Identification of novel key genes associated with uterine corpus endometrial carcinoma progression and prognosis

被引:5
|
作者
Li, Haixia [1 ,2 ,3 ]
Zhou, Quan [4 ]
Wu, Zhangying [5 ,7 ]
Lu, Xiaoling [1 ,2 ,6 ]
机构
[1] Guangxi Med Univ, Sch Basic Med Sci, Nanning, Peoples R China
[2] Guangxi Med Univ, Hosp Stomatol, Coll Stomatol, Guangxi Nanobody Engn Res Ctr,Guangxi Key Lab Nano, Nanning, Peoples R China
[3] Hubei Univ Med, Taihe Hosp, Dept Oncol, Shiyan, Peoples R China
[4] Hubei Univ Med, Renmin Hosp, Dept Tradit Chinese Med, Shiyan, Peoples R China
[5] Guizhou Med Univ, Dept Obstet & Gynecol, Affiliated Hosp, Guiyang, Peoples R China
[6] Guangxi Med Univ, 22 Shuangyong Rd, Nanning 530021, Peoples R China
[7] Guizhou Med Univ, Affiliated Hosp, 28 Guiyijie St, Guiyang 550001, Peoples R China
基金
国家重点研发计划;
关键词
Uterine corpus endometrial carcinoma (UCEC); weighted gene co-expression network analysis (WGCNA); differentially expressed gene analysis; survival analysis; bioinformatics; ESTROGEN-RECEPTOR-ALPHA; GINS COMPLEX; CANCER; AMPLIFICATION; SURVIVAL; RISK; EXPRESSION; BIOMARKER; PATTERNS; PREDICTS;
D O I
10.21037/atm-22-6461
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Background: Uterine corpus endometrial carcinoma (UCEC) is a common malignant cancer type which affects the health of women worldwide. However, its molecular mechanism has not been elucidated.Methods: To identify the hub modules and genes in UCEC associated with clinical phenotypes, the RNA sequencing data and clinical data of 543 UCEC samples were obtained from The Cancer Genome Atlas (TCGA) database and then subjected to weighted gene co-expression network analysis (WGCNA). To explore the potential biological function of the hub modules, Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted. Genes differentially expressed in UCEC were screened according to TCGA data using the "gdcDEAnalysis" package in R (The R Foundation for Statistical Computing). After intersecting with hub genes, the shared genes were used for further survival analyses. The relationship between gene expression level and clinical phenotype was analyzed in the TCGA-UCEC cohort in The University of ALabama at Birmingham CANcer data analysis Portal and the Human Protein Atlas. The microarray data set GSE17025 was also analyzed to validate the gene expression profiles.Results: There were 19 coexpression modules generated by WGCNA. Among them, 2 modules with 198 hub genes were highly correlated with clinical features (especially histologic grade and clinical stage). Meanwhile, 4,003 differentially expressed genes (DEGs) were screened out, and 164 DEGs overlapped with hub genes. Survival analyses revealed that high expression of GINS4 and low expression of ESR1 showed a trend of poor prognosis. Further analyses demonstrated that both messenger RNA (mRNA) and protein expression profiles of GINS4 and ESR1 were significantly associated with UCEC development and progression in TCGA and GSE17025 cohorts.Conclusions: Based on the integrated bioinformatic analyses, our data indicated that GINS4 and ESR1 might serve as potential prognostic markers and targets for UCEC therapy.
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页数:22
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