Identification of common core genes and pathways of sepsis and cancer by bioinformatics analysis

doi:10.21203/rs.3.rs-5118759/v1

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Identification of common core genes and pathways of sepsis and cancer by bioinformatics analysis

https://doi.org/10.21203/rs.3.rs-5118759/v1

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Introduction

Both sepsis and cancer are leading causes of death worldwide, and they share a number of pathophysiological features. Some studies have suggested a possible association between sepsis and cancer, but few have studied core genes in both diseases.

Methods

Common core genes were identified from sepsis datasets (GEO: GSE26378, GSE4607, GSE8121 and GSE13904) and cancer databases (TCGA: BRCA, COADREAD, ESCA, KIRC, LIHC, LUAD, STAD). Then, GO and Reactome enrichment analyses and PPI network analysis were performed. Pharmacophore screening was used to predict the targetsof oxymatrine and ulinastatin,and potential target genes in both cancer and sepsis were obtained. Survival analysis was performed. The association between the target genes and tumor size and number of positive lymph nodes was investigated by Pearson correlation analysis. The association between the target genes and tumor stage was investigated by Fisher’s exact test. Molecular docking analysis was performed to evaluate the affinity of the candidate drugs for their targets.

Results

In total, 641 common genes were identified. GO enrichment analysis showed that common genes were enriched in neutrophil degranulation, inflammatory response and innate immune response. Reactome enrichment analysis showed that common genes were enriched in neutrophil degranulation, interleukin-4 and interleukin-13 signaling, transcriptional regulation of granulopoiesis and interleukin-10 signaling. The PPI network showed that the top 10 core genes were TLR4, IL1B, IL10, ITGAM, TLR2, PTPRC, CDK1, FOS, MMP9 and ITGB2. The survival analysis showed that the high expression of BCAT1, CSAD, G6PD, GM2A, MMP9, PYGL and TOP2A was associated with poorer prognosis in several cancers. Molecular docking showed that oxymatrine and ulinastatin can bind to protein targets with highly stable binding.

Conclusions

We identified genes with common effects on both sepsis and cancer, which provides new insights into the association between sepsis and cancer. In addition, two drugs with potential clinical application value were identified. Further studies are required to validate the role of these common core genes in sepsis and cancer and to evaluate the potential utility of these drugs.

cancer

sepsis

bioinformatics

oxymatrine

ulinastatin

Both cancer and sepsis are leading causes of death worldwide, and they share a number of pathophysiological features. They all result from the inability of the host's immune system to defend against the primary injury, whether it is the invasion of pathogens or the abnormal differentiation of tumor cells. However, there are still many uncertainties and controversies regarding the real-world interaction between the two.

A case‒control study in the United States evaluated the association between sepsis and the subsequent risk of cancer [1]. Sepsis is considered a risk factor for tumorigenesis in multiple myeloma, lung cancer, colorectal cancer and liver cancer. The early inflammatory damage and secondary damage of sepsis lead to a persistent immunosuppressive state in patients, which may underlie a favorable environment to promote cancer outbursts [2]. Nevertheless, in contrast, patients with sepsis have a reduced risk of breast cancer, prostate cancer, kidney cancer, thyroid cancer, and melanoma, which may be related to the enhancement of the antitumor function of tumor-associated cytotoxic cells, such as tumor-associated macrophages, CD8⁺ T cells and NK cells [3–5].

On the other hand, although the evidence for a single effect is limited, the combination of the treatment-acquired immune dysfunction and the immunosuppressive microenvironment formed by the cancer itself that enables cancer cells to escape immune surveillance increases the risk and severity of sepsis [6–8]. Similarly, the results of a prospective study demonstrate that the 180-day mortality rate among cancer patients due to septic shock is higher than that of non-cancer patients [9].

Moreover, there is currently also a lack of a unified conclusion on the treatment of patients with both cancer and sepsis. The management of sepsis includes three major aspects: control of the underlying infection, hemodynamic stabilization and modulation of the host response. No strategies targeting individual elements of the immune response have thus far been shown to consistently improve outcomes in randomized clinical trials [10]. At present, the most widely used adjuvant intervention in clinical practice is the administration of glucocorticoids in patients with septic shock, but its side effect of immunosuppression may promote tumorigenesis [11, 12]. Following an exaggerated inflammatory response, patients with sepsis experience persistent immunosuppression, so therapies targeting negative costimulatory molecules are thought to increase host resistance to infection and improve outcomes. For instance, in some preclinical models, PD-1 blockade improves the outcome of sepsis [13–15]. However, no evidence has shown the benefit of PD-1 blockade in clinical trials in humans for sepsis [16, 17].

Ulinastatin and oxymatrine have been shown to have both anti-inflammatory and antitumor activities in previous studies [18–21]. However, there is currently a lack of studies exploring both the antitumor and antisepsis activities of these two drugs simultaneously.

Thus, this study aims to search for common immunomodulatory signaling pathways between cancer and sepsis and to identify potential therapeutic options that can treat both cancer and sepsis.

Data acquisition

The expression profile microarray data of the whole blood samples of sepsis (shock) and the control group were downloaded from the GEO database, and a total of 5 comparison groups in 4 items were obtained and used for subsequent analysis of differential genes. The expression data of cancer tissues and paracancerous tissues and clinical information of 7 sets of samples, including gastric cancer (STAD), breast cancer (BRCA), lung adenocarcinoma (LUAD), hepatocellular carcinoma (LIHC), renal clear cell carcinoma (KIRC), esophageal cancer (ESCA) and colorectal cancer (COADREAD), were downloaded from the TCGA database and were used for subsequent analysis of differentially expressed genes, survival analysis, and clinical correlation analysis.

Drug target analysis

Chemical structure sdf files (Chemical Structure Records) of oxymatrine and ulinastatin were obtained through the PubChem database (https://pubchem.ncbi.nlm.nih.gov/). Pharmacophore screening on the PharmMapper server (PharmMapper Server, http://lilab-ecust.cn/pharmamapper/) was used to predict the target of the compound and obtain potential target genes. Human-related genes were specifically extracted from the potential target genes as candidate drug targets.

Differential expression analysis

In the GEO database, the gene fold difference (FC) was calculated based on the normalized signal value of the chip, the significant p value was obtained by T test, and the p value was corrected by the BH method to obtain the FDR. The screening criteria for differential genes were FC > 2 and FDR < 0.05. In the TCGA database, based on the downloaded count data, DESeq2 was used to calculate the genetic differences between groups, the negative biphasic distribution test was used to obtain a significant p value, and the p value was corrected by the BH method to obtain FDR. The screening criteria for differential genes were FC > 2 and FDR < 0.05.

Functional enrichment analysis

Gene Ontology (GO) terms and reactome enrichment analyses were performed based on the GO database (http://www.geneontology.org/) and the reactome database (https://reactome.org/). The R package “ClusterProfiler” was used to distinguish the differentially expressed pathways, with p-values calculated using right‐sided hypergeometric, and the package “enrichplot” was used for visualization.

Survival analysis

The gene expression data and clinical information of 7 cancers were downloaded from the TCGA database, and the samples were divided into high-expression and low-expression groups based on the median value of gene expression, combined with clinical overall survival time (OS) using the python package lifelines to analyze and draw survival curves, using the logrank_test method in this package to calculate a significant p value. Based on the expression of target genes in cancer patient samples and clinical data, Cox analysis was performed using the CoxPHFitter method in the python package lifelines.

Clinical data analysis

In the TCGA data, gene expression, tumor size and lymph node number were extracted, and the python package scipy was used to calculate the Pearson correlation coefficient between gene expression and tumor size and lymph node number, and a significant p value was obtained. Tumor stage information was also extracted. Samples were divided into high-expression and low-expression groups according to the median value of gene expression, and each type of tumor was divided into stages I and II and stages III and IV according to tumor stage information. Fisher_exact in the python package scipy was used to perform Fisher's exact test calculation to obtain the p value.

Molecular docking

The structure files of oxymatrine and ulinastatin were obtained through the PubChem database (https://pubchem.ncbi.nlm.nih.gov/). The protein structure files were downloaded from the PDB database (https://www.rcsb.org/).

Identification of DEGs

Using an adjusted p value < 0.05 and |log2 (fold change)| > 1.5 as the cutoff criteria, DEGs in the 4 sepsis microarray datasets (GSE26378, GSE4607, GSE8121, and GSE13904) were screened (Fig. 1A). As shown in the Venn map, 1215 genes were identified as sepsis-related candidate genes (Fig. 1B). DEGs in the 7 cancer microarray datasets (BRCA, COADREAD, ESCA, KIRC, LIHC, LUAD, and STAD) were also screened (Fig. 1C). Using UpSet analysis, 11468 genes were identified as cancer-related candidate genes. The Venn plot showed that 641 genes could be associated with both cancer and sepsis (Fig. 1D).

GO Function and Reactome Pathway Enrichment Analyses of DEGs

A total of641 DEGs were selected for GO and Reactome analysis. The GO enrichment analysis results include cellular component (CC), molecular function (MF) and biological process. For BP, DEGs were predominantly enriched in neutrophil degranulation, inflammatory response and innate immune response. For MF, DEGs were mainly enriched in transmembrane signaling receptor activity, NAD(P)+ nucleosidase activity and NAD+ nucleotidase, cyclic ADP-ribose generating (Fig. 2A). Reactome pathway enrichment analysis showed that DEGs were predominantly enriched in neutrophil degranulation, interleukin-4 and interleukin-13 signaling, transcriptional regulation of granulopoiesis and interleukin-10 signaling (Fig. 2B).

Construction of the PPI Network

The PPI network was constructed using the STRING database (Fig. 3A) and Cytoscape (Fig. 3B). The node degree of proteins was used to rank, and the top 10 genes were TLR4, IL1B, IL10, ITGAM, TLR2, PTPRC, CDK1, FOS, MMP9 and ITGB2 (Table 1).

Drug Target Analysis

Pharmacophore screening was used to predict the target of the compound and obtain the potential target gene. Human-related genes were screened from the potential target genes as candidate drug targets for oxymatrine (162 genes) and ulinastatin (172 genes) in humans. The integrated analysis of the predicted targets of oxymatrine, ulinastatin and sepsis-cancer-related genes revealed 10 potential drug targets in oxymatrine (MMP9, REPS2, ANXA1, PYGL, G6PD, CSAD, RORA, IL10, CHIT1 and LILRA2) and 7 potential drug targets in ulinastatin (TOP2A, GM2A, BCAT1, RORA, IL10, CHIT1 and LILRA2), among which there were 4 common drug targets (RORA, IL10, CHIT1, LILRA2) (Fig. 4).

Clinical Data Analysis

The samples were divided into a high-expression group and a low-expression group according to the threshold of the median expression value, and survival analysis was performed. The results showed that the high expression of BCAT1, CSAD, G6PD, GM2A, MMP9, PYGL and TOP2A was associated with poorer prognosis in several cancers (Fig. 5A-M). Correlation analysis was performed between the target genes and tumor size and the number of positive lymph nodes. TOP2A showed a strong correlation with tumor size in both LIHC and KIRC and a strong correlation with positive lymph nodes in STAD (Fig. 5N-O). Analysis of the relationship between gene expression and tumor stage showed that in KIRC, patients with higher TOP2A, G6PD, and MMP9 levels and lower RORA levels were more likely to have worse tumor stages (Fig. 5P).

Expression of Target Genes in Tumor and Sepsis

According to the differential expression analysis, enrichment analysis, PPI analysis and clinical data analysis, we preliminarily identified some target genes and verified their expression in sepsis and cancer. The expression of each target gene in sepsis (Fig. 6A-H) and cancer (Fig. 6I-P) is shown in a boxplot.

Molecular Docking Analysis

To evaluate the affinity of the candidate drugs for their targets, we performed molecular docking analysis. The results showed that oxymatrine and ulinastatin can bind to their protein targets through visible hydrogen bonds (Fig. 7A-I), and they all have low binding energies of less than -7.0 kcal/mol, indicating highly stable binding (Table 2).

We selected gastric cancer, breast cancer, lung cancer, liver cancer, renal cancer, esophageal cancer, and colorectal cancer for our study. These cancers collectively represent a significant burden on global public health due to their high incidence and mortality rates. They are known to often manifest with advanced disease states and poor prognoses, increasing the likelihood of complications such as sepsis. Moreover, these cancer types frequently intersect with various aspects of the immune system, rendering patients more susceptible to infections and subsequent sepsis. Additionally, prior research has indicated notable associations between these specific cancers and sepsis [1, 22, 23].

As the two deadliest diseases in the world, sepsis and cancer are intricately linked. In the present study, we identified 641 genes that are differentially expressed in both sepsis and cancer.

GO functional analysis revealed that the DEGs mainly participated in neutrophil degranulation, the inflammatory response and the innate immune response. Reactome pathway enrichment analysis revealed that the DEGs were mainly enriched in neutrophil degranulation and interleukin-4 and interleukin-13 signaling.

Neutrophils act as the initial responders during inflammation and infection, employing cytotoxic proteins and proteases found in their cytoplasmic granules to eliminate pathogens. The process of neutrophil degranulation involves the mobilization of these granules, which then fuse with the cell membrane or the membrane of phagosomes, leading to the release of soluble granule proteins or the presentation of membrane-bound granule proteins on the cell surface. Dysfunction of neutrophils has been recognized as a crucial element in the pathophysiology of sepsis [24]. Research has shown that pentoxifylline can decrease neutrophil adhesion, inhibit their degranulation, and enhance survival rates in several animal models [25, 26]. Ye Hua et al. found that adenosine monophosphate (AMP) can inhibit neutrophil degranulation induced by lipopolysaccharide (LPS), and its expression level is negatively correlated with disease severity in sepsis patients [27]. When neutrophils are activated, several granule proteins are released into the extracellular medium, such as MMP-9, NGAL, HPSE, NE and ARG1, which can affect the progression of cancer [28]. MMP-9 released by neutrophil degranulation is the main source of angiogenesis-inducing MMP-9 in the tumor microenvironment and is not hindered by its inhibitor TIMP-1 [29, 30]. Human peripheral blood neutrophils constitutively express high amounts of ARG1 [31]. Markus Munder et al. found that during neutrophil-mediated inflammation, ARG1 is liberated and massively depletes arginine in the surroundings, severely inhibiting T-cell expansion and certain T-cell effector functions, which contributes to the immune escape of cancer cells [32].

IL-4 and IL-13 are related cytokines that regulate many aspects of allergic inflammation. They play important roles in regulating the responses of lymphocytes, myeloid cells, and nonhematopoietic cells [33]. IL4 can inhibit acute inflammation and resolve immunoparalysis with LPS-induced hyperinflammation [34]. Previous studies have suggested that IL-4 and IL-13 can exert effects on epithelial tumor cells through corresponding receptors [35]. M Kornmann et al found that IL-4R and IL-13R are expressed in the pancreatic cancer cell lines PANC-1, MIAPaCa-2 and CAPAN-1, and their proliferation is inhibited by Pseudomonas exotoxin (PE), which can bind with IL-4 and IL-13 [36]. Gabitass et al. evaluated plasma IL-4 and IL-13 levels in patients with pancreatic cancer, gastric cancer and esophageal cancer, and they found that IL-13 levels in plasma were significantly higher in all three cancer patients than in healthy controls [37]. In an indirect coculture system, M2-polarized tumor-associated macrophages (TAMs) induced by IL-4 treatment enhanced the malignant phenotypes of pancreatic cancer cells, promoting epithelial–mesenchymal transition (EMT) and eventually leading to increased cell proliferation and migration [38]. IL-13 secreted by activated pancreatic stellate cells (PSCs) was found to initiate the polarization of TAMs in the tumor microenvironment (TME), to promote pancreatic fibrosis and to mediate pancreatic tumorigenesis [39, 40].

We also identified several hub genes through PPI analysis. Toll-like receptor 4 (TLR4) is a pattern recognition receptor (PRRS) mainly expressed on the cytoplasmic membrane of hematopoietic stem cells that can trigger an innate immune response and inflammation through activation by exogenous pathogen-associated molecular patterns (PAMPs) or endogenous damage-associated molecular patterns (DAMPs) [41]. Hyodeoxycholic acid (HDCA) can suppress excessive activation of inflammatory macrophages by competitively blocking lipopolysaccharide binding to TLR4 and the myeloid differentiation factor 2 receptor complex, significantly decreasing systemic inflammatory responses, preventing organ injury, and prolonging the survival of septic mice [42]. However, in the ACESS randomized trial, the use of the TLR4 inhibitor eritoran did not reduce 28-day mortality in patients with severe sepsis compared with placebo [43]. Jialing Hu et al. found that compared with normal tissues, TLR4 is highly expressed in COAD, ESCA, KICH, LIHC, pancreatic adenocarcinoma (PAAD), STAD and testicular germ cell tumor (TGCT), and high TLR4 expression may be a risk factor for an inferior prognosis in patients with STAD and TGCT [44]. Michael G. Kelly et al. found that the TLR-4-MyD88 signaling pathway may be a risk factor for developing cancer and chemoresistance to paclitaxel [45].

Therefore, targeting inflammation or immune modulation may be a promising treatment for patients with both cancer and sepsis. However, there is still a lack of research on the use of related drugs in these patients. In this study, we selected two drugs that have therapeutic effects on both cancer and sepsis and preliminarily analyzed their potential common targets in cancer and sepsis.

Ulinastatin is a broad-spectrum protease inhibitor that can inhibit trypsin, chymotrypsin, plasmin, human leukocyte elastase and hyaluronidase and is widely used clinically to treat acute pancreatitis and shock. A multicenter randomized controlled study has shown that treatment with intravenous administration of ulinastatin in patients with severe sepsis started within 48 h of organ dysfunction would result in a reduction in 28-day all-cause mortality to 7.3% from 20.3% [46]. At present, research on the effect of ulinastatin in cancer is relatively limited, mainly focusing on breast cancer. Ulinastatin has been confirmed to inhibit the expression of uPA, uPAR and p-ERK in breast cancer and enhance the effect of docetaxel on the invasion of breast cancer cells [47]. We identified that BCAT1, GM2A, IL10 and TOP2A may be potential targets of ulinastatin in sepsis and cancer. Shoichi Nishise et al. found that ulinastatin synergistically increases IL-10 production with monocyte adsorption stimuli [48]. More studies are needed to verify the effects of ulinastatin on these targets.

Oxymatrine (OMT) is a quinolizidine alkaloid derived from the roots of plants in the Sophora genus. It has been widely used as a treatment for chronic hepatitis infections and inflammatory diseases due to its effective immunomodulatory and anti-inflammatory properties. Research has found that OMT can prevent myocardial damage caused by septic shock [49, 50]. Oxymatrine plus entecavir can reduce the levels of endotoxin and inflammatory factors, protect organ function and boost recovery [18]. In cancer, OMT can induce apoptosis and inhibit the proliferation of cancer cells through multiple signaling pathways, such as Akt, epidermal growth factor receptor (EGFR), and nuclear factor kappa B (NF-κB) [51]. OMT can induce apoptotic cell death in human pancreatic cancer through the regulation of Bcl-2 and IAP families, release of mitochondrial cytochrome c and activation of caspase-3 [52]. Combination therapy with OMT and oxaliplatin (OXA) can enhance in vitro and in vivo cytotoxicity in colon cancer lines and mouse models [53]. Our study found that CSAD, G6PD, IL10, MMP9, and PYGL may be potential targets of OMT for the treatment of both sepsis and cancer. Several articles have initially explored the effects of OMT on MMP-9 and IL10 [54–57], but there is still a lack of in-depth research on CSAD, G6PD and PYGL. Additionally, no clinical trials have been carried out with OMT to assess the efficacy of its anticancer effects or safety in both sepsis and cancer patients [51].

In this study, we identified genes with common effects on both sepsis and cancer, which provides new insights into the association between sepsis and cancer. In addition, two drugs with potential clinical application value were identified. Further studies are required to validate the role of these common core genes in sepsis and cancer and to evaluate the potential utility of these drugs.

Ethics approval and consent to participate: Not applicable.

Consent for publication: Not applicable.

Availability of data and materials: The datasets analyzed during the current study are available in the GEO database (https://www.ncbi.nlm.nih.gov/geo/), the TCGA database (https://portal.gdc.cancer.gov/), the GO database (http://www.geneontology.org/), the reactome database (https://reactome.org/), the PubChem database (https://pubchem.ncbi.nlm.nih.gov/), the PharmMapper server (http://lilab-ecust.cn/pharmamapper/), the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) and the PDB database (https://www.rcsb.org/).

Competing interests: The authors declare that they have no competing interests.

Funding: This work was supported by the Shanghai Science and Technology Commission [grant numbers 20S31905300, 20Y11900900 and 21MC1930400] and the Clinical Research Plan of SHDC [grant number SHDC2020CR4067].

Authors' contributions: Ju MJ conceived and designed this research. Ding N, He HY, Han MC, Xuan LZ and Gu ZY completed the acquisition, analysis and interpretation of the data. Ding N, He HY and Han MC were responsible for writing the manuscript. Ju MJ and Zhong M substantively revised the manuscript. All the authors have read and approved the final manuscript.

Acknowledgments: Not applicable.

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Table 1 Top 20 core proteins in the PPI according to the degree value.

Protein	Degree
TLR4	36
IL1B	33
IL10	33
ITGAM	31
TLR2	28
PTPRC	27
CDK1	26
FOS	25
MMP9	23
ITGB2	23
SPI1	22
CD44	21
BIRC5	20
GAPDH	20
FCER1G	19
CD163	18
ICAM1	17
MPO	16
CCNB2	16
TOP2A	16

Table 2 Binding energy of molecular docking analysis.

Drug	Protein	Binding energy (kcal/mol)
Oxymatrine	CSAD	-7.6
	G6PD	-7.7
	IL10	-8.4
	MMP9	-8.0
	PYGL	-8.9
Ulinastatin	BCAT1	-8.0
	GM2A	-7.5
	IL10	-8.4
	TOP2A	-8.1

No competing interests reported.

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Identification of common core genes and pathways of sepsis and cancer by bioinformatics analysis

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Introduction

Materials and Methods

Data acquisition

Drug target analysis

Differential expression analysis

Functional enrichment analysis

Survival analysis

Clinical data analysis

Molecular docking

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