Bioinformatic screening for candidate biomarkers and their prognostic values in endometrial cancer

Abstract

Background: Endometrial cancer is a common gynecological cancer with annually increasing incidence worldwide. However, the biomarkers that provide prognosis and progression for this disease remain elusive. Results: Two 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. Conclusions: The 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.

Novel DNA Damage-Related Subtypes Characterization Identifies Uterine Corpus Endometrial Carcinoma (UCEC) Based on Machine Learning

“…However, at an advanced stage, it has poor prognosis, lacks specific early symptoms, and there are no effective strategies for early detection [14]. Developing a reliable prognostic model to offer parameters for determining clinical treatment choices is thus a priority [15].…”