HCK and IL1RN as potential gene targets of sepsis based on the bioinformatics and verification in the mice model

doi:10.21203/rs.3.rs-1755816/v1

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HCK and IL1RN as potential gene targets of sepsis based on the bioinformatics and verification in the mice model

https://doi.org/10.21203/rs.3.rs-1755816/v1

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Background: Sepsis is emergency and severe problem with high incidence and fatality rate. Sepsis may be related to genetic factors, chromosomal abnormalities, gene fusion and other factors, pathogenesis is not clear. In-depth research on molecular mechanism of sepsis is particularly important.

Methods: Weighted gene co-expression network analysis (WGCNA) was used to identify significant module. DAVID, metascape, Gene Set Enrichment Analysis (GSEA) were performed to make enrichment analysis for differently expressed genes (DEGs). Hub genes were screened and identified in protein-protein interaction network via Cytoscape. Construction of mouse model of sepsis was made in C57BL/6 mice (8 weeks old), TNF-α, lactic acid, IL-6 were detected by ELISA assay to verify successful construction of sepsis model. RT-qPCR and ELISA were performed to test expression of hub genes. BP-neural network and support vector machine (SVM) were constructed with expression HCK, IL1RN, severity degree of sepsis.

Results: Compared with normal group, relative expression of TNF-α, lactic acid, IL-6 via ELISA in sepsis animal model was higher (P<0.05). After therapy by hydrogen for sepsis mice, relative expression of TNF-α, lactic acid, IL-6 significantly decreased compared with sepsis group (P<0.05). After therapy of hydrogen, expression of HCK and IL1RN was down-regulated. Relativity among expression of HCK, IL1RN, sepsis was 0.99542 via BP-neural network. Relationship between predicted value and actual value is that: y=0.8954*x+16.2933, R²=0.9805, r=0.9948 in SVM model.

Conclusion: HCK and IL1RN were significantly up-regulated in sepsis. They might be potential predictable biomarkers of sepsis, therapy of hydrogen was related to expression of HCK and IL1RN.

Sepsis

HCK

IL1RN

neural network

verification

Sepsis is a life-threatening organ dysfunction caused by the host's immune response to infection, and is an emergency and severe problem with a high incidence and fatality rate [1, 2]. In recent years, although significant progress has been made in the treatment of fluid resuscitation, anti-infection strategies and organ support, the outcome of sepsis patients is still not optimistic [3]. If patients with early sepsis cannot be diagnosed and treated promptly, multiple organ dysfunction and tissue insufficiency will progress to severe sepsis or even septic shock, which will greatly increase the mortality of patients with sepsis [4]. Early diagnosis is the basis of timely intervention. As sepsis reflects a group of clinical syndromes and disease states, the definition includes multi-dimensional clinical and biological features and lacks specificity, so it is easy to be missed or misdiagnosed clinically[5]. Sepsis may be related to genetic factors, chromosomal abnormalities, gene fusion and other factors, but the pathogenesis is not clear. Therefore, in-depth research on the molecular mechanism of sepsis is particularly important.

Bioinformatics is an interdisciplinary subject developing rapidly with the development of the Human Genome Project (HGP), mainly involving biology, mathematics and computer science [6]. With the rapid development of high-throughput sequencing technology, more comprehensive and in-depth analysis of the genome is encouraged. With the continuous accumulation of biological data resources with the characteristics of big data and the opening of the strategic plan of precision medicine, the importance of bioinformatics is becoming increasingly obvious[7], which has a great significance to promote the development of relevant fields. Rong Li [8] used bioinformatics to find that the occurrence and development of sepsis is associated with the preservative effect of vitamin C (VC) and immune dysfunction, as well as with the inhibition of inflammation-related functional processes and other signaling pathways. These major predicted biological targets may be used in future clinical practice to treat sepsis. Zhongheng Zhang found [9] that CEBPB and ETV6 and miRNA-target interaction play an important role in the occurrence and development of sepsis. Fang-Chen Gong [10] found that LRG1, ELANE, TP53, LCK, TBX21, ZAP70, CD247, ITK, and FYN as potential new biomarkers for sepsis.

HCK may play a role in neutrophil migration and neutrophil degranulation, and plays an important role in the occurrence and development of inflammation [11, 12]. The protein encoded by IL1RN (Interleukin 1 Receptor Antagonist) gene is a member of the Interleukin 1 cytokine family. This protein inhibits interleukin 1 (IL1A) and interleukin 1 (IL1B) activity and modulates a variety of interleukin-1-related immune and inflammatory responses, especially during the acute phases of infection and inflammation [13]. However, the association between HCK and IL1RN and the occurrence and development of sepsis remains unclear.

Therefore, this study intends to use bioinformatics technology to excavate the core genes of sepsis, and carry out enrichment analysis, pathway analysis and survival analysis. Public data were used to verify the role of core genes HCK and IL1RN in sepsis and the construction of sepsis mouse model to verify the value of HCK and IL1RN in its occurrence and development.

2.1 Data from the GEO database

We obtained the transcriptome expression profiles GSE25504, GSE54514 and GSE13904 from the GEO database (Table.1). Additionally, the three datasets included 115 sepsis samples and 71 normal samples, which were used to identify DEGs between sepsis and normal groups.

2.2 Screening of DEGs

Three GEO series were merged and normalized by Perl. R packages “affy” and “limma” were employed to perform probe summarization and background correction of GSE25504, GSE54514 and GSE13904. The Benjamini-Hochberg method was used to adjust the original p-values. Fold-changes (FC) were calculated using the false discovery rate (FDR). The cut-off criteria for DEGs were |log FC| > 0.585 and p value < 0.05.

2.3 WGCNA analysis

The WGCNA analysis of all genes in the merged series was performed using R package “WGCNA” (https://cran.r-project.org/web/packages/WGCNA/index.html). A co-expression network for all genes were constructed, and the algorithm filtered the 25% genes with the largest variation for further analysis. Then, 115 sepsis samples and 71 normal samples were used for WGCNA analysis. The adjacency matrix was created by samples, and then it was transformed into a topological overlap matrix (TOM). Genes were divided into different gene modules using TOM-based difference measurement. The minimal gene module > 200 and the threshold to merge similar modules=0.1 were used to search modules that play an important role in sepsis.

2.4 Functional annotation of DEGs.

The GO and KEGG analyses are computational methods that evaluate gene functions and biological pathways. Metascape (http://metascape.org/gp/index.html) database can provide a comprehensive gene list annotation and analysis resource. Gene Set Enrichment Analysis (GSEA) (http://software.broadinstitute.org/gsea/index.jsp) is a computational method that could execute GO and KEGG analysis with a given gene list. In this study, GO and KEGG analyses of target genes were performed to explore the potential mechanism by which module genes promotes the progression of sepsis.

2.5 Construction and analysis of PPI network

Funrich (http://www.funrich.org/) is a biological analysis software. The DEGs in the yellow model genes were analysis by Funrich. STRING (http://string-db.org), an online database, could predict and provide the protein-protein interaction (PPI) network after importing the yellow model DEGs. Cytoscape is an analysis tool, which could provide biological network analysis and two-dimensional (2D) visualization for biologists. In our study, the PPI network were construct and analyzed by SRTING database and Cytoscape, and the hub DEGs was screened by four algorithms. And then, the Venn plots were used to intersect the four groups hub DEGs to obtain the common hub DEGs by Funrich.

2.6 Identification of hub DEGs associated with cardiovascular diseases

Comparative toxicogenomics database (CTD database, http://ctdbase.org/) is a web-based database. The relationship between gene/protein and disease could be predicted by the CTD database. In our study, the relationships between genes products and sepsis were analyzed by this database.

2.7 MiRNA of hub DEGs prediction

TargetScan (www.targetscan.org) is an online database that performs predictive analysis and identifies possible mechanisms for co-regulating the expression of hundreds of genes expressed in different cell types. In our study, TargetScan was used to screen for miRNAs that regulate the hub DEGs.

2.8 Statistical Analysis

Univariate and multivariate logistic regression analysis were perform by R. The ROC curves were provided by MedCalc software.

2.9 Identification of candidate molecule drugs for hub genes

Connectivity map (CMap, https://portals.broadinstitute.org/cmap/) database could connect genes and genomic information with human disease and drugs that treat them. In this study, the molecule drugs that could regulate hub genes was searched by CMap database.

2.10 Construction of mouse model of sepsis

This procedure used the C57BL/6 mice (8 weeks old). Animal adaptation feeding for 1 week. The mice could be anesthetized with inhaled isoflurane (Shanghai Civic Chemical Technology Co., Ltd, Shanghai). Shave the abdomen of the mouse and disinfect area by first applying betadine solution. A 1.5cm incision was made at the midline and the cecum was exposed. After ligation (6-0 PROLENE, 8680G; Ethicon), the cecum is perforated once or twice on the ipsilateral side of the cecum with a 19 needle. Gently squeeze the cecum with your hands to squeeze out a small amount of stool. We then return the cecum to the peritoneal cavity and close the peritoneum. Close the skin wound with a clamp. Mice were subcutaneously injected with preheated 0.9% normal saline. Food and water were not restricted [14].

This study was approved by the Ethics Committee of the Tianjin chest hospital. All experiments were approved by Animal Care and Use Committee of the Tianjin chest hospital. All institutional and national guidelines for the care and use of laboratory animals were followed.

2.11 ELISA assay

The used antigen was diluted to an appropriate concentration with coating diluent (generally, the required amount of antigen coating is 100μg per well), and 100μ L was added to each well, and placed at 37℃ for 4h. The liquid in the well was discarded. 5% calf serum was placed at 37℃ and sealed for 40min. When sealing, fill each reaction hole with sealing liquid and remove bubbles in each hole. After sealing, wash the hole with washing liquid for 3 times, 3min each time. The dilution of 1:200 is used for detection, and a larger dilution volume should be used to ensure the sample absorption amount>20ul.Add the diluted samples to the enzyme-conjugate reaction well, add at least two wells to each sample, 100μ L to each well, and place at 37℃ for 40-60min. Use washing solution to wash the well 3 times, 3min each time. Enzyme-labeled antibody was added at 37℃ for 30-60min. Add 100μL to each well. Enzyme-labeled antibody was added at 37℃ for 30-60min. Add 100μL to each well. Add stop solution 50μl to each well to stop the reaction, and determine the experimental results within 20min. After OPD color rendering, 492nm wavelength was used, and TMB reaction product detection required 450nm wavelength.

The main reagents were that: Mouse IL-6 ELISA kit (KE10007, proteintech, Rosemont, USA), Mouse tumor necrosis factor (TNF) alpha ELISA kit (KE10002, proteintech, Rosemont, USA), Mouse lactic acid ELISA kit (LS-F55829, LSBio, USA), Mouse HCK ELISA Kit (Abbexa, abx389525, UK), Mouse IL1RN ELISA kit (ab238258, abcam, Cambridge, UK).

2.12 RT-PCR assay

The extraction of total RNA from the peripheral blood of sepsis mice was made by RNA extraction kit (RN5301, Hangzhou Haoxin Biotechnology Co., LTD). The reaction solution for reverse transcription was configured, and the negative control was treated with DEPC water instead of RNA template. The reaction tubes were placed on the PCR amplifiers, denaturated at 37℃ for 60min and 70℃ for 15min. The reverse transcription products were stored at 4℃ for later use. Each PCR reaction tube was put into the DNA amplifier at 94℃ for 5min, and then thermal cycling was conducted under the following conditions: 94℃, 45s; 56 ℃, 30 s; 72℃, 30s. Repeat this cycle 30 times, and extend for 10min at 72℃. RT-PCR products were detected by 1%-2% agarose gel electrophoresis. PCR product 8μ L was mixed with sample loading buffer 2μ L, and DNA Marker of appropriate size was used for electrophoresis separation. Ethidium bromide was stained, and the results were recorded and analyzed by image analyzer.

Primers of HCK, IL1RN and their sequences for PCR analysis were followed:

HCK-F: GGGCTGACCTCAGTGGAT,

HCK-R: GGCTGGTCCTGACAACAT.

IL1RN-F: TTCTGGGTCTTACTTTCC.

IL1RN-R: TTCCTCATAGGGTTATTGA.

2.13 Immunofluorescence assay

The slides were washed successively in xylene Ⅰ15min-xylene Ⅱ 15min-anhydrous ethanol Ⅰ 5min-anhydrous ethanol Ⅱ 5min-85% alcohol 5min-75% alcohol 5min-distilled water. The slides were placed in a repair box filled with EDTA antigen repair buffer (PH8.0) for antigen repair. After natural cooling, the slides were placed in PBS (PH7.4) and washed by shaking on a decolorizing shaker for 3 times, and 5min each time. Add BSA drops and incubate for 30min. The sealing solution was gently removed, and the prepared primary antibody was added to the sections with drops in a certain proportion. The sections were placed horizontally in a wet box at 4°C for incubation overnight. The slides were placed in PBS (PH7.4) and washed 3 times on a decolorizing shaker, 5min each. The slices were dried by shaking, then the second antibody was added to cover the tissue in a certain proportion, and incubated at room temperature for 50min. DAPI was used to restain the nuclei and incubate for 10min at room temperature, away from light. The self-quenching agent was added for 5min, and the water was rinsed for 10min.The slides were placed in PBS(PH7.4) and washed 3 times on a decolorizing shaker, 5min each. The slices were briefly shaken dry and sealed with anti-fluorescence quenching sealing tablets. The slices were placed under a scanner for image collection or a fluorescence microscope for photo taking. The DAPI stained nucleus is blue under ultra violet excitation, and the positive expression is corresponding fluorescein labeled red.

The main reagents were that: Mouse Anti-HCK antibody (ab61055, abcam, Cambridge, UK); Anti-IL1RN antibody (ab175392, abcam, Cambridge, UK).

2.14 Construction of BP neural network model

The calculation process of BP neural network consists of forward calculation process and reverse calculation process. Neural network is a network formed by connecting multiple neurons together according to certain rules. In the forward propagation process, the input mode is processed layer by layer from the input layer through the hidden unit layer and then transferred to the output layer. The state of neurons in each layer only affects the state of neurons in the next layer. If the desired output cannot be obtained at the output layer, reverse propagation is carried out to return the error signal along the original connection path, and the error signal is minimized by modifying the weights of each neuron. In this study, the input value of neural network is the relative expression value of HCK and IL1RN, and the output variable is the expression quantity of IL6. Define a BP neural network class, set network parameters. A BP network with 3 dimensions of output and 1 dimension of output is constructed with 3 hidden layers (10 nodes for each hidden layer). When the BP neural network is initialized, the weight, weight momentum and initial error of network nodes of each layer are initialized. Introduction of learning and training data; Input and output data iterated 3000 times. 3000 times continuously forward layer by layer to compute the output node data. At the same time, the error is calculated layer by layer and the weight value is reversed until the iteration is completed. Note that the error function must be decreasing. Introduce the data to predict the results and bring the data back to the model.

2.15 Build the support vector machine model among HCK, IL1RN and IL6

Support Vector Machine (SVM) is a kind of generalized linear classifier that classifies data by supervised learning method. The decision boundary is the maximum-margin hyperplane solved for the learning sample. SVM uses hinge loss function to calculate empirical risk and adds regularization term to the solving system to optimize structural risk. It is a classifier with sparsity and robustness. SVM is one of the common kernel learning methods, can be used for nonlinear classification by kernel method. In this study, the input value of neural network is the relative expression value of HCK and IL1RN, and the output variable is the expression quantity of IL6.

2.16 Statistical analysis

The data was expressed as mean ± SD. Independent-samples T test was used, and when the equal variances not assumed, Brown-Forsythe was performed. All statistical analyses were conducted using SPSS software, version 24.0 (IBM Corp., Armonk, NY, USA), and MATLAB (R2014a, MathWorks. Inc, New Mexcico, USA). A p-value < 0.05 was considered statistically significant.

3.1 Identification of DEGs

The merged series contained 172 up-regulate genes and 127 down-regulate genes (Figure 1A). And the heat map of DEGs expressions is shown in Figure 1B.

3.2 WGCNA analysis

In our study, WGCNA analysis were conducted by R package “WGCNA”. The selection of the soft-thresholding power was an important step in WGCNA analysis. Network topology analysis was performed to identify the soft-thresholding power. The soft-thresholding power in the WGCNA analysis was set to 9, which was the lowest power for the scale-free topology fit index of 0.9 (Figure 1C). A hierarchical clustering tree of all genes was constructed and 8 important modules were generated (Figure 1D). In addition, the dendrogram and heatmap of genes showed that there was no significant difference in interactions among different modules, demonstrating that there was a high degree of independence between these modules (Figure 1E). The yellow module had the highest correlation with the status of sepsis (Figure 1F). Interactions between these modules were then analyzed (Figure 1G).

3.3 Functional and pathway enrichment analysis of DEGs

The enrichment results of GO and KEGG analysis of yellow model genes performed by Metascape were mainly enriched in lytic vacuole, secretory granule membrane, actin filament-based process, regulation of defense response, regulation of vesicle-mediated transport, cell activation, endosome membrane, innate immune response, response to cytokine, regulation of immune effector process, positive regulation of hydrolase activity, phagocytic vesicle, inflammatory response, infection, phagosome, MAPK signaling pathway, NOD-like receptor signaling pathway, bacterial invasion of epithelial cells, platelet activation, and others (Figure 2).

3.4 Construction and analysis of protein-protein interaction network

The venn diagram is used to intersect the module gene and DEG (Figure 3A). The PPI network of DEGs in yellow module was constructed by STRING online database and analyzed by Cytoscape software (Figure 3B). Four different algorithms were employed to identify hub genes and 6 common hub genes were obtained (Figure 3C). A summary of common hub genes was shown in Table 2. The PPI network of common hub genes was showed in Figure 3D.

3.5 Logistic regression analysis of hub genes.

Univariate logistic regression analysis of hub genes (Figure 3E). Multivariate logistic regression analysis show that HCK and IL1RN are highly correlated with sepsis (Figure 3F).

3.6 The enrichment analysis of the GSEA

Through the GSEA, the main enriched terms of HCK focused on the blood vessel endothelial cell migration, cellular response to inorganic substance, inflammatory response, cleavage involved in RRNA processing, polysomal ribosome, MAPK signaling pathway, leukocyte transendothelial migration, TGF-beta signaling pathway, ubiquitin mediated proteolysis (Figure 4A).

Furthermore, through the GSEA, the main enriched terms of IL1RN focused on the cytokine receptor binding, ERK1 and ERK2 cascade, immune receptor activity, synaptic signaling, preribosome, MAPK signaling pathway, VEGF signaling pathway, lysine degradation, protein export and others (Figure 4B).

3.7 Statistical Analysis and Identification of hub genes

Differential expression analysis of HCK and IL1RN (Figure 5A). The CTD database shows common hub genes (HCK and IL1RN) associated with sepsis, as shown in Figure 5B. The ROC curves of common hub genes (HCK and IL1RN) were shown in Figure 5C, which presented that the AUC of HCK for the sepsis was 0.822 (P<0.001), and the AUC of IL1RN for the sepsis was 0.826 (P<0.001).

3.8 Prediction of miRNA associated with hub genes

The miRNA that regulates the hub genes were screened out with TargetScan (Table.3).

3.9 Identification of candidate molecule drugs for hub genes

The small molecule drugs for sepsis that target to hub genes were screened by CMap database, small molecule drugs were filtered by the number of instances (n> 3) and P value (<0.05), as shown in Table 4.

3.10 The successful construction of the sepsis animal model

Compared with the normal group, the relative expression of TNF-alpha via ELISA in the sepsis animal model was higher (P<0.05). Furthermore, after therapy by the hydrogen for the sepsis mice, the relative expression of TNF-alpha significantly decreased compared with the sepsis group (P<0.05) (Figure 6A). Compared with the normal group, the relative expression of lactic acid via ELISA in the sepsis animal model was higher (P<0.05). Furthermore, after therapy by the hydrogen for the sepsis mice, the relative expression of lactic acid significantly decreased compared with the sepsis group (P<0.05) (Figure 6B). Compared with the normal group, the relative expression of IL-6 via ELISA in the sepsis animal model was higher (P<0.05). Furthermore, after therapy by the hydrogen for the sepsis mice, the relative expression of IL-6 significantly decreased compared with the sepsis group (P<0.05) (Figure 6C).

3.11 The rising expression of HCK and IL1RN via the RT-PCR

Compared with the normal group, the relative expression of HCK via RT-PCR in the sepsis animal model was higher (P<0.05). Furthermore, after therapy by the hydrogen for the sepsis mice, the relative expression of HCK significantly decreased compared with the sepsis group (P<0.05) (Figure 7A). Compared with the normal group, the relative expression of IL1RN via RT-PCR in the sepsis animal model was higher (P<0.05). Furthermore, after therapy by the hydrogen for the sepsis mice, the relative expression of IL1RN significantly decreased compared with the sepsis group (P<0.05) (Figure 7B).

3.12 The verification of expression of HCK and IL1RN via the ELISA

Compared with the normal group, the relative expression of HCK via ELISA in the sepsis animal model was higher (P<0.05). Furthermore, after therapy by the hydrogen for the sepsis mice, the relative expression of HCK significantly decreased compared with the sepsis group (P<0.05) (Figure 8A). Compared with the normal group, the relative expression of IL1RN via ELISA in the sepsis animal model was higher (P<0.05). Furthermore, after therapy by the hydrogen for the sepsis mice, the relative expression of IL1RN significantly decreased compared with the sepsis group (P<0.05) (Figure 8B).

3.13 The high expression of HCK and IL1RN in the sepsis via the immunofluorescence

In the immunofluorescence assay, the blue color represents the nucleus, and the red represents the target gene. Compared with the normal group, the relative expression of HCK via immunofluorescence in the sepsis animal model was higher (P<0.05). Furthermore, after therapy by the hydrogen for the sepsis mice, the relative expression of HCK significantly decreased compared with the sepsis group (P<0.05) (Figure 9).

The expression of IL1RN in the sepsis mice was higher than the normal mice. And compared with the sepsis group, the expression of IL1RN was lower significantly (P<0.05). (Figure 10)

3.14 Expression of HCK and IL1RN predicted the sepsis based on BP-neural network model

Train the neural network at 3,000 steps, and the best training performance is less than 0.05, which was of significance (P<0.05) and presented the model was trained well (Figure 11A). The relativity among the expression of HCK, IL1RN, and sepsis was 0.99542 (Figure 11B). Then, 10 cases in the data were used to verify the BP-neural network, and the trend of the raw data is consistent with the trend of the forecast data (Figure 6C). The percentage of error is close to zero (Figure 11D). When the 9<HCK<16 and 14<IL1RN<19, the severity of sepsis is relatively large (Figure 11E, F).

3.15 Verification the strong predictable value of HCK and IL1RN for the sepsis via the support vector machine (SVM)

In the verification sample comparison, the trend of actual value was similar with the predicted value via the SVM model (Figure 12A). And the error histogram with 20 bins was closed title the zero error (Figure 12B). Furthermore, the percentage of error was less than the 4.5 (Figure 12C). In the scatter fitting diagram, the relationship between the predicted value and the actual value is that: y=0.8954*x+16.2933, R²=0.9805, r=0.9948 (Figure 12D). In summary, the mean error in the SVM model is 4.57%.

Sepsis is mainly a systemic inflammatory syndrome induced by major surgery, severe burns, trauma infection and other factors, which can lead to life-threatening multi-organ dysfunction [15]. This disease is characterized by high incidence, rapid disease development, difficult treatment and high mortality [16]. Although there are better methods and monitoring measures for the clinical treatment of sepsis, the harmfulness of sepsis is still high. Sepsis is an important pathway leading to death in most patients with infectious diseases, and early recognition may help to improve the adverse outcomes of patients with sepsis [17]. It is very important to explore the molecular mechanism of sepsis for the research of targeted drugs. In this study, bioinformatics technology was used to analyze the genes related to sepsis. The main results showed that HCK and IL1RN played an important role in sepsis, and the expression of HCK and IL1RN was high in sepsis compared with the control group.

HCK (Src family tyrosine kinase) is a protein-coding gene [18]. This protein is primarily hematopoietic, especially in myeloid and B lymphoid cells. In addition, it may play a role in neutrophil migration and neutrophil degranulation [19]. Due to optional splicing and the use of optional translation start codons, including a non-Aug (CUG) codon, a variety of subtypes with different subcellular distributions have arisen. Related pathways include the life cycle of HIV and the role of phospholipids in phagocytosis, as well as inflammatory diffusion [20]. The Gene Ontology (GO) annotations related to this Gene include transferase activity, transfer of phosphorus-containing groups, and protein tyrosine kinase activity. HCK provides necessary kinases for the occurrence and development of sepsis and promotes the deterioration of inflammation. Non-receptor tyrosine protein kinases are present in hematopoietic cells and transmit signals from receptors on the cell surface. They play an important role in the regulation of innate immune responses, including neutrophils, monocytes, macrophages and mast cells function, phagocytosis, cell survival and proliferation, cell adhesion and migration. Therefore, HCK promotes the spread and migration of sepsis through the above inflammatory cells. HCK acts on receptors downstream of the immunoglobulin Fc region, such as FCGR1A and FCGR2A, and on receptors of CSF3R, PLAUR, IFNG, IL2, IL6 and IL8, as well as integrins such as ITGB1 and ITGB2. During phagocytosis, it mediates mobilization of secretory lysosomes, degranulation, and activation of NADPH oxidase, leading to respiratory bursts[21, 22]. HCK plays a role in the release of inflammatory molecules [23]. It promotes the recombination of actin cytoskeleton and actin polymerization to form foot plasts and cellular processes. The HCK gene is highly expressed in sepsis (compared with the normal group) and plays a role in the release of inflammatory molecules. It promotes the recombination of actin cytoskeleton and actin polymerization to form foot plasts and cellular processes. The importance of HCK as an active component in lipolysaccharide induced TLR signaling, targeting this kinase to alleviate the possibility of excessive inflammation [24]. Antonia K Roseweir [25] study showed that HCK can promote tumor progression and reduce local inflammatory lymphocyte response. Xiangxi Kong [11] found that Hematopoietic Cell Kinase (HCK) inhibitors reduced lipopolysaccharide-induced liver inflammation in mice, and HCK played a key role in the activation of inflammatory bodies. Therefore, it is speculated that HCK gene is highly expressed in patients with sepsis, which promotes the development, migration and diffusion of inflammation, and then induces the deterioration of sepsis. Therefore, HCK plays an important role in the occurrence and development of sepsis and may be a potential molecular target for the diagnosis and treatment of sepsis in the future.

The protein encoded by IL1RN (Interleukin 1 Receptor Antagonist) gene is a member of the Interleukin 1 cytokine family. Cytokines and receptors in the interleukin-1 (IL-1) family are key mediators of innate inflammatory responses and have both pro-inflammatory and anti-inflammatory functions [26, 27]. Proteins encoded by the IL1RN gene inhibit the activity of interleukin 1, α (IL1A) and interleukin 1, β (IL1B), and regulate a variety of interleukin 1-related immune and inflammatory responses, especially during the acute phases of infection and inflammation. IL1RN leads to the development of sepsis by promoting the deterioration of inflammation. This gene and five other closely related cytokine genes form a gene cluster of about 400 KB on chromosome 2. Diseases associated with IL1RN include osteomyelitis, aseptic multifocal disease, periostitis and impetigo, and microvascular complications of diabetes [13]. Related pathways include immune system cytokine signaling pathway and IL-1 family signaling pathway. The gene body notes associated with this gene include cytokine activity and interleukin-1 receptor antagonist activity. IL1RN signals by binding to the receptor IL1R1 to inhibit interleukin-1 activity and block its association with the co-receptor IL1RAP. The polymorphism of IL1RN gene may play a potential role in the pathogenesis of inflammation and infection, thus promoting the occurrence and development of sepsis. Sona Margaryan [28] research results suggest that hypomethylated IL1RN gene promoter may promote the production of IL-1β/ IL-1RA and regulate chronic inflammation of T2DM.Mukundan Attur [29] study showed that IL1RN is related to the occurrence and development of inflammation. Clinio Alves De Souza [13] demonstrated that IL1RN is significantly associated with inflammation development. Inflammation can lead to small airway disease and destruction of lung parenchyma, resulting in airflow restriction. Polymorphism of pro-inflammatory cytokine genes may be a risk factor for the development of COPD [30]. The results of N P Barlo's [31] study showed that genetic variation in IL1RN may play a role in the pathogenesis of idiopathic inflammation, and this role may be more important than currently believed. The imbalance between IL-1RA and IL-1β may contribute to the development of inflammation in the lungs [32]. Therefore, it is speculated that IL1RN gene is highly expressed in patients with sepsis, which promotes the development, migration and diffusion of inflammation, and then induces the deterioration of sepsis. Therefore, IL1RN plays an important role in the occurrence and development of sepsis, and may be a potential molecular target for the diagnosis and treatment of sepsis in the future. The above literature review is consistent with our results that HCK and IL1RN genes play a key role in the process of inducing sepsis by promoting the occurrence and development of inflammation.

Despite the rigorous bioinformatics analysis in this study, there are still some shortcomings. In this study, no animal experiments of gene overexpression or knockout were conducted to further verify its function. Therefore, we should carry out in-depth exploration in this aspect in the future research.

In conclusion, HCK and IL1RN are highly expressed in sepsis, which promote the development, migration and diffusion of inflammation, and then induce the deterioration of sepsis. HCK and IL1RN genes play an important role in the occurrence and development of sepsis. They may serve as potential molecular targets for future diagnosis and treatment of sepsis.

Weighted gene co-expression network analysis (WGCNA), Gene Set Enrichment Analysis (GSEA), differently expressed genes (DEGs), support vector machine (SVM), Human Genome Project (HGP), vitamin C (VC), Interleukin 1 Receptor Antagonist (IL1RN), Fold-changes (FC), Gene Set Enrichment Analysis (GSEA), Comparative toxicogenomics database (CTD), Connectivity map (CMap), tumor necrosis factor (TNF), Support Vector Machine (SVM), Gene Ontology (GO), Hematopoietic Cell Kinase (HCK), interleukin-1 (IL-1), interleukin 1, α (IL1A), interleukin 1, β (IL1B)

Ethics approval and consent to participate

Consent for publication

All patients received written informed consent and gave their consent for publication.

Availability of data and materials

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

Author contributions

Tao Wang participated in the topic selection and design of the paper. Jian-ge Han participated in data analysis and interpretation. Wei Dong and Yong-hao Yudrafted and revised key theories in the paper and answered academic questions. All the authors agreed to be published.

Acknowledgements

Not applicable

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Table 1

A summary of sepsis microarray dataset from different GEO datasets.

	Series	Platform	Affymetrix GeneChip	Normal	Sepsis
1	GSE25504	GPL6947	Illumina HumanHT-12 V3.0 expression beadchip	35	28
2	GSE54514	GPL6947	Illumina HumanHT-12 V3.0 expression beadchip	18	35
3	GSE13904	GPL570	[HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array	18	52

Table 2

A summary of hub genes.

Symbol	Description	Funtion
IL1B	interleukin 1 beta	GO:1903597 negative regulation of gap junction assembly; GO:0035504 regulation of myosin light chain kinase activity; GO:0035505 positive regulation of myosin light chain kinase activity
HCK	HCK proto-oncogene, Src family tyrosine kinase	GO:0002522 leukocyte migration involved in immune response; GO:0045728 respiratory burst after phagocytosis; GO:0002679 respiratory burst involved in defense response
FPR1	formyl peptide receptor 1	GO:0002430 complement receptor mediated signaling pathway; GO:0007263 nitric oxide mediated signal transduction; GO:0007200 phospholipase C-activating G protein-coupled receptor signaling pathway
IL1RN	interleukin 1 receptor antagonist	GO:2000660 negative regulation of interleukin-1-mediated signaling pathway; GO:2000659 regulation of interleukin-1-mediated signaling pathway; GO:0034115 negative regulation of heterotypic cell-cell adhesion
AQP9	aquaporin 9	GO:0015722 canalicular bile acid transport; GO:1904823 purine nucleobase transmembrane transport; GO:0015855 pyrimidine nucleobase transport

Table 3

A summary of miRNAs that regulate hub genes.

	Gene	Predicted MiR		Gene	Predicted MiR
1	IL1B	hsa-miR-4700-3p hsa-miR-383-3p hsa-miR-3065-3p	4	IL1RN	hsa-miR-122-5p hsa-miR-224-5p hsa-miR-1292-5p
2	HCK	hsa-miR-6799-3p hsa-miR-3173-5p hsa-miR-6775-3p	5	AQP9	hsa-miR-154-5p hsa-miR-5002-3p hsa-miR-186-3p
3	FPR1	hsa-miR-920 hsa-miR-5591-5p hsa-miR-6726-5p	6	CD14	hsa-miR-664a-3p hsa-miR-8081 hsa-miR-937-5p

Table 4

A summary of connectivity map that regulate hub gene in sepsis.

	cmap name	mean	n	enrichment	p
1	hexylcaine	0.641	4	0.862	0.0005
2	oxetacaine	0.504	5	0.772	0.00138
3	isoniazid	-0.409	5	-0.763	0.00142
4	hesperidin	0.595	4	0.806	0.00265
5	pentoxyverine	0.463	4	0.795	0.00348
6	ambroxol	-0.513	4	-0.793	0.00368
7	propofol	0.478	4	0.784	0.00412
8	betonicine	-0.446	6	-0.656	0.00465

No competing interests reported.

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HCK and IL1RN as potential gene targets of sepsis based on the bioinformatics and verification in the mice model

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