2020
DOI: 10.3389/fonc.2020.00493
|View full text |Cite
|
Sign up to set email alerts
|

Quantitative SWATH-Based Proteomic Profiling for Identification of Mechanism-Driven Diagnostic Biomarkers Conferring in the Progression of Metastatic Prostate Cancer

Abstract: Prostate cancer (PCa), the most frequently diagnosed malignancy in men is associated with significant mortality and morbidity. Therefore, demand exists for the identification of potential biomarkers for patient stratification according to prognostic risks and the mechanisms involved in cancer development and progression to avoid over/under treatment of patients and prevent relapse. Quantitative proteomic mass spectrometry profiling and gene enrichment analysis of TGF-β induced-EMT in human Prostate androgen-de… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
2
1

Citation Types

1
21
0

Year Published

2020
2020
2024
2024

Publication Types

Select...
9

Relationship

0
9

Authors

Journals

citations
Cited by 31 publications
(22 citation statements)
references
References 43 publications
1
21
0
Order By: Relevance
“…We increased the depth of proteomic profiling in our cell models via SWATH-MS strategy: an accurate, highly reproducible label-free approach in whole proteome quantifi-cation [11,12]. Two recent studies that used SWATH-MS strategy on LNCaP cells validated the total number of proteins identified in our study [13][14][15]. In addition, we observed a limited correlation between PCa transcriptomic and proteomic datasets, in agreement with previous PCa omics-based research [16,17].…”
Section: Discussionsupporting
confidence: 87%
“…We increased the depth of proteomic profiling in our cell models via SWATH-MS strategy: an accurate, highly reproducible label-free approach in whole proteome quantifi-cation [11,12]. Two recent studies that used SWATH-MS strategy on LNCaP cells validated the total number of proteins identified in our study [13][14][15]. In addition, we observed a limited correlation between PCa transcriptomic and proteomic datasets, in agreement with previous PCa omics-based research [16,17].…”
Section: Discussionsupporting
confidence: 87%
“…HNRNPA2B1 also highly expressed in CRPC and associated with tumor progression and prognosis (Cheng et al, 2020). Through quantitative proteomic mass spectrometry profiling, HNRNPA2B1 was likely involved in TGF-β induced-EMT transition of PC (Singh and Sharma, 2020). NXF1 matters in interaction between two m 6 A readers, YTHDC1 and SRSF3, for mRNA export promotion (Roundtree et al, 2017b).…”
Section: Discussionmentioning
confidence: 99%
“…Cell lines may have very distinct proteomic and phosphoproteomic profiles compared to each other and to clinical samples, and cell lines are reported to have distinct phosphoproteomic profiles from primary and metastatic CRPC tissues [ 55 , 60 ]. Several studies have profiled the large-scale proteomes of prostate cancer cell lines, especially for PC-3 and LNCaP [ 96 , 98 ], and identified numerous proteins with different expression levels. Although the cell lines have differences in terms of their AR status and androgen-dependence (for example, PC-3 is androgen-independent (AI) and LNCaP is androgen-dependent (AD)), conclusions based on differences in DEPs between these cell lines in terms of androgen dependence should be validated by other assays.…”
Section: Large-scale Proteomes Of Prostate Cancer Models Provide Mechanistic Insightsmentioning
confidence: 99%
“…Interestingly, in the castration-resistant line, induction of the cytoplasmic endoribonuclease microRNA regulator Dicer was found, indicating alterations in miRNA regulation in these cells. Singh and Sharma [ 98 ] analyzed protein expression phenotypes of prostate cancer cell lines PC-3 and LNCaP by SWATH quantitative MS upon TGF-b-induced epithelial to mesenchymal transition (EMT). In the AD LNCaP cell line, 2 proteins were significantly upregulated, and 126 were downregulated.…”
Section: Large-scale Proteomes Of Prostate Cancer Models Provide Mechanistic Insightsmentioning
confidence: 99%