Article Text
Abstract
Introduction Based on bioinformatics analysis and clinical tissue sample verification, this study sought potential targets for prediction and diagnosis in uterine corpus endometrial carcinoma (UCEC).
Methods The DEGs in endometrial carcinoma cohorts of GEO and TCGA were analyzed by R and the series test of cluster was performed by STEM software. GO and KEGG analysis and PPI analysis were performed to screen for Hub genes. The expression level and prognostic analysis of these genes were verified in the online database. The expression of ECT2 was validated by immunohistochemistry in local clinical endometrial samples.
Results There are 763 common DEGs (368 up-regulated genes and 395 down-regulated genes) and 530 genes of endometrial carcinogenesis related cluster. 13 Hub genes were selected for further analysis, 9 significantly differential genes were selected as follow: ASPM, ATAD2, BUB1B, ECT2, KIF14, NUF2, HELLS, NCAPG and SPAG5. The ROC curves of candidate genes revealed that ECT2 had the best diagnostic efficacy for UCEC. The expression level of ECT2 was significantly higher in endometrial carcinoma than that in normal endometria and differently among different FIGO stages and pathological grades in UCEC. The level of ECT2 in local endometrial samples, including normal endometria (30 cases), simple hyperplasia (30 cases), atypical hyperplasia (52 cases), and endometrial carcinoma (83 cases) revealed an increase gradually trend from normal to cancer. ECT2 can significantly distinguish and help diagnose normal endometrium, simple hyperplasia, atypical hyperplasia and endometrial cancer.
Conclusion/Implications ECT2 is expected to become a potential marker for the screening and diagnosis of endometrial carcinoma.