Exploring cisplatin resistance in ovarian cancer through integrated bioinformatics approach and overcoming chemoresistance with sanguinarine

Abstract

Ovarian cancer is refractory in response towards platinum-based chemotherapy, and resistance frequently develops. We attempted to identify the driving pathways in cisplatin-resistant ovarian cancer and develop targeted therapies to overcome this resistance. Using an integrated bioinformatics approach, a GSE15372 database from NCBI's Gene Expression Omnibus database was obtained for identifying differentially expressed genes (DEGs), in which 535 DEGs were found (407 up-regulated and 128 down-regulated) in association with ovarian cancer cisplatin-resistance. Gene ontology and pathway enrichment analyses further found that aberrant activation of EGFR/ErbB2 signaling was the driving event in resistant cells. A network of dysregulated genes was built based on these identified DEGs and protein-protein interaction network, which led to the identification of 7 potential inhibitors based on screening a 77 small molecule natural product library. Sanguinarine, alone and in combination with cisplatin, was found to significantly suppress the proliferation of wt/resistant ovarian cancer cells in vitro and the growth of parental and resistant ovarian xenograft tumors in vivo. Our study suggests that EGFR/ErbB2 activation is one of the driving pathways in developing cisplatin-resistance in ovarian cancer, and that sanguinarine has the potential to be developed as an effective therapy to overcome this therapeutic resistance.

Keywords: Ovarian cancer; cisplatin-resistance; differentially expressed genes; gene expression profile; sanguinarine.

Figure 1

Cross-networks for the Differentially Expressed Genes (DEGs). A. Box graphs for the genes in chip data. B. Box graphs for RMA in chip data. C. Signal Intensity Scatter plot. D. Scatter plot for RMA Signal Intensity. E. Up-regulated genes. F. Down-regulated genes.

Figure 2

Effect of sanguinarine on the growth of SKOV3, SKOV3/DDP, A2780, and A2378/DDP cells. A. Cell growth imagines (100×) were taken 48 h after treatments. B. OD values for cell viability assessments. Control group treated with PBS; sanguinarine group treated with 2.24 μmol/L of sanguinarine dissolved in DMSO; cisplatin group treated with 2.5 μg/ml of cisplatin; and combined group treated with sanguinarine (2.24 μmol/L) and cisplatin (2.5 μg/ml). C, D. Cell growth inhibition assessments for SKOV3 and SKOV3-DDP cells over 72 h. E, F. Cell growth inhibition assessments for A2780 and A2780-DDP cells over 72 h. Statistical analysis compared to control: *, P<0.05; **, P<0.01; and ***, P<0.001. Three independent experiments were performed.

Figure 3

Effect of sanguinarine on the growth of xenograft tumors developed from SKOV3 and SKOV3/DDP cells. A, B. Animal photos. C. Tumor photos. D, E. Tumor growth curves for SKOV3 and SKOV3-DDP xenografts (n=3 in each group). F. Tumor volume doubling time. G. Tumor weights for each group. H, I. Body weights of mice. Compared to control *, P<0.05; **, P<0.01; and ***, P<0.001.

Figure 4

Mechanisms by which sanguinarine suppresses resistant ovarian cancer cells. A. Biomarker expression in xenograft tumors. Tumor sections were stained with H & E, and IHC with specified antibodies. Representative images were taken and shown in designated magnitude. B. Analysis of the IHC intensity of biomarkers in parental and cisplatin-resistant ovarian cancer xenograft tumors. Percentage of positive-cells was defined as the average of positively stained cells in 5 random areas in a slide. C. Schematic drawing of potential mechanisms sanguinarine overcomes chemoresistance in resistant ovarian cancer.