Identification of Key Metabolites for Acute Lung Injury in Patients with Sepsis

Background: The study aimed to detect critical metabolites in acute lung injury (ALI). Methods: A comparative analysis of microarray profile of patients with sepsis-induced ALI compared with sepsis patients with was conducted using bioinformatic tools through constructing multi-omics network. Multi-...

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Main Authors: Pei-Quan WANG (Author), Jing LI (Author), Li-Li ZHANG (Author), Hong-Chun LV (Author), Su-Hua ZHANG (Author)
Format: Book
Published: Tehran University of Medical Sciences, 2019-01-01T00:00:00Z.
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042 |a dc 
100 1 0 |a Pei-Quan WANG  |e author 
700 1 0 |a Jing LI  |e author 
700 1 0 |a Li-Li ZHANG  |e author 
700 1 0 |a Hong-Chun LV  |e author 
700 1 0 |a Su-Hua ZHANG  |e author 
245 0 0 |a Identification of Key Metabolites for Acute Lung Injury in Patients with Sepsis 
260 |b Tehran University of Medical Sciences,   |c 2019-01-01T00:00:00Z. 
500 |a 10.18502/ijph.v48i1.785 
500 |a 2251-6085 
500 |a 2251-6093 
520 |a Background: The study aimed to detect critical metabolites in acute lung injury (ALI). Methods: A comparative analysis of microarray profile of patients with sepsis-induced ALI compared with sepsis patients with was conducted using bioinformatic tools through constructing multi-omics network. Multi-omics composite networks (gene network, metabolite network, phenotype network, gene-metabolite association network, phenotype-gene association network, and phenotype-metabolite association network) were constructed, following by integration of these composite networks to establish a heterogeneous network. Next, seed genes, and ALI phenotype were mapped into the heterogeneous network to further obtain a weighted composite network. Random walk with restart (RWR) was used for the weighted composite network to extract and prioritize the metabolites. On the basis of the distance proximity among metabolites, the top 50 metabolites with the highest proximity were identified, and the top 100 co-expressed genes interacted with the top 50 metabolites were also screened out. Results: Totally, there were 9363 nodes and 10,226,148 edges in the integrated composite network. There were 4 metabolites with the scores > 0.009, including CHITIN, Tretinoin, sodium ion, and Celebrex. Adenosine 5'-diphosphate, triphosadenine, and tretinoin had higher degrees in the composite network and the co-expressed network. Conclusion: Adenosine 5'-diphosphate, triphosadenine, and tretinoin may be potential biomarkers for diagnosis and treatment of ALI. 
546 |a EN 
690 |a Acute lung injury 
690 |a Metabolites 
690 |a Multi-omics network 
690 |a Differentially expressed genes 
690 |a Public aspects of medicine 
690 |a RA1-1270 
655 7 |a article  |2 local 
786 0 |n Iranian Journal of Public Health, Vol 48, Iss 1 (2019) 
787 0 |n https://ijph.tums.ac.ir/index.php/ijph/article/view/15941 
787 0 |n https://doaj.org/toc/2251-6085 
787 0 |n https://doaj.org/toc/2251-6093 
856 4 1 |u https://doaj.org/article/ccf87fe6c4ca4cd39ce51a3b0d02dcfb  |z Connect to this object online.