Expression profile-based screening for critical genes reveals S100A4, ACKR3 and CDH1 in docetaxel-resistant prostate cancer cells

Abstract

Docetaxel is a first-line anticancer drug widely used in the treatment of advanced prostate cancer. However, its therapeutic efficacy is limited by its side effects and the development of chemoresistance by the tumor. Using a gene differential expression microarray, we identified 449 genes differentially expressed in docetaxel-resistant DU145 and PC3 cell lines as compared to docetaxel-sensitive controls. Moreover, western blotting and immunohistochemistry revealed altered expression of S100A4, ACKR3 and CDH1in clinical tumor samples. Cytoscape software was used to investigate the relationship between critical proteins and their signaling transduction networks. Functional and pathway enrichment analyses revealed that these signaling pathways were closely related to cellular proliferation, cell adhesion, cell migration and metastasis. In addition, ACKR3 knockout using the crispr/cas9 method andS100A4knockdownusing targeted shRNA exerted additive effects suppressing cancer cell proliferation and migration. This exploratory analysis provides information about potential candidate genes. It also provides new insight into the molecular mechanism underlying docetaxel-resistance in androgen-independent prostate cancer and highlights potential targets to improve therapeutic outcomes.

Keywords: AIPC; differentially expressed genes; docetaxel; microarray.

Figure 1

Cell viability of PCa cells treated with different concentrations of Doc, volcano plots and Venn diagrams of DEGs. (A) DU145 cells treated with different concentrations of Doc; (B) PC3 cells treated with different concentrations of Doc. Viability of DU145 and PC3 cells was determined by MTT assay. Error bars = SEM (n = 6). (C and D) Volcano plots of DEGs from DU145R and PC3R compared with their parent cell lines respectively. X-axes show the fold changes (log-scaled), and Y-axes indicate p values (log-scaled). Red and green dots represent upregulated and downregulated genes, respectively. Grey dots represent non-DEGs. (E and F) VennPlots for the downregulated and upregulated DEGs. (G) Heatmap of DEGs overlapping between the DU145R and PC3R datasets. Red represents higher expression and green lower expression. The criteria used to select DEGs were P<0.05 and |log2 (fold-change)|>1. DEGs, differentially expressed genes.

Figure 2

GO and KEGG analysis of overlapping DEGs, expression levels of three critical genes. (A–C) GO analyses. Shown are the top 10 biological processes (A), cellular components (B), and molecular functions (C). (D) KEGG pathway analysis. (E–G) Expression levels of S100A4, ACKR3 and CDH1 in Doc-resistant PCa cells (DOC-R) and Doc-sensitive controls (DOC-S). (H–J) Comparison of gene expression levels between Doc-resistant and Doc-sensitive cells. *P < 0.05, **P <0.01, ***P < 0.001.

Figure 3

PPI networks constructed using the DEGs from microarray data. (A) Network of significant proteins from DU145R cells. (B) Network of significant proteins from PC3R cells. (C) Network derived from panel A with first-stage nodes associated with the core proteins S100A4, ACKR3 and CDH1. (D) Network derived from panel B with first neighbors associated with the core proteins S100A4, ACKR3 and CDH1. (E) Significant hub proteins extracted from network C. (F) Significant hub proteins extracted from network D. Red and green intensities indicate the degree of upregulation and downregulation, respectively.

Figure 4

Validation of DEGs identified in the microarray analysis. (A and B) qRT-PCR analysis of 18 DEGs in Doc-resistant DU145R and PC3R smples. (C–E) ROC curves for S100A4, ACKR3 and CDH1 in the microarray. (F–H) Correlation between the expression levels among S100A4, ACKR3 and CDH1. Expression data are represented by a log ratio calculated by comparing ΔCq from the DOC-R samples with ΔCq from the controls. ΔCq was calculated as the difference between Cq of the targeted genes and Cq of the endogenous control gene ACTB.

Figure 5

Detection of S100A4, ACKR3 and CDH1 protein expression in Doc-R PCa samples, Kaplan-Meier curves of OS in patients from TCGA-PRAD. (A) Western blots for S100A4, ACKR3 and CDH1 in DU145R and PC3R cells. (C–E) Immunostaining for S100A4, ACKR3 and CDH1 in representative samples of Doc-R tumor tissue from PCa patients. (F–H) Kaplan-Meier analysis of OS among patients in TCGA-PRAD dataset exhibiting high or low S100A4, ACKR3, or CDH1 expression.

Figure 6

Cell viability and migration effects after treatment with ACKR3 knockout and/or S100A4 knockdown. Western blots (A) ACKR3 knockout and (B) S100A4 knockdown. (C) Photomicrographs of DU145R and PC3R cells 48 h after ACKR3 knockout and/or S100A4 knockdown. (D and E) Relative viability rates among DU145R and PC3R cells after 24, 48 and 72 h under the indicated treatment condition. Viability among control cells was assigned a value of 100%. (F) Photomicrographs of migration of Doc-resistant DU145R and PC3R cells in wound healing assays 48 h after ACKR3 knockout and/or S100A4 knockdown. (G and H) Relative quantification of migration in wound healing assays under the indicated treatment condition. Migration of control cells was assigned a value of 100%. *P < 0.05, **P <0.01, ***P < 0.001.