ArticleBMC genetics2020
Bioinformatic screening for candidate biomarkers and their prognostic values in endometrial cancer.
Article in BMC genetics, 2020. 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, 6 citations in OpenAlex.
- Identification of Important Diagnostic Genes in the Uterine Using Bioinformatics and Machine Learning.Medical journal of the Islamic Republic of Iran · 2025Article
- Comprehensive analysis of a NAD+ metabolism-derived gene signature to predict the prognosis and immune landscape in endometrial cancer.Biomolecules & biomedicine · 2024Article
- Serum Anti-Mullerian Hormone Levels Might Indicate Premenopausal Endometrial Lesions.Diagnostics (Basel, Switzerland) · 2023Article
- Novel DNA Damage-Related Subtypes Characterization Identifies Uterine Corpus Endometrial Carcinoma (UCEC) Based on Machine Learning.Journal of oncology · 2022Article
- CTHRC1 expression is a novel shared diagnostic and prognostic biomarker of survival in six different human cancer subtypes.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundEndometrial cancer is a common gynecological cancer with annually increasing incidence worldwide. However, the biomarkers that provide prognosis and progression for this disease remain elusive.
resultsTwo eligible human endometrial cancer datasets (GSE17025 and GSE25405) were selected for the study. A total of 520 differentially expressed mRNAs and 30 differentially expressed miRNAs were identified. These mRNAs were mainly enriched in cell cycle, skeletal system development, vasculature development, oocyte maturation, and oocyte meiosis signalling pathways. A total of 160 pairs of differentially expressed miRNAs and mRNAs, including 22 differentially expressed miRNAs and 71 overlapping differentially expressed mRNAs, were validated in endometrial cancer samples using starBase v2.0 project. The prognosis analysis revealed that Cyclin E1 (CCNE1, one of the 82 hub genes, which correlated with hsa-miR-195 and hsa-miR-424) was significantly linked to a worse overall survival in endometrial cancer patients.
conclusionsThe hub genes and differentially expressed miRNAs identified in this study might be used as prognostic biomarkers for endometrial cancer and molecular targets for its treatment.
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Registered trials
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