Identification of potential biomarkers of sepsis using bioinformatics analysis

Yang, Yi; Li, Li

doi:10.3892/etm.2017.4178

Cited by 15 publications

(13 citation statements)

References 60 publications

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“…59 For the other key genes, RHOG has already been linked to sepsis through its participation in inflammatory processes. 60 The amount of LAT on the cell surface may determine the extent of T cell activation. 61 Systemic inflammation in patients with acute sepsis causes persistent T cell dysfunction, which leads to immunosuppression.…”

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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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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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“…Bioinformatics enables the analysis of large-scale genetic, protein, metabolite and other biomolecular data by the application of information science (24). The biological processes of the 63 differentially expressed proteins were mainly involved in the growth and development of organ tissues, the regulation of biological quality and response to external stimuli, which indicated that the differential proteins had extensive biological functions in the ET state.…”

Section: Discussionmentioning

confidence: 99%

“…In the present study, differential proteins were involved in 27 signaling pathways, including the TLR, NF-κB and TNF signaling pathways, and antigen presentation processes. In ET, the TLR and the NF-κB signaling pathways were inhibited (5,24), and the downstream effector factor, TNF-α, was downregulated. The results of the present study are consistent with these previous findings.…”

Section: Discussionmentioning

confidence: 99%

iTRAQ‑based proteomic analysis of endotoxin tolerance induced by lipopolysaccharide

Zhang

et al. 2019

Mol Med Report

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The purpose of the present study was to investigate the differentially expressed proteins between endotoxin tolerance and sepsis. Cell models of an endotoxin tolerance group (ET group) and sepsis group [lipopolysaccharide (LPS) group] were established using LPS and evaluated using ELISA and flow cytometry methods. Differentially expressed proteins between the ET and the LPS groups were identified using isobaric tags for relative and absolute quantitation (iTRAQ) analysis and evaluated by bioinformatics analysis. The expression of core proteins was detected by western blotting. It was identified that the expression of tumor necrosis factor-α and interleukin-6 was significantly decreased in the ET group compared with the LPS group. Following high-dose LPS stimulation for 24 h, the positive rate of cluster of differentiation-16/32 in the ET group (79.07%) was lower when compared with that of the LPS group (94.27%; P<0.05). A total of 235 proteins were identified by iTRAQ, and 36 upregulated proteins with >1.2-fold differences and 27 downregulated proteins with <0.833-fold differences were detected between the ET and LPS groups. Furthermore, the expression of high mobility group (HMG)-A1 and HMGA2 in the ET group was higher compared with the LPS group following high-dose LPS stimulation for 4 h, while HMGB1 and HMGB2 exhibited the opposite expression trend under the same conditions. In conclusion, proteomics analysis using iTRAQ technology contributes to a deeper understanding of ET mechanisms. HMGA1, HMGA2, HMGB1 and HMGB2 may serve a crucial role in the development of ET.

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“…Mice deficient for FCER1G gene encoding the γ-subunit of Fc epsilon RI show increased resistance to sepsis. [56,57] KEGG: Autophagy -other…”

Section: Kegg: Fc Epsilon Ri Signalling Pathwaymentioning

confidence: 99%

Discovery of pharmaceutically-targetable pathways and prediction of survivorship for pneumonia and sepsis patients from the view point of ensemble gene noise

Guryev

Moshkin

2020

Preprint

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Finding novel biomarkers for human pathologies and predicting clinical outcomes for patients is rather challenging. This stems from the heterogenous response of individuals to disease which is also reflected in the inter-individual variability of gene expression responses. This in turn obscures differential gene expression analysis (DGE). In the midst of the COVID-19 pandemic, we wondered whether an alternative to DGE approaches could be applied to dissect the molecular nature of a host-response to infection exemplified here by an analysis of H1N1 influenza, community/hospital acquired pneumonia (CAP) and sepsis. To this end, we turned to the analysis of ensemble gene noise. Ensemble gene noise, as we defined it here, represents a variance within an individual for a collection of genes encoding for either members of known biological pathways or subunits of annotated protein complexes. From the law of total variance, ensemble gene noise depends on the stoichiometry of the ensemble genes' expression and on their average noise (variance). Thus, rather than focusing on specific genes, ensemble gene noise allows for the holistic identification and interpretation of gene expression disbalance on the level of gene networks and systems. Comparing H1N1, CAP and sepsis patients we spotted common disturbances in a number of pathways/protein complexes relevant to the sepsis pathology which lead to an increase in the ensemble gene noise. Among others, these include mitochondrial respiratory chain complex I and peroxisomes which could be readily targeted for adjuvant treatment by methylene blue and 4-phenylbutyrate respectively. Finally, we showed that ensemble gene noise could be successfully applied for the prediction of clinical outcome, namely mortality, of CAP and sepsis patients. Thus, we conclude that ensemble gene noise represents a promising approach for the investigation of molecular mechanisms of a pathology through a prism of alterations in coherent expression of gene circuits.

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Identification of potential biomarkers of sepsis using bioinformatics analysis

Cited by 15 publications

References 60 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

iTRAQ‑based proteomic analysis of endotoxin tolerance induced by lipopolysaccharide

Discovery of pharmaceutically-targetable pathways and prediction of survivorship for pneumonia and sepsis patients from the view point of ensemble gene noise

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