The aim of our study was to explore circular RNA (circRNA) expression profiles associated with human endometrial carcinoma (EC) and to analyse the molecular mechanisms involved in cancer development and their potential clinical importance. Differential expression profiles were revealed by Arraystar human circRNA microarray analysis. The results of the circRNA microarray were confirmed by quantitative real-time PCR. Interactions between circRNAs and microRNAs (miRNAs) were predicted using Arraystar's miRNA target prediction software. The functions of the circRNA-miRNA coexpression network were identified by KEGG pathway analysis and GO analysis. Compared with para-tumorous tissues, 14 genes were significantly upregulated and 12 genes were significantly downregulated in EC tissues ( < 0.05). The quantitative real-time PCR data demonstrated consistency with the results of the microarray profile analysis. We generated a circRNA-miRNA coexpression network. Hsa_circRNA_079422 expression was significantly lower and miR-136-5p expression was higher in EC tissues than in normal endometrial tissues. KEGG pathway analysis and GO analysis indicated that hsa_circRNA_079422 might play roles in different signalling pathways and biological functions. We confirmed the presence of different circRNA expression profiles and predicted the circRNA-miRNA coexpression network in human EC tissues. Hsa_circRNA_079422 might be involved in the pathogenesis and biological process of EC via interactions with miRNAs.IMPACT STATEMENT? EC is a common malignancy of the female reproductive system. CircRNAs were demonstrated to exert critical roles in cancers, including EC.? The results of this study add circRNAs expression profiles, the circRNA-miRNA coexpression network and cancer-related circRNA-miRNA target genes in EC. It was first found that hsa_circRNA_079422 was downregulated, while miR-136-5p was upregulated in EC tissues.? In clinical practice, early EC diagnosis lacks specific biomarkers, so most EC patients are diagnosed at an advanced stage. In the management of EC patients, we also lack personalised adjuvant treatment that combines the clinical pathological characteristics. For the existing literature, we identified a new EC differential expression biomarker, hsa_circ_079422. It can be used to verify the correlation with EC clinical severity or poor prognosis. Its targeting can also be used to stratify EC patients with different molecular types, including to guide adjuvant therapy. In addition, we can verify and analyse regulatory pathways associated with it for the design of regulating engineering circRNA.

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