
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

39294340
72724
10.1038/s41598-024-72724-1
Article
Investigating immune dysregulation and hub genes in septic cardiomyopathy development
Li Wenli 1
Hua Shi 2
Yang Jianzhong 1
Cao Yang 1
Gao Ranran 1
Sun Hu 3
Yang Kai 1
Wang Ying 4
Peng Peng Pengpeng4949@126.com

1
1 https://ror.org/02qx1ae98 grid.412631.3 Emergency Trauma Center, The First Affiliated Hospital of Xinjiang Medical University, No. 137, Liyushan South Road, Urumqi, 830011 Xinjiang People’s Republic of China
2 https://ror.org/011r8ce56 grid.415946.b 0000 0004 7434 8069 Department of Neurosurgery, Linyi People’s Hospital, Linyi, People’s Republic of China
3 https://ror.org/02qx1ae98 grid.412631.3 Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Xinjiang Medical University, Ürümqi, People’s Republic of China
4 https://ror.org/02qx1ae98 grid.412631.3 Medical Department, The First Affiliated Hospital of Xinjiang Medical University, Ürümqi, People’s Republic of China
16 9 2024
16 9 2024
2024
14 2160821 3 2023
10 9 2024
© The Author(s) 2024
2024
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Septic cardiomyopathy is a life-threatening heart dysfunction caused by severe infection. Considering the complexity of pathogenesis and high mortality, the identification of efficient biomarkers are needed to guide clinical practice. Based on multimicroarray analysis, this study aimed to explore the pathogenesis of septic cardiomyopathy and the related immune landscape. The results showed that septic cardiomyopathy resulted in organ dysfunction due to extreme pro- and anti-inflammatory effects. In this process, KLRG1, PRF1, BCL6, GAB2, MMP9, IL1R1, JAK3, IL6ST, and SERPINE1 were identified as the hub genes regulating the immune landscape of septic cardiomyopathy. Nine transcription factors regulated the expression of these genes: SRF, STAT1, SP1, RELA, PPARG, NFKB1, PPARA, SMAD3, and STAT3. The hub genes activated the Th17 cell differentiation pathway, JAK-STAT signaling pathway, and cytokine‒cytokine receptor interaction pathway. These pathways were mainly involved in regulating the inflammatory response, adaptive immune response, leukocyte-mediated immunity, cytokine-mediated immunity, immune effector processes, myeloid cell differentiation, and T-helper cell differentiation. These nine hub genes could be considered biomarkers for the early prediction of septic cardiomyopathy.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-72724-1.

Keywords

Septic cardiomyopathy
Hub genes
Transcription factors
Immune landscape
Pathogenesis
Biomarkers
Prognosis signatures
Subject terms

Computational biology and bioinformatics
Immunology
Cardiology
Diseases
National Natural Science Foundation of China81860335 issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Septic cardiomyopathy is a life-threatening organ dysfunction caused by sepsis. According to an epidemiological survey of global mortality from 1990 to 2017, there were 48.9 million new sepsis patients worldwide in 2017, and 11 million of these patients died1. Moreover, sepsis affects 20% of ICU patients, and 90-day mortality is as high as 35.5%2. Sepsis-related death occurs in two stages, with the early peak occurring within a few days of onset, primarily due to cardiopulmonary failure caused by untimely treatment. The late peak occurs within a few weeks and is characterized by multiple organ failure, among which the prognosis of septic cardiomyopathy is the most unfavorable3.

As a clinical manifestation of sepsis, septic cardiomyopathy reflects persistent inflammation-induced myocardial injury driven by a dynamic imbalance of the innate and adaptive immune systems4, namely, systemic inflammatory response syndrome (SIRS) and compensatory anti-inflammatory response syndrome (CARS). In the early stage of septic cardiomyopathy, SIRS is an uncontrolled hyperimmune response and the leading cause of death. However, with the development of supportive therapy, most patients can survive SIRS and develop the more complex immunosuppressive CARS5,6. The coexistence and reinforcement of SIRS and CARS inevitably lead to severe immune-related injury, which might be the basis of septic cardiomyopathy7.

The diagnosis of septic cardiomyopathy relies on clinical manifestations and auxiliary examinations. Myocardial dysfunction is a common clinical symptom and a potential indication of sepsis deterioration to septic cardiomyopathy8,9. As many as 20–65% of patients with sepsis have myocardial dysfunction, and more than 10% of sepsis patients eventually develop septic cardiomyopathy10. Although researchers have shown that early-stage myocardial dysfunction can be reversed11,12, it is still difficult to intervene in advance due to vague pathophysiological processes and a lack of accurate biomarkers13–15.

Considering the complexity of immune dysfunction and high mortality, this research aimed to explore the pathogenesis and immune landscape of septic cardiomyopathy, identify effective screening biomarkers, and establish prognostic risk signatures (RSs), which could provide the best clinical benefits in early-stage intervention and reduce the mortality of critical patients.

Methods and materials

Data acquisition and preprocessing

The microarray data of healthy, septic, and cardiomyopathic (dilated, ischemic, and septic) patients were downloaded from the GEO database. The diagnosis and complete follow-up information of all the patients were determined.

This process involved extracting the expression data from the raw data matrix. The probe ID and gene ID were matched according to the platform annotation file. The average value for a gene that has multiple probe IDs was taken. Data normalization based on the “SVA” and “limma” packages eliminated the batch effect between arrays to reform a multiarray expression matrix. The results are displayed as diagrams after being processed by the “boxplot” and “principal component analyses (PCA)” functions of the “ggplot2” package.

Screening of differentially expressed genes (DEGs)

Two methods were used to screen DEGs. Method 1: Differential analysis was conducted on each microarray, and the DEGs were identified according to robust rank aggregation (RRA). Method 2: Differential expression analysis was conducted on the multiarray expression matrix to obtain the DEGs. Finally, the “VennDiagram” package of R was used to cross-validate the results of the two methods to obtain the most biologically significant DEGs (logFCf ≥ 1, adj P value < 0.05).

Identification of hub genes

The DEGs were entered into STRING (https://cn.string-db.org/) to construct a protein‒protein interaction (PPI) network. The minimum required interaction score was set at 0.7, and disconnected nodes were hidden in the network.

The DEG interaction file and the DEG attribute file from STRING were input into Cytoscape to generate the DEG interaction diagram. Moreover, eight algorithms of cytoHubba, namely, DMNC (density of maximum neighborhood component), radial, closeness, ecCentricity, BottleNeck, degree, MNC (maximum neighborhood component), and betweenness, were used to evaluate the connectivity of DEGs. The top 50 DEGs of each algorithm were screened as the hub genes by the “UpSet” package of R.

Identification of hub gene-related transcription factors (TFs) and their regulatory network

Based on the Transcription Regulatory Relationships Unraveled Sentence-based Text mining (TRRUST) database16, enrichment analysis identified the TFs that could regulate the expression of the hub genes. The file of key regulators was input into Cytoscape to construct an interaction diagram between the hub genes and TFs. Additionally, the expression of TFs in septic cardiomyopathic hearts and healthy hearts was examined by differential expression analysis.

The biological function of hub genes and their effectiveness as biomarkers of septic cardiomyopathy

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to predict hub gene-related biological functions17–19. According to the default-weighted enrichment, the cutoff for the number of random combinations was set to 1000, the nominal P value was set to (NOM P-val) < 0.05 and the FDR was se to < 0.25.

Patients with healthy hearts and nonseptic cardiomyopathy patients were compared, so the area under the receiver operating characteristic (ROC) curve (AUC) ’was used to evaluate the effectiveness of the hub genes as biomarkers of septic cardiomyopathy.

Immune landscape in sepsis and septic cardiomyopathy

CIBERSORT was used to calculate the immune components in each sample. LASSO regression was used to screen for differentially expressed immunocytes in septic cardiomyopathy. The Pearson correlation coefficient was used to evaluate the correlation between the hub genes and immunocytes.

Results

Data acquisition and preprocessing

The GSE100159, GSE119217, GSE79962, GSE42955, and GSE171546 datasets were downloaded and normalized (Fig. 1A and D). The expression data were reformed into four new groups: the healthy group (40 samples), the nonseptic cardiomyopathy group (44 samples of dilated cardiomyopathy and ischemic cardiomyopathy), the sepsis group (114 samples), and the septic cardiomyopathy group (20 samples).

Fig. 1 Data processing. The data processing results for the GSE100159, GSE119217, GSE79962, and GSE42955 datasets are shown as boxplots, and PCA was performed. Before batch correction, the boxplot (A) shows the existence of the batch effect. PCA (B) indicates that there were four subgroups and that there was no significant correlation between them. After batch correction, the boxplot (C) and PCA (D) suggested that the batch effect and the subgroups between microarrays were eliminated.

Identifying the DEGs associated with sepsis and septic cardiomyopathy

Compared to the healthy group, 841 DEGs were identified by the RRA. Of these DEGs, 455 were downregulated and 386 were upregulated (Fig. 2A). Differential expression analysis of the multiarray expression matrix revealed 614 DEGs, of which 342 were downregulated and 272 were upregulated (Fig. 2B). After cross-verification by the “VennDiagram” package, 506 DEGs with the greatest biological significance for sepsis and septic cardiomyopathy were obtained. Of these DEGs, 270 were downregulated, and 236 were upregulated (Fig. 2C).

Fig. 2 Screening of DEGs. The RRA revealed 841 DEGs, 455 of which were downregulated and 386 of which were upregulated in sepsis and septic cardiomyopathy patients (A). Differential expression analysis of the batch-normalized multiarray expression matrix revealed 614 DEGs, of which 342 were downregulated and 272 were upregulated in sepsis and septic cardiomyopathy patients (B). Finally, 506 reliable DEGs were screened through cross-validation of the RRA and batch (C) methods.

Identification of the hub genes from the DEGs

After STRING screening, a PPI network containing 444 DEGs was constructed with Cytoscape (Supplemental Figs. 1–2). Moreover, the DEG interaction file (string_interactions_short.tsv) was obtained. CytoHubba identified 26 hub genes from 444 DEGs (Fig. 3A, Supplement Fig. 3). Compared to healthy and nonseptic cardiomyopathy patients, nine hub genes were differentially expressed in septic cardiomyopathy patients. BCL6, GAB2, MMP9, IL1R1, JAK3, IL6ST, and SERPINE1 were upregulated, and KLRG1 and PRF1 were downregulated (Fig. 3B and C). Temporal analysis based on the cecal ligation and puncture (CLP) model (GSE171546) further demonstrated the differential expression of SERPINE1, JAK3, IL1R1, MMP9, and BCL6 at certain time points (24, 48, and 72 h after surgery) (Fig. 3D).

Fig. 3 Identification of hub genes. CytoHubba identified 26 hub genes from 444 DEGs (A). A comparison of healthy (B) and nonseptic cardiomyopathy (C) patients revealed that KLRG1, PRF1, BCL6, GAB2, MMP9, IL1R1, JAK3, IL6ST, and SERPINE1 were differentially expressed in septic cardiomyopathy patients. Temporal analysis based on the cecal ligation and puncture (CLP) model (GSE171546) further demonstrated differential expression of SERPINE1, JAK3, IL1R1, MMP9, and BCL6 at certain time points (24, 48, and 72 h postoperatively) (D).

Hub gene-related TFs and their regulatory network

Based on the TRRUST database, enrichment analysis revealed a regulatory network formed by 9 TFs (SRF, STAT1, SP1, RELA, PPARG, NFKB1, PPARA, RELA, SMAD3, and STAT3) and four hub genes (SERPINE1, MMP9, BCL6, and IL1R1) (Fig. 4A). Compared with those in healthy control individuals and patients with nonseptic cardiomyopathy, the expression levels of SMAD3, PPARA, STAT3, and RELA were greater in patients with septic cardiomyopathy (Fig. 4B,C).

Fig. 4 The regulatory network of TFs and their differential expression analysis. The hub genes SERPINE1, MMP9, BCL6, and IL1R1 were regulated by 9 TFs, namely, SRF, STAT1, SP1, RELA, PPARG, NFKB1, PPARA, RELA, SMAD3, and STAT3 (A). Compared with those in healthy control individuals and patients with nonseptic cardiomyopathy, SMAD3, PPARA, STAT3, and RELA were differentially expressed in patients with septic cardiomyopathy (B-C).

The biological functions of the hub genes

GO analysis identified the top 10 biological processes enriched by the nine hub genes. These pathways included the regulation of the inflammatory response, regulation of the adaptive immune response, regulation of leukocyte-mediated immunity, regulation of cytokines involved in the immune response, regulation of immune effector processes, adaptive immune response based on somatic recombination of immune receptors built from immunoglobulin superfamily domains, myeloid cell differentiation, and negative regulation of T-helper cell differentiation (Fig. 5A-B). The immune-related signaling pathways included the Th17 cell differentiation pathway, JAK-STAT signaling pathway, and cytokine‒cytokine receptor interaction pathway (Fig. 5C). In addition, GeneMANIA predicted 20 coexpressed genes among the hub genes and their regulatory networks. Among them, 52.96% were coexpressed, 20.97% were associated with pathways, 12.2% were involved in physical interactions, 7.95% were involved in colocalization, and 5.92% were involved in genetic interactions. The hub genes and their coexpressed genes were mainly involved in the cellular response to interleukin-1, the adaptive immune response, the regulation of the adaptive immune response, immune receptor activity, the receptor signaling pathway via JAK-STAT, the negative regulation of the response to cytokine stimuli, and the regulation of cytokine-mediated signaling pathways (Fig. 5D, Supplement Material 1).

Fig. 5 GO, KEGG and GeneMANIA analyses. GO analysis suggested that the biological processes in which the hub genes were involved mainly in were the regulation of inflammatory and immune responses (A-B). KEGG analysis suggested that the immune-related signaling pathways activated by the hub genes included the Th17 cell differentiation pathway, JAK-STAT signaling pathway, and cytokine‒cytokine receptor interaction pathway (C). GeneMANIA predicted 20 coexpressed genes of the hub genes and their regulatory networks, which were also involved in immune regulation and inflammatory responses (D).

Hub genes regulate the immune landscape in septic cardiomyopathy

CIBERSORT and LASSO verified that immunocytes, including naive B cells, memory B cells, CD8 + T cells, naive CD4 + T cells, activated memory CD4 + T cells, γδ T cells, activated NK cells, M0/M1 macrophages, activated dendritic cells, resting mast cells, and neutrophils (Fig. 6), were differentially expressed between sepsis and septic cardiomyopathy patients and healthy and nonseptic cardiomyopathy patients, (Fig. 7A-C). Correlation analysis revealed that the expression of BCL6 was related to naive B cells and resting mast cells; that of JAK3 was related to memory B cells. The expression of KLRG1 was related to resting mast cells and CD8 + T cells. That of MMP9 was related to resting mast cells, M0 macrophages, and CD8 + T cells; and that of PRF1 was related to resting mast cells, activated memory CD4 + T cells, and neutrophils (Fig. 7D-N).Fig. 6 The evaluation of hub genes as biomarkers for septic cardiomyopathy. Between healthy control individuals and patients with septic cardiomyopathy, the AUC of the 9 hub genes was greater than 0.7 (A), and the AUC of the diagnostic model based on the hub genes was 0.972 (B). The AUCs of the 9 hub genes between nonseptic cardiomyopathy and septicemic cardiomyopathy patients were greater than 0.6 (C), and the AUC of the diagnostic model based on the hub genes was 0.945 (D).

Fig. 7 Hub genes regulate the immune landscape in septic and septic cardiomyopathy patients. CIBERSORT and LASSO verified that compared with those in healthy people and nonseptic cardiomyopathy patients, immunocytes, including naive B cells, memory B cells, CD8 + T cells, naive CD4 + T cells, activated memory CD4 + T cells, γδ T cells, activated NK cells, M0/M1 macrophages, activated dendritic cells, resting mast cells, and neutrophils, were differentially expressed between septic and septic cardiomyopathy patients (A-C). Correlation analysis revealed that the expression of BCL6 was related to naive B cells (D) and resting mast cells (E), that JAK3 expression was related to memory B cells (F), that KLRG1 expression was related to resting mast cells (G) and CD8 + T cells (H), that MMP9 expression was related to resting mast cells (I), M0 macrophages (J), and CD8 + T cells (K), and that PRF1 expression was related to resting mast cells (L), activated memory CD4 + T cells (M), and neutrophils (N).

Hub gene-based prognostic RS signature

Based on the nine hub genes, two groups of ROCs were established to test their sensitivity and specificity as biomarkers of septic cardiomyopathy. The AUCs of the 9 hub genes were greater than 0.7 between healthy control individuals and patients with septic cardiomyopathy (Fig. 6A) and were greater than 0.6 between patients with nonseptic cardiomyopathy and patients with septic cardiomyopathy (Fig. 6C). Moreover, the AUC of the diagnostic model in these two groups was greater than 0.9 (Fig. 6B,D). All the results indicated that the hub genes were reliable biomarkers of septic cardiomyopathy.

Discussion

Despite progress in diagnosis and emergency resuscitation, septic cardiomyopathy is still one of the most common fatal diseases in the world. Previous studies have shown that, compared with the 20% sepsis mortality rate, the mortality rate of septic cardiomyopathy can reach 70−90%. Considering that cardiac dysfunction in the early stage of sepsis (7–10 days) is reversible, preventative intervention is essential for reducing mortality20–22. Therefore, the identification of effective biomarkers of the progression of sepsis to septic cardiomyopathy is urgently needed.

First, we sought to identify early biomarkers of septic cardiomyopathy. Using healthy myocardium, dilated cardiomyopathy, and ischemic cardiomyopathy samples as control samples, multimicroarray analysis identified 9 pivotal genes related to septic cardiomyopathy. Of these genes, BCL6, GAB2, MMP9, IL1R1, JAK3, IL6ST, and SERPINE1 were upregulated, and KLRG1 and PRF1 were downregulated. Temporal analysis based on the cecal ligation and puncture (CLP) model further demonstrated the differential expression of SERPINE1, JAK3, IL1R1, MMP9, and BCL6 at certain time points (24, 48, and 72 h postoperative). ROC analysis verified the sensitivity and specificity of the 9 hub genes as biomarkers of septic cardiomyopathy. Furthermore, 20 coexpressed genes and their mutual regulatory network were identified based on GeneMANIA, and 9 TFs that could regulate the expression of hub genes were identified based on TRRUST.

Furthermore, GO and KEGG analyses indicated that the nine hub genes were involved in the regulation of inflammatory responses, the regulation of cytokines involved in immune responses, and the regulation of immune effector processes through immune-related signaling pathways, such as the Th17 cell differentiation pathway, the JAK-STAT signaling pathway, and the cytokine‒cytokine receptor interaction pathway. These conclusions are supported by GeneMANIA.

Moreover, as an inevitable immune response in septic cardiomyopathy, the coexistence of SIRS and CARS can cause immune dysfunction and uncontrolled immune-related injuries7,23. Therefore, another key goal was to clarify the immune landscape of septic cardiomyopathy. In our study, CIBERSORT and ESTIMATE suggested that the numbers of neutrophils, activated memory CD4 + T cells, naive B cells, memory B cells, CD8 + T cells, and naive CD4 + T cells changed regularly during the progression from sepsis to septic cardiomyopathy, which deserves further attention.

The rapid release of neutrophils reflects the anti-inflammatory effect of innate immunity on early-stage sepsis24. Regulated by programmed cell death, neutrophils have a shorter life span; this process is also a necessary mechanism for inhibiting the inflammatory response and maintaining immune homeostasis. However, the progression of sepsis can inhibit the apoptosis of neutrophils and the massive migration of neutrophils to organs, resulting in immune-related injury and immunosuppression25. This might explain the results revealed by CIBERSORT, which indicated that the increase in neutrophils in septic cardiomyopathy patients is most significant in the expression profile of immune cells. Naive B cells are mature B cells that are not activated by antigens, and their role in sepsis is still vague. Our study also revealed that the expression profile of memory B cells was significantly reduced in septic cardiomyopathy patients. Memory B cells are produced in the germinal center during the T-cell-dependent immune response and play an essential role in the secondary immune response to eliminate reinfection. However, a prospective cohort study showed that memory B cells were selectively depleted in patients with advanced sepsis, resulting in the immune system not responding to reinfection26. Naive CD4 + T cells stay in the thymus until they are stimulated by antigen-presenting cells (APCs) and transform into helper T (Th) cells. Under persistent inflammation, some Th cells or regulatory T cells (Tregs) transform into memory CD4 + T cells, which can be activated rapidly during reinfection. CD8 + T cells are cytotoxic T lymphocytes (CTLs) transformed from naive CD8 + T cells. Its ability to kill pathogens could lead to autoimmune diseases27,28.

In conclusion, the increase in the abundances of neutrophils and activated memory CD4 + T cells manifested proinflammatory effects. In contrast, the decrease in the abundances of memory B cells, CD8 + T cells, and naive CD4 + T cells was associated with anti-inflammatory effects. These findings further confirmed that sepsis-related cardiomyopathy involves immune-related organ injury.

Based on LASSO regression, we analyzed the regulatory effect of the hub genes on the immune landscape, and the results showed that the expression of BCL6, JAK3, KLRG1, MMP9, and PRF1 was related to neutrophils, naive B cells, memory B cells, CD8 + T cells, and naive CD4 + T cells. Furthermore, the immune-related signaling pathways activated by the hub genes (IL1R1, JAK3, IL6ST, and MMP9) and TFs (STAT1 and STAT3) included the Th17 cell differentiation pathway, JAK-STAT signaling pathway, and cytokine‒cytokine receptor interaction pathway. Moreover, the biological processes associated with the 9 hub genes and their coexpressed genes included the regulation of the inflammatory response, adaptive immune response, leukocyte and cytokine-mediated immunity, immune effector process, myeloid cell differentiation, and negative regulation of T-helper cell differentiation.

Previous studies have shown that JAK3 is involved in cytokine receptor-mediated intracellular signal transduction. It is predominantly expressed in marrow cells, thymocytes, NK cells, activated B cells, and activated T cells. JAK3 also transduces a signal in response to its activation via tyrosine phosphorylation by interleukin receptors29. STAT3 is the substrate and downstream signaling molecule of the JAK kinase family and is responsible for regulating gene transcription and signal transduction30. IL1R1 belongs to the interleukin-1 receptor family and is a significant mediator of cytokine-induced immune responses. IL6ST is a signal transducer shared by many cytokines, such as IL6, CNTF, LIF, and OSM31. Knockout studies in mice suggest that IL6ST is critical for regulating myocyte apoptosis32.

MMP9 was an exceptional hub gene that was highly expressed only in septic cardiomyopathy patients. Previous research has shown that MMP9 regulates the release of cytokines and strengthens the chemotaxis of neutrophils to regulate inflammatory reactions and coagulation cascades during early-stage sepsis33,34. In our study, the biological processes in which MMP9 participated were the regulation of the inflammatory response and myeloid cell differentiation, and CD8 + T cells were related to the expression of MMP9.

In summary, sepsis-induced cardiomyopathy is characterized by immune-related myocardial injury after severe pro- and anti-inflammatory conditions. KLRG1, PRF1, BCL6, GAB2, MMP9, IL1R1, JAK3, IL6ST, and SERPINE1 are hub genes related to abnormally expressed neutrophils, activated memory CD4 + T cells, naive B cells, memory B cells, CD8 + T cells, and naive CD4 + T cells in septic cardiomyopathy. These 9 hub genes could serve as biomarkers of septic cardiomyopathy and should be further verified by in vitro and in vivo experiments in the future.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1.

Supplementary Material 2.

Supplementary Material 3.

Supplementary Material 4.

Supplementary Material 5.

Abbreviations

KLRG1 Killer cell lectin like receptor G1

PRF1 Perforin 1

BCL6 BCL6 transcription repressor

GRB2 GRB2 associated binding protein 2

MMP9 Matrix metallopeptidase 9

IL1R1 Interleukin 1 receptor type 1

JAK3 Janus kinase 3

IL6ST Interleukin 6 cytokine family signal transducer

SERPINE1 Serpin family E member 1

SRF Serum response factor

STAT1 Signal transducer and activator of transcription 1

Sp1 Sp1 transcription factor

RELA RELA proto-oncogene

PPARG Peroxisome proliferator activated receptor gamma

NFKB1 Nuclear factor kappa B subunit 1

PPARA Peroxisome proliferator activated receptor alpha

SMAD3 SMAD family member 3

STAT3 Signal transducer and activator of transcription 3

Th Helper T

ICU Intensive care unit

SIRS Systemic inflammatory response syndrome

CARS Compensatory anti-inflammatory response syndrome

RS Risk score

GEO Gene expression omnibus

ID Identity document

PCA Principal component analysis

DEGs Differentially expressed genes

RRA Robust rank aggregation

PPI Protein-protein interaction

DMNC Density of maximum neighborhood component

TFs Transcription factors

TRRUST Transcription regulatory relationships unraveled sentence-based text mining

GO Gene ontology

KEGG Kyoto Encyclopedia of Genes and Genomes

NOM P-val Nominal P-values

ROC Receiver operating characteristic curve

AUC Area under curve

APCs Antigen-presenting cells

Tregs Regulatory T cells

CTL Cytotoxic T lymphocytes

NK Natural killer

IL6 Interleukin-6

CNTF Ciliary neurotrophic factor

LIF Leukemia inhibitory factor

OSM OncoStatin M

Acknowledgements

We also acknowledge the valuable suggestions given by Dr. Shi Hua of the Department of Neurosurgery, Linyi People’s Hospital.

Funding

This study was supported by grants from the National Natural Science Foundation of China, China (no. 81860335).

Data availability

The GSE100159, GSE119217, GSE79962, and GSE42955 datasets were obtained from the GEO database (https://www.ncbi.nlm.nih.gov/geo). The data used to support the findings of this study are included in the article.

Declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

Ethical approval was not required because the data came from publicly available databases.

Consent for publication

All authors of the manuscript have read and agreed to its content and are accountable for all aspects of the accuracy and integrity of the manuscript in accordance with ICMJE criteria. This article is original, has not already been published in a journal and is not currently under consideration by another journal. All authors agree with the BioMed Central Copyright and License Agreement terms.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

These authors contributed equally: Wenli Li and Shi Hua.
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