Abstract

Objective

   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 [46]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 [47]GSE13904 (children) and
   [48]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 ([49]GSE13904, [50]GSE25504) and
   adults ([51]GSE9960, [52]GSE154918). Finally, we analyzed differences
   in DNA methylation levels between sepsis patients and controls in
   [53]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 [54]GSE13904 (children) and [55]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.

   Keywords: sepsis, septic shock, bioinformatics, diagnosis,
   immunosuppression

Introduction

   Sepsis is defined as life-threatening organ dysfunction caused by a
   dysregulated host response to infection.[56]^1 In 2017, approximately
   48.9 million sepsis cases and 11.0 million sepsis-related deaths were
   recorded worldwide, accounting for 19.7% of all global deaths.[57]^2
   According to a recently published systematic review, sepsis with organ
   dysfunction occurring during hospitalization affects 24.4% of all
   patients in intensive care units and neonatal intensive care
   units.[58]^3 Septic shock is defined as sepsis complicated by
   hyperlactatemia and simultaneous hypotension requiring vasopressin
   therapy. The in-hospital mortality rate of septic shock is
   30–50%.[59]^4

   The World Health Organization states that the annual number of deaths
   due to sepsis worldwide is approximately 6 million, most of which are
   preventable.[60]^5 The identification of altered molecular signatures
   and biochemical pathways in sepsis patients has driven interest in the
   discovery of novel biomarkers.[61]^6 Early recognition of sepsis is
   essential for timely treatment and can improve sepsis
   outcomes.[62]^7^,[63]^8 Mortality risk is higher when sepsis treatment
   is delayed.[64]^9 Analyzing the molecular characteristics of patients
   is an effective method to screen for potential diagnostic and
   prognostic targets.[65]^10

   Patients with sepsis often present acquired immunodeficiency.[66]^11
   Significant lymphopenia has been reported in sepsis patients, and
   apoptosis may be a major mechanism of lymphocyte death in
   sepsis.[67]^12 A number of anti-inflammatory and immunostimulatory
   agents are currently in clinical trials for sepsis and septic
   shock.[68]^13^,[69]^14 At the same time, sepsis may result in prolonged
   methylation changes in monocytes, which may help stabilize them in an
   immunotolerant state.[70]^15 Multiple studies have shown that
   epigenetic alterations in sepsis may affect prognosis and serve as
   diagnostic biomarkers.[71]^16^,[72]^17

   Improved infection prevention and control strategies are urgently
   needed to reduce the health care burden associated with the development
   and progression of sepsis and septic shock, as are approaches to early
   diagnosis and appropriate treatment. The present study used
   bioinformatics to explore biomarkers and potential therapeutic targets
   associated with the development of sepsis.

Materials and Methods

Sepsis Data Collection

   Data were downloaded from the Gene Expression Omnibus (GEO) database
   ([73]https://www.ncbi.nlm.nih.gov/geo/). The [74]GSE13904 dataset
   included array-based gene expression profiles of whole blood from 18
   normal children, 52 children with sepsis, and 106 children with septic
   shock. The [75]GSE25504 dataset included array-based gene expression
   profiles of whole blood from 37 control and 25 infected human neonates.
   The [76]GSE9960 dataset included array-based gene expression profiles
   of peripheral blood mononuclear cells from 54 adult sepsis patients and
   16 controls. The raw data in these three datasets were processed using
   the lumi package in R.[77]^18 The [78]GSE154918 dataset included gene
   expression profiles based on high-throughput sequencing of peripheral
   blood from 65 adult sepsis patients and 40 controls. Raw counts were
   pre-processed and normalized using DESeq2.[79]^19

Coexpression Analysis

   The variance of gene expression in [80]GSE13904 (children) was first
   calculated, and the top 20% of the greatest variance was screened to
   construct a coexpression network through weighted gene coexpression
   network analysis (WGCNA) using the WGCNA package in R.[81]^20 The
   connections among different pairs of genes were identified and weighted
   based on the correlated expression levels across patients and control
   samples. Then the adjacency matrix was converted into a topological
   overlap matrix (TOM) to detect gene connectivity in the network. Genes
   were separated into different clusters (modules) based on their
   connectivity and covariance coefficients, then hierarchically
   clustered. The WGCNA package was employed to test the independence and
   average connectivity of different modules under different power values,
   and the power values corresponding to an independence index of R^2 =
   0.9 were selected. The minimum size of the gene dendrogram was 30.
   Potential correlations between modules and clinical characteristics of
   patients were explored through Pearson correlation analysis.

Difference Analysis

   Gene expression profiles were constructed for each sample of septic
   shock patients, sepsis patients and controls. Differential analysis
   between sepsis patients and healthy individuals in [82]GSE13904
   (children) or [83]GSE154918 (adults) was performed after using the
   limma package in R.[84]^21 A screening threshold of P < 0.05 was set to
   obtain the differentially expressed genes (DEGs) between sepsis
   patients and controls. Then, DEGs whose expression differed in the same
   direction in patients (up- or down-regulation) were screened for
   intersection analysis with module genes to obtain the common genes.
   DEGs between septic shock patients and sepsis patients in [85]GSE13904
   (children) was obtained using the limma package in R and a significance
   criterion of P < 0.05.

Enrichment Analysis

   We used the clusterProfiler package in R[86]^22 to perform gene
   ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG)
   pathway enrichment analysis of common genes. P < 0.05 was considered
   statistically significant for enrichment. The significantly enriched
   terms were subjected to gene set variation analysis (GSVA)[87]^23 to
   calculate the score for the enrichment using the gene expression
   profile. The up-regulation or down-regulation of enrichment terms in
   septic patients relative to controls was evaluated using the GSVA score
   in the limma package in R. Gene set enrichment analysis (GSEA) was used
   to identify KEGG pathway using GSEA software. The fgsea package in R
   was used to display the results of the GSEA.

Single Sample Gene Set Enrichment Analysis (ssGSEA)

   The marker gene sets for immune cell types were obtained from Bindea et
   al[88]^24. The gene signatures expressed by the immune cell populations
   were quantified using the single-sample gene set enrichment analysis
   (ssGSEA) function in GSVA. Differences in immune cell infiltration
   between sepsis patients and healthy controls were then calculated, and
   a significance criterion of P < 0.05 was applied. Correlations between
   levels of infiltration by different types of immune cells were
   calculated using Pearson’s correlation, based on ssGSEA scores for the
   immune cell types.

PPI Network Analysis

   The common genes were analyzed using the online tool STRING
   ([89]https://string-db.org), and the protein-protein interaction (PPI)
   network based on the criterion of a combined score > 900 was
   constructed and displayed using Cytoscape software. Genes were ranked
   based on their degree of connectivity with other genes. The pROC
   package in R[90]^25 was used to calculate the the area under the
   receiver operating characteristic curve (AUC) for the top 100 genes
   showing the greatest connectivity in the PPI network.

Short Time-Series Expression Miner (STEM) Analysis

   We performed STEM analysis to cluster the common genes in healthy
   individuals, sepsis patients and septic shock patients in the
   [91]GSE13904 database (children).[92]^26 Significant clustering was
   defined as P < 0.05. The significantly clustered genes showed a trend
   of gradual up- or down-regulation as one moved from controls to sepsis
   patients and then to septic shock patients.

Methylation Analysis

   The [93]GSE138074 dataset contains array-based gene methylation
   profiles of monocytes from 14 adult septic patients and 11 healthy
   donors. Data were processed using the ChAMP package in R.[94]^27
   Differences in methylation levels between sepsis patients and healthy
   donors were calculated using the limma package in R.

Results

Coexpression Network of Sepsis-Related Genes

   The flow chart of this study is shown in [95]Figure 1. We calculated
   variance of gene expression in [96]GSE13904 (children) and then
   screened 4093 genes in the top 20% greatest variance genes in order to
   identify coexpression patterns. WGCNA identified a coexpression network
   containing 2550 genes ([97]Figure 2A). These genes clustered into eight
   coexpression modules: the brown module had the strongest positive
   correlation with sepsis shock, whereas the pink module had the
   strongest negative correlation with sepsis shock ([98]Figure 2B). By
   calculating the expression profiles of the modules in different
   clinical samples, we identified the expression trend for each module in
   sepsis development ([99]Figure 2C). The brown module showed gradual
   up-regulation, while the pink module showed gradual down-regulation, in
   the order: controls < patients with sepsis < patients with septic
   shock.

Figure 1.

   [100]Figure 1
   [101]Open in a new tab

   Flowchart of this study. The following datasets were used for the
   identification of potential diagnostic genes and mechanisms associated
   with the development of sepsis: [102]GSE13904 (children), [103]GSE25504
   (children), [104]GSE9960 (adults) and [105]GSE154918 (adults).

   Abbreviations: AUC, area under the receiver operating characteristic
   curve; GSVA, gene set variation analysis; PPI, protein-protein
   interaction; STEM, short time-series expression miner; WGCNA, weighted
   gene coexpression network analysis.

Figure 2.

   [106]Figure 2
   [107]Open in a new tab

   WGCNA of genes showing large expression variance in sepsis patients.
   (A) Network heatmap of unsupervised cluster analysis of module genes.
   Different colors in columns and rows represent different modules. (B)
   Correlation of modules with clinical traits. Each column represents a
   different module; each row, a different clinical phenotype. Red
   indicates positive correlation; blue, negative correlation. (C)
   Progressive up- or down-regulation in a module in the trend: healthy
   controls < sepsis < sepsis shock.

DEGs in Sepsis

   Of the four sets of sepsis data that we obtained, [108]GSE13904
   (children) and [109]GSE154918 (adults) had the largest samples
   ([110]Figure 3A). In [111]GSE13904 (children), we obtained 7850 DEGs
   between sepsis and control samples ([112]Figure 3B, [113]Table S1). In
   [114]GSE154918 (adults), we obtained 12496 DEGs between sepsis and
   control samples ([115]Figure 3B, [116]Table S2). By comparing the DEGs
   between adult and pediatric sepsis, we found 5638 genes that were
   common to both, 2212 that may be specific to the pediatric condition
   and 6857 that may be specific to the adult condition ([117]Figure 3C).
   Genes at the intersection may be strongly associated with sepsis in
   adults and children. In addition, we screened 5143 DEGs that were
   simultaneously up- or down-regulated in [118]GSE13904 (children) and
   [119]GSE154918 (adults). Intersection analysis of these genes with
   module genes yielded 1274 common genes for subsequent analysis
   ([120]Figure 3D).

Figure 3.

   [121]Figure 3
   [122]Open in a new tab

   Identification of common genes in sepsis. (A) Petal plot of sample size
   for sepsis and control groups in four datasets. Red petals represent
   healthy controls; blue petals, sepsis patients. (B) Manhattan plot of
   differentially expressed genes between sepsis patients and healthy
   controls in [123]GSE13904 and [124]GSE154918. Up, up-regulation in
   sepsis; Down, down-regulation in sepsis; Not, no significant difference
   from controls. The three genes in each group showing the largest fold
   difference are labeled. (C) Venn diagram of differentially expressed
   genes in [125]GSE13904 (children) and [126]GSE154918 (adults). (D) Venn
   diagram showing DEGs in [127]GSE13904 (children) and [128]GSE154918
   (adults) as well as module genes, that are common to adults and
   children or specific to each one of those groups.

Biological Functions of Selected Genes

   Enrichment analysis showed that, based on the common genes identified
   above, defensive responses to bacteria, complement-dependent
   cytotoxicity, and canonical glycolysis were significantly up-regulated
   in sepsis patients, whereas interleukin-17 production, B cell
   activation, and T cell receptor signaling were significantly
   down-regulated ([129]Figure 4A). KEGG results showed that
   Hypoxia-Inducible Factor 1 (HIF-1) signaling pathway, Tumor Necrosis
   Factor (TNF) signaling pathway, and glycolysis/gluconeogenesis were
   significantly up-regulated in sepsis, whereas Th1 and Th2 cell
   differentiation, Th17 cell differentiation, and T cell receptor
   signaling pathways were significantly down-regulated ([130]Figure 4B).
   KEGG results in GSEA similarly found that metabolism-related pathways
   were up-regulated while immune related pathways were down-regulated
   ([131]Figure 4C).

Figure 4.

   [132]Figure 4
   [133]Open in a new tab

   Biological functions and KEGG pathways enriched in common genes. (A)
   Common genes of sepsis patients relative to controls involved in up- or
   down-regulated biological processes, as quantified by gene set
   variation analysis (GSVA). FC, fold change. (B) Common genes of sepsis
   patients relative to controls involved in up- or down-regulated KEGG
   pathways, as quantified by single-sample GSVA. FC, fold change. (C)
   Common genes involved in up- or down-regulated KEGG pathways of GSEA
   results in sepsis patients relative to controls. P < 0.05 was
   considered statistically significant.

Immune Cell Infiltration in Sepsis

   In the enrichment results, we found that a large number of
   immune-related biological functions were significantly down-regulated
   in sepsis samples. Therefore, we assessed immune cell infiltration in
   sepsis patients ([134]Figure 5A). Unsurprisingly, sepsis was associated
   with significantly reduced infiltration by most types of immune cells,
   except that infiltration by macrophages, mast cells, and neutrophils
   was elevated. Cytotoxic T cell types were defined based on gene
   markers, and CD8+ T cells were treated as one subtype of T cells with
   different gene markers than other subtypes.[135]^24 The ssGSEA scores
   for these different subtypes were used to generate immune cell
   interaction networks. We grouped the differentially infiltrated immune
   cells into four clusters ([136]Figure 5B).

Figure 5.

   [137]Figure 5
   [138]Open in a new tab

   Immune cell suppression in sepsis patients. (A) Differences in immune
   cell infiltration between sepsis and controls. Each row represents a
   type of immune cell; each column, a different dataset. Red nodes
   represent significant up-regulation in sepsis; blue nodes, significant
   down-regulation. (B) Clustering and correlation of immune cell types
   based on infiltration levels. The size of each node represents the
   single-sample gene set enrichment analysis (ssGSEA) scores of each
   immune cell type, transformed by log[10] (Log rank test P-value).
   Connections between immune cell types represent interactions between
   the two. The thickness of the line indicates the strength of the
   correlation, based on Pearson correlation analysis. Red lines represent
   positive correlations; blue lines, negative correlations. Immune cell
   cluster A is shown in yellow; cell cluster B, blue; cell cluster C,
   red; and cell cluster D, brown.

PPI Network of Common Genes

   In addition, we performed PPI network analysis of the common genes and
   ranked the connectivity degree between the genes in the network
   ([139]Figure 6A). We screened the top 100 genes with the greatest
   connectivity ([140]Figure 6B). By calculating the AUC values of these
   genes in [141]GSE13904 (children) and [142]GSE154918 (adults), we
   identified genes with AUC values greater than 0.9 in both sets of data,
   which we defined as key genes ([143]Figure 6C). Among them, MAPK14,
   FGR, RHOG were up-regulated in sepsis, while LAT, PRKACB, UBE2Q2, ITK,
   IL2RB, and CD247 were down-regulated ([144]Figure 6D). Importantly, the
   patterns of differential expression of these key genes were consistent
   across [145]GSE13904, [146]GSE154918, [147]GSE25504, and [148]GSE9960.
   Correlation analysis found that up-regulated genes had stronger
   negative correlation with immune cells, while down-regulated genes had
   stronger positive correlation with immune cells, especially T cells
   ([149]Figure 6E).

Figure 6.

   [150]Figure 6
   [151]Open in a new tab

   Identification of potential diagnostic genes for sepsis. (A) PPI
   network of common genes (top 100 with highest degree of connection).
   Darker color indicates higher degree of connectivity. (B) Heatmap of
   the top 100 genes based on degree of connectivity in the PPI network.
   Data from sepsis patients are shown in yellow; sepsis shock patients,
   green; and controls, brown. (C) Genes with areas under the receiver
   operating characteristic curve (AUCs) greater than 0.9 in [152]GSE13904
   (children) and [153]GSE154918 (adults). Red represents genes with
   up-regulated expression in sepsis; blue, genes with down-regulated
   expression. The lengths of the bars represent the mean AUC values of
   the genes. (D) Differential expression of key genes between sepsis
   patients and controls. Red nodes represent genes with up-regulated
   expression in sepsis; blue nodes, genes with down-regulated expression;
   grey nodes, no significant difference from controls. (E) Correlation
   between key genes and immune infiltrating cells, based on Pearson
   correlation analysis. Red nodes represent positive correlations; blue
   nodes, negative correlations. *P < 0.05, **P < 0.01.

Genes Associated with Sepsis Development

   To identify the change in gene expression during the course of
   development from sepsis to septic shock, we utilized STEM software to
   identify 712 common genes showing progressive dysregulation during
   sepsis progression. These genes fell into five significant clusters
   ([154]Figure 7A). Interestingly, all the key genes that we identified
   above were detected in these clusters ([155]Figure 7B). The expression
   of MAPK14, FGR, RHOG was gradually up-regulated from control to sepsis
   then to septic shock, while the expression of LAT, PRKACB, UBE2Q2, ITK,
   IL2RB, and CD247 was gradually down-regulated ([156]Figure 7C). The
   pathway heatmap built by GSEA algorithm showed that starch and sucrose
   metabolism, complement and coagulation cascades, and legionellosis were
   gradually up-regulated during the development of sepsis, while Th1 and
   Th2 cell differentiation, IgA production in the intestinal immune
   network, as well as antigen processing and presentation were gradually
   down-regulated ([157]Figure 7D).

Figure 7.

   [158]Figure 7
   [159]Open in a new tab

   Identification of persistently dysregulated genes during progression
   from sepsis to septic shock. (A) Heatmap of persistently dysregulated
   genes identified by STEM in the trend: healthy controls < sepsis
   patients < septic shock patients. Gene sets were arranged based on
   cluster assignment in order to generate simplified expression profiles.
   We graphically depicted only five clusters with >10 genes. (B) The box
   plots of STEM genes in five clusters. Line plots and box plots were
   used to display log[2] (fold change). Representative genes were
   highlighted using red lines. Key genes were in significant STEM
   clustering and located on the right side of the box map. *P < 0.05, **P
   < 0.01. (C) Expression of key genes were persistently elevated during
   sepsis development. (D) Signaling pathways persistently up- or
   down-regulated as sepsis develops. Red in the heatmap represents an
   enrichment score greater than 0; blue represents an enrichment score
   less than 0.

Methylation Marks in Sepsis

   To identify the sepsis key genes whose methylation may be modified in
   the disease, we first analyzed differentially methylated positions
   (DMPs) between sepsis samples and controls in [160]GSE138074 (adult)
   ([161]Figure 8A). DMPs with delta beta values in the opposite direction
   to the expression value of common DEGs were filtered and identified as
   methylation marks. A total of 1313 methylation marks were identified
   ([162]Figure 8B). These included MAPK14, FGR, and CD247. The
   methylation levels of MAPK14 (cg18213931) and FGR (cg16922167) were
   lower in sepsis than control, and that of CD247 (cg21161394) was higher
   in sepsis ([163]Figure 8C).

Figure 8.

   [164]Figure 8
   [165]Open in a new tab

   Screening of key methylation markers for sepsis in [166]GSE138074. (A)
   Differentially methylated positions (DMPs) between sepsis and controls.
   Of all DMPs, 49.58% were hypermethylated and 50.42% were
   hypomethylated. The 10 sites with the highest methylation levels are
   labeled. Each feature color represents a different methylation site.
   (B) The methylation levels and the expression levels of methylation
   marks. Key genes are labeled. (C) Differences in key gene methylation
   levels between sepsis and controls. P for CD247 was 0.039; MAPK14,
   0.047; FGR, 0.047.

Discussion

   Early recognition of sepsis is key to timely treatment. In this study,
   we subjected sepsis-related sequencing data to bioinformatics analysis
   in order to explore the sepsis-related molecular dysregulation
   mechanisms. Compared to other studies using the same
   datasets,[167]^28^,[168]^29 the present work used WGCNA and PPI
   networks to identify potential diagnostic markers for sepsis not only
   in adults but also in children. We further identified potential
   biomarkers associated with septic shock as well as methylation
   modification status. We found that in patients with sepsis, multiple
   metabolic pathways were significantly up-regulated, whereas
   infiltration by immune cells was significantly decreased. We identified
   nine potential biomarkers associated with sepsis progression. In
   addition, we found evidence that the expression of MAPK14, FGR, and
   CD247 is modified by methylation, which may facilitate the diagnosis of
   sepsis and septic shock.

   Based on analysis of variance, WGCNA and differential expression
   analysis, we identified genes that may be associated with sepsis and
   septic shock. Enrichment analysis revealed that the genes involved in
   inflammatory and metabolic responses were up-regulated in sepsis,
   whereas the genes corresponding to immune responses were significantly
   suppressed. This is similar to previous findings.[169]^30 Increased
   galactosylation of the crystallizable immunoglobulin G fragment has
   been linked to increased complement-dependent cytotoxicity, which in
   turn may contribute to pediatric sepsis.[170]^31 In patients with
   sepsis, increased levels of both C3a and C5a in serum have been
   reported.[171]^32 Monocytes from sepsis patients have significantly
   higher basal glycolysis than monocytes from healthy controls.[172]^33
   Aerobic glycolysis has been found to contribute to sepsis development
   by regulating inflammasome activation in macrophages.[173]^34 Both
   bacterial and fungal sepsis have been observed to cause a shift in
   cellular metabolism towards glycolysis, which is associated with an
   impaired ability of leukocytes to produce pro-inflammatory cytokines
   upon a second stimulus.[174]^35 Moreover, lymphocyte loss, equally
   affecting B and T cells, was demonstrated in septic
   patients.[175]^36^,[176]^37 Accumulating evidence supports that
   immunosuppression is one of the major contributors to sepsis-related
   mortality, and that T cell exhaustion is one of the most severe
   responses.[177]^38^,[178]^39

   Consistent with our KEGG enrichment results, HIF-1α contributes to the
   pathogenic role of macrophages in sepsis.[179]^40^,[180]^41 HIF-1α
   promotes the expression of multiple gene products, including enzymes
   that promote glycolysis.[181]^42 Some studies consider HIF as a
   potential biomarker of sepsis, but this remains controversial.[182]^43
   Tumor necrosis factor α (TNF-α) contributes to sepsis immunosuppression
   by increasing apoptosis.[183]^44 TNF-α levels in plasma increase
   progressively as sepsis progresses, with the highest values found in
   patients with septic shock. This finding supports a prognostic
   biomarker role for this cytokine.[184]^45 Th17 cytokine production is
   reduced in sepsis, which may adversely affect long-term
   mortality.[185]^46 Treatment with IL-7 increases the responsiveness of
   Th17 cells and reduces the mortality of secondary fungal infections,
   making IL-17 a potential therapeutic agent.[186]^47 A ratio of Th2 to
   Th1 cells are clearly up-regulated in severe sepsis patients, and their
   continuous dynamic elevation is associated with acquired infection and
   28-day mortality in the intensive care unit.[187]^48

   Patients with sepsis have impaired innate and adaptive antibacterial
   immunity, which renders them unable to control primary and secondary
   infections.[188]^49 Our analysis of immune cell infiltration in sepsis
   patients showed that the levels of infiltration of some innate immune
   cells, such as macrophages and mast cells, were significantly higher.
   The host defends against pathogens by mounting a systemic inflammatory
   response in innate immune cells, such as macrophages, which produce
   pro-inflammatory cytokines and chemokines and which initiate
   coagulation cascade within minutes.[189]^50 In our clustering of immune
   cell types based on differential infiltration, we identified four
   clusters. Each is characterized by unique immune responses that may
   influence the disease state of the patient.[190]^51

   On the other hand, we found 9 key genes that may influence the
   development of sepsis. Among them, MAPK14, FGR, and CD247 expression
   may be modified by methylation. MAPK14 has already been identified as a
   key gene and potential therapeutic target in neonatal
   sepsis.[191]^52^,[192]^53 Deficiency of MAPK14 in macrophages protects
   mice from lipopolysaccharide (LPS)-induced sepsis.[193]^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.[194]^55 FGR ^−/− mice have leukocyte migration disorder, and
   FGR is involved in the release of proinflammatory mediators.[195]^56
   Studies have shown that CD247 is a potential new biomarker for sepsis
   and may be beneficial for the prognosis of patients with
   sepsis.[196]^57^,[197]^58 CD247 is involved in the immune response to
   sepsis caused by Staphylococcus aureus infection.[198]^59

   For the other key genes, RHOG has already been linked to sepsis through
   its participation in inflammatory processes.[199]^60 The amount of LAT
   on the cell surface may determine the extent of T cell
   activation.[200]^61 Systemic inflammation in patients with acute sepsis
   causes persistent T cell dysfunction, which leads to
   immunosuppression.[201]^37 PRKACB is differentially expressed in
   neonatal sepsis, and it may influence disease progression via the MAPK
   signaling pathway.[202]^62^,[203]^63 ITK has been found to regulate
   thermal homeostasis during sepsis via its effects on mast
   cells.[204]^64 Previous work has found that IL2RB is down-regulated in
   sepsis, which is consistent with our study,[205]^65 and that IL2RB is
   negatively correlates with organ failure and mortality in
   sepsis.[206]^66 Our results reveal UBE2Q2 as a potential key gene for
   sepsis diagnosis, and we are unaware of previous studies showing an
   association between UBE2Q2 and sepsis.

   Our study also has certain limitations. First, the data for this
   analysis were from public databases and involved small samples, which
   may lead to biased interpretation of the results. Second, molecular
   experiments for validation are missing, and follow-up with larger
   clinical samples is needed to validate our main results. Third, further
   study is required to confirm whether methylation modifications found in
   key genes are associated with the progression of sepsis.

Conclusion

   The present study demonstrated heightened metabolic response and
   suppressed immune response in patients with sepsis and septic shock.
   The nine key genes that we identified may be useful for diagnosing
   sepsis and monitoring its development, while their specific functions
   and mechanisms in the disease need further study. Indeed, further
   experimental analysis is needed to validate our major results.

Acknowledgments