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. 2023 Jun 6:14:1183769.
doi: 10.3389/fimmu.2023.1183769. eCollection 2023.

The development of endoplasmic reticulum-related gene signatures and the immune infiltration analysis of sepsis

Yi Zhou  1 , Yifang Chen  1 , Jianbo Li  1 , Zailin Fu  1 , Qian Chen  1 , Wei Zhang  1 , Huan Luo  2 , Minghua Xie  1
Affiliations

The development of endoplasmic reticulum-related gene signatures and the immune infiltration analysis of sepsis

Yi Zhou et al. Front Immunol. .

Abstract

Background: Sepsis is a complex condition involving multiorgan failure, resulting from the hosts' deleterious systemic immune response to infection. It is characterized by high mortality, with limited effective detection and treatment options. Dysregulated endoplasmic reticulum (ER) stress is directly involved in the pathophysiology of immune-mediated diseases.

Methods: Clinical samples were obtained from Gene Expression Omnibus datasets (i.e., GSE65682, GSE54514, and GSE95233) to perform the differential analysis in this study. A weighted gene co-expression network analysis algorithm combining multiple machine learning algorithms was used to identify the diagnostic biomarkers for sepsis. Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, and the single-sample gene set enrichment analysis algorithm were used to analyze immune infiltration characteristics in sepsis. PCR analysis and western blotting were used to demonstrate the potential role of TXN in sepsis.

Results: Four ERRGs, namely SET, LPIN1, TXN, and CD74, have been identified as characteristic diagnostic biomarkers for sepsis. Immune infiltration has been repeatedly proved to play a vital role both in sepsis and ER. Subsequently, the immune infiltration characteristics result indicated that the development of sepsis is mediated by immune-related function, as four diagnostic biomarkers were strongly associated with the immune infiltration landscape of sepsis. The biological experiments in vitro and vivo demonstrate TXN is emerging as crucial player in maintaining ER homeostasis in sepsis.

Conclusion: Our research identified novel potential biomarkers for sepsis diagnosis, which point toward a potential strategy for the diagnosis and treatment of sepsis.

Keywords: diagnostic biomarkers; endoplasmic reticulum; immune infiltration; machine learning; sepsis.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Study flow.
Figure 2
Figure 2
Analysis of the DEGs and molecular function enrichment. (A) Volcano analysis of the DEGs between healthy and sepsis groups. (B) Heatmap illustrating the top 25 up- and downregulated DEGs in the healthy and sepsis groups. (C) GO enrichment analysis of the DEGs. (D) KEGG enrichment analysis of the DEGs.
Figure 3
Figure 3
WGCNA construction of the transcriptome matrix and key DE-ERRGs selection. (A, B) Scale independence and mean connectivity. The soft threshold (power) β = 9. (C) Gene dendrogram and module color. (D) Association between different gene modules and clinical features. (E) Venn plot showing the key DE-ERRGs determined by WGCNA and differential expression analysis. (F) PPI network exhibiting the relationship between the 14 key DE-ERRGs.
Figure 4
Figure 4
Characteristic diagnostic biomarker exploration using machine learning algorithms. (A, B) Identification of feature variates using LASSO analysis. (C) SVM-RFE analysis of 14 key DE-ERRGs. (D) Variate importance of DE-ERRGs determined via the RF algorithm. (E) Venn diagram showing the intersection genes of three machine learning algorithms. (F) Correlation heatmap of diagnostic biomarkers.
Figure 5
Figure 5
Nomogram construction and diagnostic effectiveness evaluation. Expression profile of SET, LPIN1, TXN, and CD74 in (A) the training cohort and (B) the test cohort. (C, D) Nomogram developed based on diagnostic biomarker expression profiles in the training and test cohorts. (E, F) Diagnostic effectiveness exploration of diagnostic biomarkers and nomogram.
Figure 6
Figure 6
Molecular subgroups of sepsis. (A) Consensus matrix for k = 2. (B) Consensus cumulative distribution function (CDF). (C) Delta area. (D) PCA plot of clusters A and B. (E) GSVA estimation of KEGG terms in sepsis. (F–I) Expression levels of SET, LPIN1, TXN, and CD74.
Figure 7
Figure 7
Generation of DEGs and tumor microenvironment (TME) characteristics in subgroups. (A, B) GO and KEGG enrichment evaluation of DEGs in different subgroups. (C) Immune score estimation in sepsis. (D) Immune infiltration feature of 23 immune cells in the sepsis subgroups.
Figure 8
Figure 8
Evaluation of immune infiltration landscape in healthy and sepsis groups. (A) The percentage of 23 immune cells in the healthy and sepsis groups, as calculated by ssGSEA. (B) PCA plot based on the immune cell fractions. (C) The fractions of cells associated with immune infiltration in healthy and sepsis groups.
Figure 9
Figure 9
Correlation analysis of four biomarkers and immune infiltration features. The lollipop plots show the relationship between immune infiltration characteristics and CD74 (A), LPIN1 (B), SET (C), and TXN (D).
Figure 10
Figure 10
The effect of TXN on endoplasmic reticulum stress of cardiomyocytes after sepsis, including the levels of CD74 (A), LPIN1 (B), SET (C), and TXN (D), as detected by qPCR in control and sepsis rats (n = 6). (E–H) The expressions of CHOP, GRP78, and GRP94 after sepsis (n = 3). (I–L) The expressions of CHOP, GRP78, and GRP94 after being treated with siTXN (n = 3). Results are expressed as mean ± SD. Each analysis in vitro was performed in triplicate. Significance level: *p< 0.05, **p< 0.01, ***p< 0.001, and ****p< 0.0001 versus control; #p< 0.05, ##p< 0.01, ###p< 0.001, and ####p< 0.0001 versus sepsis.
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References

    1. Svendsen M, Steindal SA, Hamilton Larsen M, Trygg Solberg M. Comparison of the systematic inflammatory response syndrome and the quick sequential organ failure assessment for prognostic accuracy in detecting sepsis in the emergency department: a systematic review. Int Emerg Nurs (2023) 66:101242. doi: 10.1016/j.ienj.2022.101242 - DOI - PubMed
    1. Fleischmann C, Scherag A, Adhikari NK, Hartog CS, Tsaganos T, Schlattmann P, et al. . Assessment of global incidence and mortality of hospital-treated sepsis. current estimates and limitations. Am J Respir Crit Care Med (2016) 193(3):259–72. doi: 10.1164/rccm.201504-0781OC - DOI - PubMed
    1. van der Poll T, van de Veerdonk FL, Scicluna BP, Netea MG. The immunopathology of sepsis and potential therapeutic targets. Nat Rev Immunol (2017) 17(7):407–20. doi: 10.1038/nri.2017.36 - DOI - PubMed
    1. Cohen J. The immunopathogenesis of sepsis. Nature (2002) 420(6917):885–91. doi: 10.1038/nature01326 - DOI - PubMed
    1. Nedeva C, Menassa J, Duan M, Liu C, Doerflinger M, Kueh AJ, et al. . TREML4 receptor regulates inflammation and innate immune cell death during polymicrobial sepsis. Nat Immunol (2020) 21(12):1585–96. doi: 10.1038/s41590-020-0789-z - DOI - PubMed

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