Identification and Validation of an m7G-Related lncRNAs Signature for Prognostic Prediction and Immune Function Analysis in Endometrial Cancer

被引:16
作者
Sun, Jiani [1 ]
Li, Li [1 ]
Chen, Hong [1 ]
Gan, Lei [1 ]
Guo, Xiaoqing [1 ]
Sun, Jing [1 ]
机构
[1] Tongji Univ, Shanghai First Matern & Infant Hosp, Sch Med, Dept Gynecol, Shanghai 200092, Peoples R China
关键词
N7-methylguanosine; long noncoding RNAs; endometrial carcinoma; prognostic model; immune function; LONG NONCODING RNAS; RECEPTORS;
D O I
10.3390/genes13081301
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
Background: N7-methylguanosine is a novel kind of internal modification that is widespread in human mRNA. The relationship between m7G-related lncRNAs (MRL) and endometrial cancer remains unknown. The aim of our study is to explore a predictive prognosis MRL signature in endometrial cancer and identify the underlying biological mechanism. Methods: We obtained RNA-seq profiles, clinical data, and information on somatic mutations from the TCGA database and obtained m7G-related genes from a previous study. MRLs were identified through a co-expression network. The prognostic model was constructed based on 10 m7G-related lncRNAs. Differentially expressed genes between low- and high-risk groups were identified for further analysis, consisting of functional enrichment analysis, immune function analysis, somatic mutation analysis, and potential drugs exploration. Results: We constructed a 10-MRLs signature. According to the risk score, the signature was classified into high- and low-risk groups. The signature had a reliable capacity for predicting the prognosis of endometrial cancer patients. The findings about differentially expressed genes were also of great significance for therapeutic treatments for endometrial cancer and gave novel insights into exploring the underlying molecular mechanism. Conclusion: The prognostic model based on 10 MRLs is a reliable and promising approach for predicting clinical outcomes and suggesting therapeutic methods for endometrial cancer patients.
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页数:15
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