Introduction
Acute pulmonary thromboembolism (PTE), the obstruction by a thrombus in the pulmonary artery or its branches, is the most common form of pulmonary embolism (PE) (1). The common pathology of PTE comprises hemodynamic instability, hypoxia, and pulmonary hypertension, which may cause heart failure with hypotension (2,3). PTE is reported to be a complex disease caused by a variety of factors, including the vascular microenvironment and vascular cell dysfunction (4,5). During an episode of PTE, a thrombus trapped in the pulmonary blood vessels injures the vascular endothelium, causing the unregulated release of pro‑inflammatory mediators (2,6). Pulmonary vascular remodeling caused by repeated pulmonary vascular injury may lead to secondary pulmonary hypertension (7), a major clinical consequence of PTE, which indicates that endothelial injury induced by PTE may be key in the pathophysiological consequences of PTE (8). PTE is a common disease, which is a contributing factor to the global non-communicable disease burden (8,9).
Previous studies have identified brain natriuretic peptide (BNP) and N-terminal pro-BNP (NT-proBNP) in the blood as biomarkers for predicting echocardiographic right ventricular dysfunction in patients with acute PTE (10,11). Furthermore, it is reported that troponin I, D-dimer, and plasma tenascin-C are positively correlated with the occurrence of PTE, whereas the erythrocyte sedimentation rate is downregulated in patients with PTE (12). Tang et al identified interleukin 8 (IL‑8), tumor necrosis factor-α (TNF‑α), and C‑X‑C motif chemokine ligand 5 as important factors in PTE in rabbits (13). In addition, it was revealed that these important genes are associated with cardiovascular disease, pulmonary disease, immune disease, and inflammation (13). However, small chemical molecules targeting differently expressed genes (DEGs) between the PTE group and the control group did not screen out. The use of small chemical molecules that target important disease-related genes has become an effective strategy for disease treatment (14,15).
In the present study, the GSE84738 dataset was obtained from the Gene Expression Omnibus (GEO) database. Following data preprocessing, the DEGs between a PTE group and control group were identified. Subsequently, the protein‑protein interaction (PPI) network of these DEGs was visualized using Cytoscape, and the most significantly clustered modules in the network were analyzed. Subsequently, functional enrichment analysis of the DEGs was performed. Furthermore, the chemical‑target interaction networks were investigated using the Comparative Toxicogenomics Database (CTD), visualized via Cytoscape. The present study identified the important genes and their functional enrichment in relation to the occurrence of PTE, and identified small chemical molecules potentially useful for PTE treatment. These findings may provide an important basis to detect mechanisms underlying PTE and potential treatment methods for PTE.
Materials and methods
Data sources
The RNA expression profiling dataset of the European rabbit (Oryctolagus cuniculus) was obtained from the GEO database in the National Center for Biotechnology Information (http://www.ncbi.nlm.nih.gov/geo/; accession no. GSE84738) (13). The dataset included eight samples (four control samples and four PTE samples), collected from rabbit pulmonary artery tissue. All samples were assessed, based on the GPL13288 Agilent‑020908 Oryctolagus cuniculus (rabbit) Oligo Microarray platform. The use of animals was approved by the Animal Ethics Committee of Affiliated Hospital of Nantong University (Nantong, China).
Screening of DEGs
The original file was downloaded and annotated. In cases where one gene was detected by multiple probes, the mean value of the probes was used to represent the expression level. The gene expression matrix was separated into a control group and a PTE group, and the DEGs between the two groups were screened according to the Linear Models for Microarray package (version 3.30.3, www.bioconductor.org/packages/release/bioc/html/limma.html) in R software. The P‑values of these DEGs were calculated and adjusted using an unpaired t-test and Benjamini-Hochberg method, respectively, at a significance level of P<0.05. Subsequently, the heatmap in R package, which is used for drawing heat-maps of gene expression, was utilized to plot the heat map of DEGs (16). Clustering between the different samples and different genes was performed using Pearson’s and Spearman’s correlation coefficients, respectively.
Analysis of the PPI network
The PPIs of the DEGs between the control group and PTE group were extracted from the Search Tool for the Retrieval of Interacting Genes/Proteins database (version 10.0, http://www.string‑db.org/) with default parameters (species: Oryctolagus cuniculus) (17). Those PPIs meeting the combined score requirement of >0.4 were used to construct the PPI network, which was then visualized using Cytoscape (version 3.2.0, http://cytoscape.org/) (18). In the PPI network, each node represents a protein, and each edge between two nodes represents an interaction between these two proteins. The degree is defined as the number of proteins interacting with the node.
Furthermore, the degree centrality, a network topology index, was used to analyze the scores of nodes in the network. The nodes with higher degrees are considered to be important in the PPI network, and may be the key nodes.
Subnet module analysis
Genes often have a regulatory role by interacting with other cellular components. Proteins produced by genes in the same module tend to have the same function or similar functions, and where they act as a module they may have the same biological role. The most significantly clustered modules in the network were analyzed using the Multi Contrast Delayed Enhancement plugin of Cytoscape (19).
GO and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of regulatory network modules
In order to examine the gene function and pathways related to the DEGs, the Database for Annotation Visualization and Integrated Discovery (DAVID) online tool (version 6.8, https://david‑d.ncifcrf.gov/) (20) was used to perform the Gene Ontology (GO) functional and pathway enrichment analyses of DEGs based on the GO database (20) and KEGG pathway database (21). Only function terms with an adjusted P‑value of <0.05 and count ≥2 were considered as significant.
Analysis of disease‑related chemistry small molecule‑target gene interaction network
The CTD, a publicly available database (http://ctdbase.org/), provides manually curated information on chemical-disease, gene-disease associations and chemical‑gene/protein interactions. Based on PE‑related chemical-gene interaction data provided by the CTD (22), the chemical‑gene interaction association pairs were visualized via Cytoscape. Chemicals interacting with a higher number of DEGs may be more important in this disease.
Results
Identification of DEGs
A total of 548 (262 upregulated and 286 downregulated DEGs) were identified in the PTE group, compared with the control group. The results of the hierarchical clustering heat map of DEGs showed that these DEGs were clearly distinguishable between the different sample types (Fig. 1).
Functional enrichment analysis
The upregulated DEGs were significantly enriched in 27 KEGG pathways, 51 GO biological processes (BPs), 27 GO cellular components (CCs) and 10 GO molecular functions (MFs). The downregulated genes were significantly enriched in 11 KEGG pathways, 15 GO BPs, 14 GO CCs, and 11 GO MFs. The enriched results are presented in Table I. The upregulated DEGs were largely enriched in the phagosome (P=5.51E-09) and hematopoietic (P=4.39E-07, e.g. TNF) cell lineages in the KEGG pathways, and the downregulated DEGs were enriched in the arrhythmogenic right ventricular cardiomyopathy (P=5.12E‑05) and calcium signaling (P=2.26E‑04) pathways in the KEGG pathways (Table I). Interleukin‑6 production (P=4.02E-07, e.g. TNF) in GO BP, external side of plasma membrane (P=1.46E-05, e.g. TNF), NADPH oxidase complex (P=2.08E‑05) in GO CC, and low‑density lipoprotein particle binding (P=6.06E‑06) in MF were the upregulated genes, and were enriched among the abovementioned GO terms (Fig. 2A). The downregulated genes were largely enriched in lens development in camera-type eye (P=2.40E-05), sarcolemma (P=5.93E-05), and structural constituent of eye lens (P=1.01E-08) (Fig. 2B).
PPI network and module analysis
A total of 342 nodes and 1,128 interactional pairs were included in the PPI network. Among them, 10 proteins had >25 degrees (Fig. 3). TNF and ERBB2 were the upregulated genes with the highest degree in the PPI network, with 47 and 40 degrees respectively. C‑JUN was the downregulated gene with the highest degree of 46. Furthermore, C‑JUN was an interacting protein of TNF and ERBB2.
In total, three subnet modules (A‑C) were identified with a score >4. Module A (score=6.769) included 14 nodes (e.g. actin β) and 44 interactional pairs (Fig. 4A). Module B (score=6) had seven nodes (e.g. potassium voltage-gated channel subfamily B member 2) and 18 interactional pairs (Fig. 4B). Module C (score=4.435) included 24 nodes (e.g. TNF and C‑JUN) and 51 interactional pairs (Fig. 4C). Genes in module A were most enriched in cardiac muscle contraction (P=3.97E-03), hypertrophic cardiomyopathy (P=4.79E-03) and dilated cardiomyopathy (P=5.45E-03). Genes in module B were most enriched in the oxytocin signaling pathway (P=1.33E‑03), whereas biosynthesis of antibiotics in the KEGG pathways (P=1.61E‑03) was enriched in module C genes.
PTE‑related chemical‑small molecule‑target gene inter‑ action network
In the CTD database, 50,719 PE-related chemical‑target interactions, including 1,537 DEG‑chemical interactions, were detected (Fig. 5). A total of 1,537 interactional pairs, 127 upregulated DEGs, 105 downregulated DEGs, and 117 chemicals, including four with a degree of >100 [ethinyl (135), cyclosporine (126), thrombomodulin precursor (THBD) (113) and tretinoin (111)], were found in the DEG‑chemical interaction network. Among them, ethinyl targeted TNF, whereas ERBB2 were targeted by cyclosporine. In addition to THBD being an upregulated gene, it was an important chemical interacting with ethinyl, cyclosporine and tretinoin. The chemical tretinoin targeted ERRB2 (Table II).
Discussion
The findings of the present study demonstrated that TNF and ERBB2 were upregulated, and C‑JUN was the main downregulated gene in the PTE group. Additionally, ethinyl, cyclosporine, and tretinoin may have significant effects in the treatment of PTE. Among them, TNF was targeted by ethinyl and cyclosporine, ERBB2 was targeted by cyclosporine and tretinoin, and C‑JUN was targeted by TNF and ERBB2.
TNF is a multifunctional pro‑inflammatory cytokine produced by macrophages. Previous studies have indicated that inflammation is associated with the occurrence of PTE (23). Due to stimulation caused by hypoxia and trauma during PTE, inflammation may trigger pulmonary platelet activation and endothelial dysfunction (24). Additionally, systemic inflammatory and leukocytosis responses are prognostic factors for mortality following 30 days of PTE (25). In addition, pro‑inflammatory factors, including TNF and IL‑6, are significantly correlated with PTE mortality rate (26,27). Consistent with previous studies, the results of the present study suggested that, compared with the control group, TNF was upregulated in the PTE group, and that it was mainly enriched in the hematopoietic cell lineage, positive regulation of interleukin‑6 production, and external side of the plasma membrane. Furthermore, it was targeted by small chemical molecules, including ethinyl and cyclosporine. Ethinyl is a contraceptive, although oral ethinyl increases the risk of venous thromboembolisms including PTE (28,29). Cyclosporine is a calcineurin inhibitor (CNI), which is beneficial for reducing the incidence of complications during homologous transplantation, including vascular toxicity (30). CNIs induce proinflammatory cytokine production and endothelial activation in the isolated mouse aorta and vascular smooth muscle cells (31). Cyclosporine also increases the mRNA levels of proinflammatory cytokines, including TNF‑α, IL‑6, C‑C motif chemokine ligand 5 (CCL5), and CCL2, which are involved in vascular injury (31). Consistent with previous studies, the results of the present study indicated that TNF was targeted by cyclosporine. Cyclosporine promotes inflammation‑induced monocyte adhesion to human intestinal endothelial cells (32). Therefore, ethinyl and cyclosporine may increase the incidence of PTE via targeting TNF and promoting the onset of inflammation.
Cyclosporine is also targeted by ERBB2, which is a member of the epidermal growth factor receptor family. ERBB2 is increased in patients with cancer, including breast and ovarian cancer (33). In breast cancer, ERBB2 is important in regulating oncogenic microRNA expression (34). ERBB2 is also a potent independent predictor of the metastatic potential of breast cancer cells (35). However, few studies appear to have investigated the role of ERBB2 in PTE. It is reported that breast cancer is closely associated with the development of vascular emboli (36). In the present study, ERBB2 was the important upregulated gene in the PTE group, as it had a higher degree in the PPI network, and it was a target of the small chemical molecules, cyclosporine and tretinoin. Tretinoin, a metabolite of retinol, can repair lung tissue in pulmonary emphysema model rats (37). The role of tretinoin in promoting alveolar regeneration involves the regulation of vascular endothelial growth factor (VEGF), VEGF receptor 2, and matrix metallo-proteinase 1 (38). Therefore, tretinoin may reduce the degree of lung injury in PTE by downregulating ERBB2. Cyclosporine may contribute to the occurrence of PTE via targeting ERBB2 and TNF, thereby regulating the expression of C‑JUN.
In humans, C-JUN is a protein encoded by JUN. C-JUN in combination with C‑FOS, forms the early response transcription factor, activator protein 1 (39). In addition, C-JUN cooperates with nuclear factor‑κB to prevent apoptosis induced by TNF-α. C-JUN can also protect hepatocytes from apoptosis, as hepatocytes lacking C‑JUN show increased sensitivity to TNF-α‑induced apoptosis (40). However, the role of C-JUN in PTE has not been reported. As stated above, pro‑inflammatory factors, including TNF and IL‑6, show a significant correlation with PTE mortality rate. In addition, β4 integrin forms a complex with ERBB2 and enhances activation of the transcription factors signal transducer and activator of transcription 3 and C-JUN (41). C-JUN is also necessary for ERBB2-mediated hyperproliferation. Therefore, C‑JUN may regulate TNF and ERBB2.
However, the results of the present study have not been experimentally validated, which is a limiting factor that requires resolution in the future. In addition, the regulation of small chemical molecules by PTE-related gene expression was not investigated. The sample size used in the present study was also small (four control and four PTE pulmonary artery samples).
In conclusion, the findings of the present study indicate that small chemical molecules, cyclosporine and ethinyl, may trigger PTE by regulating the expression of TNF and ERBB2. Furthermore, tretinoin may delay the progression of PTE via targeting ERBB2. Taken together, cyclosporine, ethinyl, and tretinoin may be promising potential targets for PTE treatment.