Genomics of neonatal sepsis: has-miR-150 targeting BCL11B functions in disease progression

Huang, Li; Qiao, Lixing; Zhu, Huan; Jiang, Li; Li, Yin

doi:10.1186/s13052-018-0575-9

Cited by 20 publications

(12 citation statements)

References 42 publications

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“…MAPK14 has already been identified as a key gene and potential therapeutic target in neonatal sepsis. 52 , 53 Deficiency of MAPK14 in macrophages protects mice from lipopolysaccharide (LPS)-induced sepsis. 54 FGR proto-oncogene (FGR) is a Src family kinase that is expressed in innate immune cells, including macrophages and granulocytes, and is considered a major signaling molecule downstream of many immune cell receptors.…”

Section: Discussionmentioning

confidence: 99%

Screening of Key Genes of Sepsis and Septic Shock Using Bioinformatics Analysis

Sun

Feng

Yang

et al. 2021

JIR

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Sepsis is a disease associated with high mortality. We performed bioinformatic analysis to identify key biomarkers associated with sepsis and septic shock. Methods: The top 20% of genes showing the greatest variance between sepsis and controls in the GSE13904 dataset (children) were screened by co-expression network analysis. The differentially expressed genes (DEGs) were identified through analyzing differential gene expression between sepsis patients and control in the GSE13904 (children) and GSE154918 (adult) data sets. Intersection analysis of module genes and DEGs was performed to identify common DEGs for enrichment analysis, protein-protein interaction network (PPI network) analysis, and Short Time-series Expression Miner (STEM) analysis. The PPI network genes were ranked by degree of connectivity, and the top 100 sepsis-associated genes were identified based on the area under the receiver operating characteristic curve (AUC). In addition, we evaluated differences in immune cell infiltration between sepsis patients and controls in children (GSE13904, GSE25504) and adults (GSE9960, GSE154918). Finally, we analyzed differences in DNA methylation levels between sepsis patients and controls in GSE138074 (adults). Results: The common genes were associated mainly with up-regulated inflammatory and metabolic responses, as well as down-regulated immune responses. Sepsis patients showed lower infiltration by most types of immune cells. Genes in the PPI network with AUC values greater than 0.9 in both GSE13904 (children) and GSE154918 (adults) were screened as key genes for diagnosis. These key genes (MAPK14, FGR, RHOG, LAT, PRKACB, UBE2Q2, ITK, IL2RB, and CD247) were also identified in STEM analysis to be progressively dysregulated across controls, sepsis patients and patients with septic shock. In addition, the expression of MAPK14, FGR, and CD247 was modified by methylation. Conclusion: This study identified several potential diagnostic genes and inflammatory and metabolic responses mechanisms associated with the development of sepsis.

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Section: Discussionmentioning

confidence: 99%

Screening of Key Genes of Sepsis and Septic Shock Using Bioinformatics Analysis

Sun

Feng

Yang

et al. 2021

JIR

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“…Wang et al [ 17 ] identified candidate biomarkers (Icam1 and Socs3) for sepsis from one GEO datasets by performing DEG, gene functional enrichment, and PPI network analyses, and validated their results with RT-qPCR. Huang et al [ 18 ] identified hub genes (MAPK14, ZAP70, and TSPO) and miRNA related to sepsis progression from 1 dataset of E-MTAB-4765 by developing an integrated method including DEG screen, pathway analysis, gene annotations, PPI networks, and regulatory network construction. These studies also identified some hub-genes of sepsis.…”

Section: Discussionmentioning

confidence: 99%

Screening and identification of key gene in sepsis development

et al. 2020

Medicine

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Sepsis is one of the leading causes of mortality in intensive care units (ICU). The growing incidence rate of sepsis and its high mortality rate result are very important sociosanitary problems. Sepsis is a result of infection which can cause systemic inflammatory and organ failure. But the pathogenesis and the molecular mechanisms of sepsis is still not well understood. The aim of the present study was to identify the candidate key genes in the progression of sepsis. Microarray datasets GSE28750, GSE64457, and GSE95233 were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified, and function enrichment analyses were performed. The protein–protein interaction network (PPI) was constructed and the module analysis was performed using STRING and Cytoscape. Furthermore, to verify the results of the bioinformatics analyses, the expression levels of selected DEGs were quantified by Reverse Transcription-Polymerase Chain Reaction (RT-PCR) in libobolysaccharide (LPS)-induced Human Umbilical Vein Endothelial Cells (HUVECs) to support the result of bioinformatics analysis. Thirteen hub genes were identified and biological process analysis revealed that these genes were mainly enriched in apoptotic process, inflammatory response, innate immune response. Hub genes with high degrees, including MAPK14, SLC2A3, STOM, and MMP8, were demonstrated to have an association with sepsis. Furthermore, RT-PCR results showed that SLC2A3 and MAPK14 were significantly upregulated in the HUVECs induced by LPS compared with controls. In conclusion, DEGs and hub genes identified in the present study help us understand the molecular mechanisms of sepsis, and provide candidate targets for diagnosis and treatment of sepsis.

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“…In these patients, T-cells are inadequately supervised by the thymus microenvironment, exhibit decreased apoptosis and cannot differentiate into Th2 T-cells (33). Huang et al (34) reported that the downregulation of Zap70 accelerated disease progression of neonatal sepsis. IKBKB encodes IKB kinase 2 (also known as IKKβ /IKK2), a component of the nuclear factor-κB signaling pathway.…”

Section: Discussionmentioning

confidence: 99%

Identification of key miRNA‑mRNA pairs in septic mice by bioinformatics analysis

2019

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Sepsis is one of the most common causes of death among critically ill patients in intensive care units worldwide; however, the microRNAs (miRNAs/miRs) involved in the sepsis process (and their target genes) are largely unknown. The present study integrated miRNA and mRNA datasets to elucidate key sepsis-related miRNA-mRNA pairs. The datasets, GSE74952 and GSE55238 were downloaded from the Gene Expression Omnibus. By performing bioinformatics analysis such as GEO2R, miRNA target gene prediction, Gene Ontology analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis and miRNA-mRNA network analysis, a total of four sepsis-related miRNA-mRNA pairs were successfully obtained. Mmu-miR-370-3p, cluster of differentiation (CD)8a, CD247, Zap70 and inhibitor of nuclear factor κ B kinase subunit β (Ikbkb) were identified as the components involved in these pairs, and these genes were enriched in the T-cell receptor signaling pathway. Finally, reverse transcription-quantitative PCR results validated that the expression levels of the four genes (CD8a, CD247, Zap70 and Ikbkb) in the sepsis model mice were consistent with the microarray analysis. In conclusion, the present study identified four sepsis-related miRNA-mRNA pairs using bioinformatics analysis. These results indicated that the candidate miRNA-mRNA pairs may be involved in the regulation of immunity in sepsis, which may in turn act as indicators or therapeutic targets for sepsis.

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Genomics of neonatal sepsis: has-miR-150 targeting BCL11B functions in disease progression

Cited by 20 publications

References 42 publications

Screening of Key Genes of Sepsis and Septic Shock Using Bioinformatics Analysis

Screening of Key Genes of Sepsis and Septic Shock Using Bioinformatics Analysis

Screening and identification of key gene in sepsis development

Identification of key miRNA‑mRNA pairs in septic mice by bioinformatics analysis

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