Identification of the pivotal differentially expressed genes and pathways involved in Staphylococcus aureus-induced infective endocarditis by using bioinformatics analysis

被引:5
|
作者
Xiao, S-J [1 ]
Zhou, Y-F [3 ]
Jia, H. [2 ]
Wu, Q. [1 ]
Pan, D-F [1 ]
机构
[1] Xuzhou Med Univ, Affiliated Hosp, Dept Cardiol, Xuzhou, Jiangsu, Peoples R China
[2] Xuzhou Med Univ, Affiliated Hosp, Dept Neurosurg, Xuzhou, Jiangsu, Peoples R China
[3] Nanjing Med Univ, Affiliated Hosp 1, Dept Cardiol, Nanjing, Jiangsu, Peoples R China
关键词
Staphylococcus aureus; Infective endocarditis; Differentially expressed genes; Pathways; Protein-protein interaction network; MicroRNA-transcription factor-mRNA network; MICRORNA-361; CANCER; CELLS;
D O I
10.26355/eurrev_202101_24420
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
OBJECTIVE: Infective endocarditis (IE), particularly by Staphylococcus aureus, is an uncommon bacteremia-associated infection of the endocardium and cardiac valves. Herein, we evaluated predictive noninvasive biomarkers for IE caused by S. aureus through bioinformatics analysis. MATERIALS AND METHODS: Staphylococcus aureus-associated and IE-associated differentially expressed genes (DEGs) were identified by bioinformatics analysis of the GSE6269 and GSE29161 Gene Expression Omnibus (GEO) datasets. The DEGs were analyzed with the LIMMA package, and the coregulated genes were chosen as the intersection of DEGs between the two datasets, called common differentially expressed genes (CDEGs). The enrichment study of CDEGs was subsequently performed with the DAVID and KOBAS web resources. Finally, protein-protein interaction (PPI) network, microRNA (miRNA)-transcription factor (TF)-mRNA (messenger RNA) regulatory network, and the network of drug-genes were identified. RESULTS: From GSE6269 and GSE29161, respectively, a total of 201 and 741 DEGs were obtained. Gene Ontology (GO) analysis showed that CDEGs were primarily involved in innate immune response, extracellular exosome, as well as calcium ion binding, while the pathway analysis of Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed that CDEGs were significantly enriched in the B-cell receptor, IL-17, and NF-kappa B signaling pathways. The hub genes in the PPI network included HP, S100Al2, SPI1, CD14, CCR1, S100A9 and so on. In the miR-NA-TF-mRNA regulatory network, SPI1 could target miR-361-5p, miR-155-5p, and miR-339-5p in the progression of IE. CONCLUSIONS: Several pivotal genes and pathways were identified in the progression of S. aureus-induced IE, which may have the potential for early detection.
引用
收藏
页码:487 / 497
页数:11
相关论文
共 50 条
  • [31] Identification of Differentially Expressed Genes and Signaling Pathways in Acute Myocardial Infarction Based on Integrated Bioinformatics Analysis
    Chen, Da-Qiu
    Kong, Xiang-Sheng
    Shen, Xue-Bin
    Huang, Mao-Zhi
    Zheng, Jian-Ping
    Sun, Jing
    Xu, Shang-Hua
    CARDIOVASCULAR THERAPEUTICS, 2019, 2019
  • [32] Identification of differentially expressed genes, signaling pathways and immune infiltration in rheumatoid arthritis by integrated bioinformatics analysis
    Yanzhi Ge
    Li Zhou
    Zuxiang Chen
    Yingying Mao
    Ting Li
    Peijian Tong
    Letian Shan
    Hereditas, 158
  • [33] Identification of differentially expressed genes and pathways in kidney of ANCA-associated vasculitis by integrated bioinformatics analysis
    Deng, Xu
    Gao, Junying
    Zhao, Fei
    RENAL FAILURE, 2022, 44 (01) : 204 - 216
  • [34] Identification of differentially expressed genes, signaling pathways and immune infiltration in rheumatoid arthritis by integrated bioinformatics analysis
    Ge, Yanzhi
    Zhou, Li
    Chen, Zuxiang
    Mao, Yingying
    Li, Ting
    Tong, Peijian
    Shan, Letian
    HEREDITAS, 2021, 158 (01)
  • [35] Identification of Differentially Expressed Genes and Molecular Pathways Involved in Osteoclastogenesis Using RNA-seq
    Rashid, Sarah
    Wilson, Scott G.
    Zhu, Kun
    Walsh, John P.
    Xu, Jiake
    Mullin, Benjamin H.
    GENES, 2023, 14 (04)
  • [36] Identification of Genes and Pathways Involved in Hepatocellular carcinoma by Bioinformatics Analysis
    Shi, Longqing
    Wu, Di
    Chen, Weibo
    Liu, Shengyong
    Yang, Yu
    Yang, Yong
    Nong, Kate
    Qu, Zhen
    Chen, Jing
    An, Yong
    Lu, Yunjie
    BASIC & CLINICAL PHARMACOLOGY & TOXICOLOGY, 2020, 127 : 42 - 42
  • [37] Uncovering the differentially expressed genes and pathways involved in the progression of stable coronary artery disease to acute myocardial infarction using bioinformatics analysis
    Xiao, S-J
    Zhou, Y-F
    Wu, Q.
    Ma, W-R
    Chen, M-L
    Pan, D-F
    EUROPEAN REVIEW FOR MEDICAL AND PHARMACOLOGICAL SCIENCES, 2021, 25 (01) : 301 - 312
  • [38] Identification of pivotal genes and pathways for spinal cord injury via bioinformatics analysis
    Zhu, Zonghao
    Shen, Qiang
    Zhu, Liang
    Wei, Xiaokang
    MOLECULAR MEDICINE REPORTS, 2017, 16 (04) : 3929 - 3937
  • [39] Identification and validation of differentially expressed genes for targeted therapy in NSCLC using integrated bioinformatics analysis
    Altaf, Reem
    Ilyas, Umair
    Ma, Anmei
    Shi, Meiqi
    FRONTIERS IN ONCOLOGY, 2023, 13
  • [40] IDENTIFICATION OF DIFFERENTIALLY EXPRESSED GENES AND PATHWAYS IN NON-DIABETIC CKD AND DIABETIC CKD BY INTEGRATED BIOINFORMATICS ANALYSIS
    Barrios, Clara
    Julia, Jan
    Gomez, Jessica
    Marquez, Eva
    Ribas, Andres
    Sans, Laia
    RodrIguez GarcIa, Eva
    Crespo, Marta
    Riera, Marta
    NEPHROLOGY DIALYSIS TRANSPLANTATION, 2023, 38 : I110 - I110