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Construction of a miRNA-Based Nomogram Model to Predict the Prognosis of Endometrial Cancer

Authors :
Leyi Ni
Chengyun Tang
Yuning Wang
Jiaming Wan
Morgan G. Charles
Zilong Zhang
Chen Li
Ruijie Zeng
Yiyao Jin
Penghao Song
Ming Wei
Bocen Li
Jin Zhang
Zhenghao Wu
Source :
Journal of Personalized Medicine; Volume 12; Issue 7; Pages: 1154
Publication Year :
2022
Publisher :
Freie Universität Berlin, 2022.

Abstract

Objective: To investigate the differential expression of microRNA (miRNA) in patients with endometrial cancer and its relationship with prognosis and survival. Method: We used The Cancer Genome Atlas (TCGA) database to analyze differentially expressed miRNAs in endometrial cancer tissues and adjacent normal tissues. In addition, we successfully screened out key microRNAs to build nomogram models for predicting prognosis and we performed survival analysis on the key miRNAs as well. Result: We identified 187 differentially expressed miRNAs, which includes 134 up-regulated miRNAs and 53 down-regulated miRNAs. Further univariate Cox regression analysis screened out 47 significantly differentially expressed miRNAs and selected 12 miRNAs from which the prognostic nomogram model for ECA patients by LASSO analysis was constructed. Survival analysis showed that high expression of hsa-mir-138-2, hsa-mir-548f-1, hsa-mir-934, hsa-mir-940, and hsa-mir-4758 as well as low-expression of hsa-mir-146a, hsa-mir-3170, hsa-mir-3614, hsa-mir-3616, and hsa-mir-4687 are associated with poor prognosis in EC patients. However, significant correlations between the expressions levels of has-mir-876 and hsa-mir-1269a and patients’ prognosis are not found. Conclusion: Our study found that 12 significantly differentially expressed miRNAs might promote the proliferation, invasion, and metastasis of cancer cells by regulating the expression of upstream target genes, thereby affecting the prognosis of patients with endometrial cancer.

Details

Language :
English
Database :
OpenAIRE
Journal :
Journal of Personalized Medicine; Volume 12; Issue 7; Pages: 1154
Accession number :
edsair.doi.dedup.....01741175ba1090b75480bd406bc21171