Exploring the key genes and pathways in enchondromas using a gene expression microarray

被引:8
|
作者
Shi, Zhongju [1 ]
Zhou, Hengxing [1 ]
Pan, Bin [1 ]
Lu, Lu [1 ]
Kang, Yi [1 ]
Liu, Lu [1 ]
Wei, Zhijian [1 ]
Feng, Shiqing [1 ]
机构
[1] Tianjin Med Univ Gen Hosp, Dept Orthopaed, Tianjin, Peoples R China
基金
中国国家自然科学基金;
关键词
enchondromas; microarray; differentially expressed genes; pathways; protein-protein interaction; CANCER-CELL-PROLIFERATION; OLLIER-DISEASE; KYOTO ENCYCLOPEDIA; IDH2; MUTATIONS; BONE-TUMORS; INVASION; HYPERMETHYLATION; CHONDROSARCOMA; IDENTIFICATION; ORGANIZATION;
D O I
10.18632/oncotarget.16700
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Enchondromas are the most common primary benign osseous neoplasms that occur in the medullary bone; they can undergo malignant transformation into chondrosarcoma. However, enchondromas are always undetected in patients, and the molecular mechanism is unclear. To identify key genes and pathways associated with the occurrence and development of enchondromas, we downloaded the gene expression dataset GSE22855 and obtained the differentially expressed genes (DEGs) by analyzing high-throughput gene expression in enchondromas. In total, 635 genes were identified as DEGs. Of these, 225 genes (35.43%) were up-regulated, and the remaining 410 genes (64.57%) were down-regulated. We identified the predominant gene ontology (GO) categories and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways that were significantly over-represented in the enchondromas samples compared with the control samples. Subsequently the top 10 core genes were identified from the protein-protein interaction (PPI) network. The enrichment analyses of the genes mainly involved in two significant modules showed that the DEGs were principally related to ribosomes, protein digestion and absorption, ECM-receptor interaction, focal adhesion, amoebiasis and the PI3K-Akt signaling pathway. Together, these data elucidate the molecular mechanisms underlying the occurrence and development of enchondromas and provide promising candidates for therapeutic intervention and prognostic evaluation. However, further experimental studies are needed to confirm these results.
引用
收藏
页码:43967 / 43977
页数:11
相关论文
共 50 条
  • [41] Exploring matrix factorization techniques for significant genes identification of Alzheimer's disease microarray gene expression data
    Kong, Wei
    Mou, Xiaoyang
    Hu, Xiaohua
    BMC BIOINFORMATICS, 2011, 12
  • [42] Identification of differentially expressed genes and signalling pathways in bark of Hevea brasiliensis seedlings associated with secondary laticifer differentiation using gene expression microarray
    Loh, Swee Cheng
    Thottathil, Gincy P.
    Othman, Ahmad Sofiman
    PLANT PHYSIOLOGY AND BIOCHEMISTRY, 2016, 107 : 45 - 55
  • [43] Identification of significant periodic genes in microarray gene expression data
    Chen, J
    BMC BIOINFORMATICS, 2005, 6 (1)
  • [44] Unsupervised selection of informative genes in microarray gene expression data
    Liaghat, Samaneh
    Mansoori, Eghbal G.
    INTERNATIONAL JOURNAL OF APPLIED PATTERN RECOGNITION, 2016, 3 (04) : 351 - 367
  • [45] Parsimonious Selection of Useful Genes in Microarray Gene Expression Data
    Gonzalez-Navarro, Felix F.
    Belanche-Munoz, Lluis A.
    SOFTWARE TOOLS AND ALGORITHMS FOR BIOLOGICAL SYSTEMS, 2011, 696 : 45 - 55
  • [46] Identification of significant periodic genes in microarray gene expression data
    Jie Chen
    BMC Bioinformatics, 6
  • [47] AtCAST, a Tool for Exploring Gene Expression Similarities among DNA Microarray Experiments Using Networks
    Sasaki, Eriko
    Takahashi, Chitose
    Asami, Tadao
    Shimada, Yukihisa
    PLANT AND CELL PHYSIOLOGY, 2011, 52 (01) : 169 - 180
  • [48] Identification of key genes associated with bladder cancer using gene expression profiles
    Han, Yuping
    Jin, Xuefei
    Zhou, Hui
    Liu, Bin
    ONCOLOGY LETTERS, 2018, 15 (01) : 297 - 303
  • [49] Identification of key genes and pathways in pelvic organ prolapse based on gene expression profiling by bioinformatics analysis
    Zhou, Quan
    Hong, Li
    Wang, Jing
    ARCHIVES OF GYNECOLOGY AND OBSTETRICS, 2018, 297 (05) : 1323 - 1332
  • [50] Identification of Key Genes and Pathways associated with Endometriosis by Weighted Gene Co-expression Network Analysis
    Wu, Jingni
    Fang, Xiaoling
    Xia, Xiaomeng
    INTERNATIONAL JOURNAL OF MEDICAL SCIENCES, 2021, 18 (15): : 3425 - 3436