Bioinformatics-based study to detect chemical compounds that show potential as treatments for pulmonary thromboembolism

Abstract

The objectives of the present study comprised the recognition of major genes related to pulmonary thromboembolism (PTE) and the evaluation of their functional enrichment levels, in addition to the identification of small chemical molecules that may offer potential for use in PTE treatment. The RNA expression profiling of GSE84738 was obtained from the Gene Expression Omnibus database. Following data preprocessing, the differently expressed genes (DEGs) between the PTE group and the control group were identified using the Linear Models for Microarray package. Subsequently, the protein‑protein interaction (PPI) network of these DEGs was examined using the Search Tool for the Retrieval of Interacting Genes/Proteins database, visualized via Cytoscape. The most significantly clustered modules in the network were identified using Multi Contrast Delayed Enhancement, a plugin of Cytoscape. Subsequently, functional enrichment analysis of the DEGs was performed, using the Database for Annotation Visualization and Integrated Discovery tool. Furthermore, the chemical‑target interaction networks were investigated using the Comparative Toxicogenomics Database as visualized via Cytoscape. A total of 548 DEGs (262 upregulated and 286 downregulated) were identified in the PTE group, compared with the control group. The upregulated and downregulated genes were enriched in Gene Ontology terms related to inflammation and eye sarcolemma, respectively. Tumor necrosis factor (TNF) and erb‑b2 receptor tyrosine kinase 2 (ERBB2) were upregulated genes that ranked higher in the PPI network (47 and 40 degrees, respectively) whereas C‑JUN was the most downregulated gene (46). Small chemical molecules ethinyl (135), cyclosporine (126), thrombomodulin precursor (113) and tretinoin (111) had >100 degrees in the DEG‑chemical interaction network. In addition, ethinyl targeted to TNF, whereas TNF and ERBB2 were targeted by cyclosporine, and tretinoin was a targeted chemical of ERBB2. Therefore, cyclosporine, ethinyl, and tretinoin may be potential targets for PTE treatment.

Figure 1

Clustered heat map of differentially expressed genes in the PTE group compared with the control group. The abscissa represents different samples, and the ordinate represents different genes. The red boxes indicate upregulated genes, and the green boxes indicate downregulated genes. Clustering between different samples and different genes were performed based on Pearson’s and Spearman’s correlation coefficients, respectively. PTE, pulmonary thromboembolism.

Figure 2

Top GO terms enriched in DEGs. Top 5 GO terms enriched in (A) upregulated and (B) downregulated DEGs. Red, yellow, and green boxes represent GO BP, CC, and MF, respectively. GO, gene ontology; DEGs, differentially expressed genes; BP, biological process; CC, cellular component; MF, molecular function.

Figure 3

Protein‑protein interaction network of differentially expressed genes. Red circles and green squares represent the upregulated and downregulated genes, respectively. Lines indicate protein-protein interaction.

Figure 4

Results of subnet analysis of the PPI network. (A) Module A, (B) module B, and (C) module C of the PPI network are presented. Red circles and green squares represent the upregulated and downregulated genes, respectively. The lines indicate PPI. PPI, protein‑protein interaction.

Figure 5

Chemical‑target interactional network. The blue quadrilaterals, red circles, and green squares represent the chemical small molecules, the upregulated genes, and the downregulated genes, respectively. Lines indicate protein‑protein interaction.