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Gene expression profile analysis of ventilator-associated pneumonia.


ABSTRACT: Based on the gene expression profile of patients with ventilator-associated pneumonia (VAP) and patients not affected by the disease, the present study aimed to enhance the current understanding of VAP development using bioinformatics methods. The expression profile GSE30385 was downloaded from the Gene Expression Omnibus database. The Linear Models for Microarray Data package in R language was used to screen and identify differentially expressed genes (DEGs), which were grouped as up? and down?regulated genes. The up? and downregulated genes were functionally enriched using the Database for Annotation, Visualization and Integrated Discovery system and then annotated according to TRANSFAC, Tumor Suppressor Gene and Tumor Associated Gene databases. Subsequently, the protein?protein interaction (PPI) network was constructed, followed by module analysis using CFinder software. A total of 69 DEGs, including 33 up? and 36 downregulated genes were screened out in patients with VAP. Upregulated genes were mainly enriched in functions and pathways associated with the immune response (including the genes ELANE and LTF) and the mitogen-activated protein kinase (MAPK) signaling pathway (including MAPK14). The PPI network comprised 64 PPI pairs and 44 nodes. The top two modules were enriched in different pathways, including the MAPK signaling pathway. Genes including ELANE, LTF and MAPK14 may have important roles in the development of VAP via altering the immune response and the MAPK signaling pathway.

SUBMITTER: Xu X 

PROVIDER: S-EPMC4626132 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature

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Gene expression profile analysis of ventilator-associated pneumonia.

Xu Xiaoli X   Yuan Bo B   Liang Quan Q   Huang Huimin H   Yin Xiangyi X   Sheng Xiaoyue X   Nie Niuyan N   Fang Hongmei H  

Molecular medicine reports 20150929 5


Based on the gene expression profile of patients with ventilator-associated pneumonia (VAP) and patients not affected by the disease, the present study aimed to enhance the current understanding of VAP development using bioinformatics methods. The expression profile GSE30385 was downloaded from the Gene Expression Omnibus database. The Linear Models for Microarray Data package in R language was used to screen and identify differentially expressed genes (DEGs), which were grouped as up‑ and down‑  ...[more]

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