| Literature DB >> 29137275 |
Jianzhong Ai1,2, Tao Jin2,1, Lu Yang2,1, Qiang Wei1,2, Yang Yang3, Hong Li1,2, Ye Zhu4.
Abstract
Prostate cancer (PCa) is one of the most common diseases for male population, and the effective treatment for metastatic castration-resistant PCa is still lacking. To unravel the underlying mechanism of PCa cell migration, we plan to analyze the related crucial proteins and their roles. In our study, we firstly identify the differentially expressed proteins using quantitative proteomics, and confirm their mRNA expression using quantitative polymerase chain reaction (qPCR). The alterations of these proteins at DNA and mRNA levels are obtained from cBioPortal database. Furthermore, the functions of these proteins are evaluated using wound-healing assay. The quantitative proteomics identified vinculin (VCL) and filamin-C (FLNC) as two highly expressed proteins in PC3 cells, and the DNA and mRNA of these two proteins were amplified and upregulated in a part of PCa patients. Knockdown of VCL and FLNC gene expression significantly inhibit PCa cell migration. These findings suggest that VCL and FLNC identified by quantitative proteomics are highly expressed in PCa cells with high migration potential, and they could be effective targets for repressing PCa cell migration, paving a new avenue for the prognosis and treatment of advanced PCa.Entities:
Keywords: FLNC; VCL; migration; prostate cancer; quantitative proteomics
Year: 2017 PMID: 29137275 PMCID: PMC5669901 DOI: 10.18632/oncotarget.19397
Source DB: PubMed Journal: Oncotarget ISSN: 1949-2553
Figure 1VCL and FLNC mRNA and protein expression in PC3 cells is higher than that in LNCaP cells
(A) The protein levels of FLNC and VCL were approximately 10-fold and 20-fold overexpressed in PC3 cells. (B) The trend of FLNC and VCL mRNA expression was consistent with their protein overexpression. **, p<0.01; ***, p<0.001.
Figure 2Alterations of FLNC and VCL CNA and mRNA in PCa patients
The data were collected from independent cohort studies, the ratio was calculated using the following formula: %=Number of patients with CNA and/or mRNA upregulation/total patient number of a study. CNA, copy number amplification.
Figure 3Knock-down of FLNC and VCL using shRNA
(A) Significant downregulation of FLNC at mRNA level using its shRNA. (B) Significant downregulation of VCL at mRNA level using its shRNA. **, p<0.01; ****, p<0.0001.
Figure 4Detection of cell migration capabilities using a wound-healing assay
(A) Cells were scratched using a 10-μL tip, and the wound widths were recorded at 0, 8 and 24 h post-scratch. (B) The widths were measured using image J software, and the data were analyzed using Prism 6.0. *, p<0.05.