Identification of crucial genes in intracranial aneurysm based on weighted gene coexpression network analysis

被引:23
|
作者
Zheng, X. [1 ]
Xue, C. [2 ]
Luo, G. [3 ]
Hu, Y. [3 ]
Luo, W. [3 ]
Sun, X. [3 ]
机构
[1] Southern Med Univ, Affiliated Hosp 3, Dept Pharm, Guangzhou, Guangdong, Peoples R China
[2] Zhuhai Hitech Ind Dev Znoe Peoples Hosp, Dept Neurosurg, Zhuhai, Guangdong, Peoples R China
[3] Guang Dong 2 Prov Peoples Hosp, Dept Neurosurg, Guangzhou 510317, Guangdong, Peoples R China
关键词
SUBARACHNOID HEMORRHAGE; EXPRESSION PROFILES; CELLS; PROGRESSION; REVEALS; RESPONSES; ONCOGENE; PROTEINS; BONE;
D O I
10.1038/cgt.2015.10
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
The rupture of intracranial aneurysm (IA) is the leading cause for devastating subarachnoid hemorrhage. This study aimed to investigate genes related to IA and potential diagnosis targets. Two data sets (GSE15629 and GSE54083) were downloaded from Gene Expression Omnibus database. GSE15629 contained eight RI (ruptured IA), six UI (unruptured IA) and five control IA samples. GSE54083 included 8 RI, 5 UI and 10 superficial temporal artery samples. In total, 452 differentially expressed genes (DEGs) between RI and control, and 570 DEGs between UI and control, were identified. Protein-protein interaction networks for two kinds of DEGs related to RI and UI were constructed, respectively. Module networks were searched for DEGs related to RI or UI based on WGCNA (weighted gene coexpression network analysis). In the significant modules, FOS, CCL2, COL4A2 and CXCL5 were screened as crucial nodes with high degrees. Among them, FOS and CCL2 were enriched in immune response and COL4A2 was involved in the ECM (extracellular matrix) pathway, whereas CXCL5 was related to cytokine-cytokine receptor pathway. Taken together, FOS, CCL2, COL4A2 and CXCL5 might participate in the pathogenesis of RI or UI, and could serve as potential diagnosis targets.
引用
收藏
页码:238 / 245
页数:8
相关论文
共 50 条
  • [41] Identification of WTAP-related genes by weighted gene co-expression network analysis in ovarian cancer
    Jing Wang
    Jing Xu
    Ke Li
    Yunke Huang
    Yilin Dai
    Congjian Xu
    Yu Kang
    Journal of Ovarian Research, 13
  • [42] Identification of Key Genes Associated with Heat Stress in Rats by Weighted Gene Co-Expression Network Analysis
    Zhang, Fan
    Dou, Jinhuan
    Zhao, Xiuxin
    Luo, Hanpeng
    Ma, Longgang
    Wang, Lei
    Wang, Yachun
    ANIMALS, 2023, 13 (10):
  • [43] Identification of key module and hub genes in pulpitis using weighted gene co-expression network analysis
    Zhang, Denghui
    Zheng, Chen
    Zhu, Tianer
    Yang, Fan
    Zhou, Yiqun
    BMC ORAL HEALTH, 2023, 23 (01)
  • [44] Identification of 5 Potential Predictive Biomarkers for Alzheimer's Disease by Integrating the Unified Test for Molecular Signatures and Weighted Gene Coexpression Network Analysis
    Zhou, Siquan
    Ma, Guochen
    Luo, Hang
    Shan, Shufang
    Xiong, Jingyuan
    Cheng, Guo
    JOURNALS OF GERONTOLOGY SERIES A-BIOLOGICAL SCIENCES AND MEDICAL SCIENCES, 2023, 78 (04): : 653 - 658
  • [45] Weighted Gene Coexpression Network Analysis of Human Left Atrial Tissue Identifies Gene Modules Associated With Atrial Fibrillation
    Tan, Nicholas
    Chung, Mina K.
    Smith, Jonathan D.
    Hsu, Jeffrey
    Serre, David
    Newton, David W.
    Castel, Laurie
    Soltesz, Edward
    Pettersson, Gosta
    Gillinov, A. Marc
    Van Wagoner, David R.
    Barnard, John
    CIRCULATION-CARDIOVASCULAR GENETICS, 2013, 6 (04) : 362 - 371
  • [46] Identifying Macrophage-Related Genes in Ulcerative Colitis Using Weighted Coexpression Network Analysis and Machine Learning
    Hong, Shaocheng
    Wang, Hongqian
    Chan, Shixin
    Zhang, Jiayi
    Chen, Bangjie
    Ma, Xiaohan
    Chen, Xi
    MEDIATORS OF INFLAMMATION, 2023, 2023
  • [47] The identification of candidate radio marker genes using a coexpression network analysis in gamma-irradiated rice
    Kim, Sun-Hee
    Hwang, Sun-Goo
    Hwang, Jung Eun
    Jang, Cheol Seong
    Velusamy, Vijayanand
    Kim, Jin-Baek
    Kim, Sang Hoon
    Ha, Bo-Keun
    Kang, Si-Yong
    Kim, Dong Sub
    PHYSIOLOGIA PLANTARUM, 2013, 149 (04) : 554 - 570
  • [48] Identification of key genes associated with the progression of intrahepatic cholangiocarcinoma using weighted gene co-expression network analysis
    Ye, Zi
    Zeng, Zhirui
    Wang, Da
    Lei, Shan
    Shen, Yiyi
    Chen, Zubing
    ONCOLOGY LETTERS, 2020, 20 (01) : 483 - 494
  • [49] Identification of Hub Genes and Potential ceRNA Networks of Diabetic Nephropathy by Weighted Gene Co-Expression Network Analysis
    Li, Guoqing
    Zhang, Jun
    Liu, Dechen
    Qiong Wei
    Wang, Hui
    Lv, Yingqi
    Ye, Zheng
    Liu, Gaifang
    Li, Ling
    FRONTIERS IN GENETICS, 2021, 12
  • [50] Identification of key genes in calcific aortic valve disease via weighted gene co-expression network analysis
    Sun, Jin-Yu
    Hua, Yang
    Shen, Hui
    Qu, Qiang
    Kan, Jun-Yan
    Kong, Xiang-Qing
    Sun, Wei
    Shen, Yue-Yun
    BMC MEDICAL GENOMICS, 2021, 14 (01)