Identification of Target Genes Related to Sulfasalazine in Triple-Negative Breast Cancer Through Network Pharmacology

Abstract

BACKGROUND The anti-inflammatory drug sulfasalazine (SAS) has been confirmed to inhibit the growth of triple-negative breast cancer (TNBC), but the mechanism is not clear. The aim of this study was to use network pharmacology to find relevant pathways of SAS in TNBC patients. MATERIAL AND METHODS Through screening of the GeneCards, CTD, and ParmMapper databases, potential genes related to SAS and TNBC were identified. In addition, gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed using the R programming language. Protein-protein interaction networks were constructed using Cytoscape. The Kaplan-Meier plotter screened genes related to TNBC prognosis. TNBC patient gene expression profiles and clinical data were downloaded from The Cancer Genome Atlas database. A heatmap was generated using the R programming language that presents the expression of potential target genes in patients with TNBC. RESULTS Eighty potential target genes were identified through multiple databases. The bioinformatical analyses predicted the interrelationships, potential pathways, and molecular functions of the genes from multiple aspects, which are associated with physiological processes such as the inflammatory response, metabolism of reactive oxygen species (ROS), and regulation of proteins in the matrix metalloproteinase (MMP) family. Survival analysis showed that 12 genes were correlated with TNBC prognosis. Heatmapping showed that genes such as those encoding members of the MMP family were differentially expressed in TNBC tissues and normal tissues. CONCLUSIONS Our analysis revealed that the main reasons for the inhibitory effect of SAS on TNBC cells may be inhibition of the inflammatory response and MMP family members and activation of ROS.

Figure 1

Flowchart of screening of 80 genes.

Figure 2

(A) Chemical structure of sulfasalazine that was downloaded from the PubChem database. (B) Identification of potential target genes associated with both SAS and TNBC.

Figure 3

(A, B) Functional and pathway enrichment analysis of genes related to SAS and TNBC.

Figure 4

(A) Protein–protein interaction (PPI) network of 80 genes associated with SAS and TNBC. The 8 yellow nodes indicate the 8 genes that are connected to the highest number of other genes. (B, C) From the entire PPI network, 2 significant subgroups were identified by MCODE in Cytoscape. (D) The top 15 nodes with the most connections among 80 nodes.

Figure 5

The 12 genes identified by the Kaplan-Meier survival analysis were significantly associated with relapse-free survival (RFS) among the 80 genes.

Figure 6

Heatmap of 80 differentially expressed genes. Red color represents upregulated genes, and green represents downregulated genes in TNBC patients and normal patients.

Figure 7

Schematic diagram of potential pathways for SAS to inhibit growth and proliferation of TNBC cells.