ArticleMedical science monitor : international medical journal of experimental and clinical research2019
Establishment of the Prognosis Predicting Signature for Endometrial Cancer Patient.
Article in Medical science monitor : international medical journal of experimental and clinical research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 35 citations in OpenAlex.
- Development and Validation of a Prognostic Nomogram for Endometrial Cancer Based on Systemic Immune-Inflammation Index and Key Clinicopathological Features.Cancer management and research · 2026Article
- Identification of methylation-driven genes prognosis signature and immune microenvironment in uterus corpus endometrial cancer.Cancer cell international · 2021Article
- Review
- Low Expression of miR-375 and miR-190b Differentiates Grade 3 Patients with Endometrial Cancer.Biomolecules · 2021Article
- Development of a novel immune-related lncRNA signature as a prognostic classifier for endometrial carcinoma.International journal of biological sciences · 2021Article
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND Novel biomarkers provide clinicians more critical information on tumor genetic features and patients' prognosis. Here, we aimed to establish prognosis-predicting signatures for endometrial carcinoma (EC) patients based on the miRNA information. MATERIAL AND METHODS The Cancer Genome Atlas (TCGA) website was available for dataset extraction. Prognosis-associated miRNAs were generated by univariate Cox regression test. Online websites were used to predict the targeted genes of these enrolled miRNAs. The miRNA-mRNA network was described by Cytoscape software, while the relevant signaling pathways of these targeted genes were enriched by Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. RESULTS The miRNA-based overall survival (OS) and recurrence-free survival (RFS) predicting signatures were constructed by LASSO Cox regression analyses, respectively, by which, the endometrial carcinoma patients were separated into high- and low-risk groups in both the discovery and validation sets. Univariate Cox regression analyses suggested that these high-risk patients had elevated death and recurrence risk compared to low-risk patients. In addition, multivariate Cox regression analysis confirmed that our signatures were independent prognosticate factors with or without clinicopathological features for endometrial carcinoma patients. Moreover, the miRNA-mRNA network was displayed by Cytoscape software, and the pathway enrichment analyses found that the targeted genes of these enrolled miRNAs were enriched in tumor progression and drug resistance-related pathways. CONCLUSIONS The OS and RFS predicting classifiers serve as independent prognosis-associated determiners for EC patients.
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