Comprehensive Genomic Analysis of Puerarin in Inhibiting Bladder Urothelial Carcinoma Cell Proliferation and Migration

Abstract

Background: Bladder urothelial carcinoma (BUC) ranks second in the incidence of urogenital system tumors, and the treatment of BUC needs to be improved. Puerarin, a traditional Chinese medicine (TCM), has been shown to have various effects such as anti-cancer effects, the promotion of angiogenesis, and anti-inflammation. This study investigates the effects of puerarin on BUC and its molecular mechanisms.

Methods: Through GeneChip experiments, we obtained differentially expressed genes (DEGs) and analyzed these DEGs using the Ingenuity® Pathway Analysis (IPA®), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) pathway enrichment analyses. The Cell Counting Kit 8 (CCK8) assay was used to verify the inhibitory effect of puerarin on the proliferation of BUC T24 cells. String combined with Cytoscape® was used to create the Protein-Protein Interaction (PPI) network, and the MCC algorithm in cytoHubba plugin was used to screen key genes. Gene Set Enrichment Analysis (GSEA®) was used to verify the correlation between key genes and cell proliferation.

Results: A total of 1617 DEGs were obtained by GeneChip. Based on the DEGs, the IPA® and pathway enrichment analysis showed they were mainly enriched in cancer cell proliferation and migration. CCK8 experiments proved that puerarin inhibited the proliferation of BUC T24 cells, and its IC50 at 48 hours was 218μmol/L. Through PPI and related algorithms, 7 key genes were obtained: ITGA1, LAMA3, LAMB3, LAMA4, PAK2, DMD, and UTRN. GSEA showed that these key genes were highly correlated with BUC cell proliferation. Survival curves showed that ITGA1 upregulation was associated with poor prognosis of BUC patients.

Conclusion: Our findings support the potential antitumor activity of puerarin in BUC. To the best of our knowledge, bioinformatics investigation suggests that puerarin demonstrates anticancer mechanisms via the upregulation of ITGA1, LAMA3 and 4, LAMB3, PAK2, DMD, and UTRN, all of which are involved in the proliferation and migration of bladder urothelial cancer cells.

Keywords: Puerarin; bioinformatics.; bladder urothelial carcinoma (BUC); migration; proliferation; protein-protein interaction.

Fig. (1)

The DEGs. (A) The dose-response curve of puerarin on T24 cells demonstrates the IC₅₀ was 218 µmol/L. (B) The volcano plot of all genes contained in Affymetrix^® gene expression microarrays. The blue color on the left side represents 1053 downregulated genes, the red color on the right side represents 564 upregulated genes.

Fig. (2)

Flowchart of this study. The flowchart elucidates the research strategy of this study.

Fig. (3)

Diseases and functions and canonical pathway analysis by IPA^®. (A) The top 20 cluster of the differential genes in the disease and functional categories, which were ranked by -log (p-value). (B) The top 20 pathways in canonical pathway analysis by IPA^®.

Fig. (4)

Upstream regulator (MAPK1) by IPA^®. Gene networks of upstream regulators related to tumorigenesis: MAPK1 network. The color shade and font size of a gene tag are proportional to the absolute value of its log (FC), with red representing its log (FC) as positive and green representing its log (FC) as negative.

Fig. (5)

Pathway enrichment analyses by metascape^® showed that DEGs were enriched in pathways related to cancer, cell proliferation and migration. (A) The top 20 results of the KEGG pathway analysis ranked by -log p. (B) The top 20 results of biological processes ranked by -log p.

Fig. (6)

Protein-protein interaction network by String^® and Cytoscape^®. The PPI network of the top 50 genes in MCC and the color depth of the circle are proportional to the importance of corresponding genes.

Fig. (7)

GSEA revealed that key genes were enriched in proliferation. Heatmap of gene expression abundance. Graph of GSEA enrichment analysis results.

Fig. (8)

Survival curves by GEPIA^®. The overall survival curves of key genes show that BUC patients with high expression of ITGA1 was significantly related to low survival and high expression of LAMA4 and DMD was very likely associated with low survival.

Fig. (9)

(A, B). Separate pathway enrichment of up- and downregulated genes.