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.
引用
收藏
页数:22
相关论文
共 50 条
  • [11] Identification of key miRNAs and targeted genes involved in the progression of oral squamous cell carcinoma
    Gu, Yuxi
    Tang, Shouyi
    Wang, Zhen
    Cai, Luyao
    Shen, Yingqiang
    Zhou, Yu
    JOURNAL OF DENTAL SCIENCES, 2022, 17 (02) : 666 - 676
  • [12] A comprehensive analysis of CEBPA on prognosis and function in uterine corpus endometrial carcinoma
    Wang, Jiaxing
    Huang, Weiyu
    Chai, Shiwei
    Gan, Jiayi
    Zeng, Yulu
    Long, Ping
    Pang, Lihong
    SCIENTIFIC REPORTS, 2024, 14 (01):
  • [13] Identification of key genes associated with endometriosis and endometrial cancer by bioinformatics analysis
    Ma, Ruyue
    Zheng, Yu
    Wang, Jianing
    Xu, Hong
    Zhang, Ruirui
    Xie, Zhijia
    Zhang, Lei
    Zhao, Ruiheng
    FRONTIERS IN ONCOLOGY, 2024, 14
  • [14] Bioinformatics analysis of RNA sequencing data reveals multiple key genes in uterine corpus endometrial carcinoma
    Shen, Liang
    Liu, Ming
    Liu, Wei
    Cui, Jing
    Li, Changzhong
    ONCOLOGY LETTERS, 2018, 15 (01) : 205 - 212
  • [15] Identification of CAPG as a potential prognostic biomarker associated with immune cell infiltration and ferroptosis in uterine corpus endometrial carcinoma
    Liu, Junwei
    Zhu, Weiqiang
    Xia, Lingjin
    Zhu, Qianxi
    Mao, Yanyan
    Shen, Yupei
    Li, Min
    Zhang, Zhaofeng
    Du, Jing
    FRONTIERS IN ENDOCRINOLOGY, 2024, 15
  • [16] Novel targets and their functions in the prognosis of uterine corpus endometrial cancer patients
    Sui, Xin
    Feng, Penghui
    Guo, Jie
    Chen, Xingtong
    Chen, Rong
    Zhang, Yanmin
    He, Falin
    Deng, Feng
    JOURNAL OF APPLIED GENETICS, 2024, 65 (04) : 757 - 772
  • [17] FAT2 mutation is associated with better prognosis and responsiveness to immunotherapy in uterine corpus endometrial carcinoma
    Wang, Zhe
    Xing, Linan
    Huang, Yujie
    Han, Peilin
    CANCER MEDICINE, 2023, 12 (03): : 3797 - 3811
  • [18] Key wound healing genes as diagnostic biomarkers and therapeutic targets in uterine corpus endometrial carcinoma: an integrated in silico and in vitro study
    Jiang, Fuchuan
    Ahmad, Sajjad
    Kanwal, Sadia
    Hameed, Yasir
    Tang, Qian
    HEREDITAS, 2025, 162 (01):
  • [19] Identification of hub genes and key pathways associated with the progression of gynecological cancer
    Zhang, Xi
    Wang, Yudong
    ONCOLOGY LETTERS, 2019, 18 (06) : 6516 - 6524
  • [20] Identification of Key Genes Associated with Progression and Prognosis of Bladder Cancer through Integrated Bioinformatics Analysis
    Verma, Shiv
    Shankar, Eswar
    Lin, Spencer
    Singh, Vaibhav
    Chan, E. Ricky
    Cao, Shufen
    Fu, Pingfu
    MacLennan, Gregory T.
    Ponsky, Lee E.
    Gupta, Sanjay
    CANCERS, 2021, 13 (23)