

Carrasco-Aviño

et al. 2020

Cancers

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The diffuse-type of gastric cancer (DGC), molecularly associated with epithelial to mesenchymal transition (EMT), is increasing in incidence. Loss of E-cadherin expression is the hallmark of the EMT process and is largely due to the upregulation of the EMT-inducing transcription factors ZEB1/2, Snail, Slug, and Twist1/2. However, ncRNA, such as miRNA and lncRNAs, can also participate in the EMT process through the direct targeting of E-cadherin and other EMT-inducing transcription factors. Additionally, lncRNA can sponge the miRNA pool that targets these transcripts through competing endogenous RNA (ceRNA) networks. In this review, we focus on the role of ncRNA in the direct deregulation of E-cadherin, as well as EMT-inducing transcription factors. Based on the relevance of the ceRNA network hypothesis, and the lack of said networks in EMT, we performed a prediction analysis for all miRNAs and lncRNAs that target E-cadherin, as well as EMT-inducing transcription factors. This analysis resulted in novel predicted ceRNA networks for E-cadherin and EMT-inducing transcription factors (EMT-TFs), as well as the expansion of the molecular basis of the DGC.

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CDH1/E-cadherin and solid tumors. An updated gene-disease association analysis using bioinformatics tools

Cited by 8 publications

References 69 publications

DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants

DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants

Identification of early stage recurrence endometrial cancer biomarkers using bioinformatics tools

Competing Endogenous RNA Networks in the Epithelial to Mesenchymal Transition in Diffuse-Type of Gastric Cancer

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