TY - JOUR
T1 - High Throughput Isolation and Data Independent Acquisition Mass Spectrometry (DIA-MS) of Urinary Extracellular Vesicles to Improve Prostate Cancer Diagnosis
AU - Zhang, Hao
AU - Zhang, Gui-Yuan
AU - Su, Wei-Chao
AU - Chen, Ya-Ting
AU - Liu, Yu-Feng
AU - Wei, Dong
AU - Zhang, Yan-Xi
AU - Tang, Qiu-Yi
AU - Liu, Yu-Xiang
AU - Wang, Shi-Zhi
AU - Li, Wen-Chao
AU - Wesselius, Anke
AU - Zeegers, Maurice P.
AU - Zhang, Zi-Yu
AU - Gu, Yan-Hong
AU - Tao, W. Andy
AU - Yu, Evan Yi-Wen
N1 - Funding Information:
This research was funded by the National Natural Science Foundation of China (82204033), Natural Science Foundation of Jiangsu Province (BK20220826), Fundamental Research Funds for the Central Universities of China (2242022R10062/3225002202A1), and Medical Foundation of Southeast University (4060692202/021).
Publisher Copyright:
© 2022 by the authors.
PY - 2022/11/23
Y1 - 2022/11/23
N2 - Proteomic profiling of extracellular vesicles (EVs) represents a promising approach for early detection and therapeutic monitoring of diseases such as cancer. The focus of this study was to apply robust EV isolation and subsequent data-independent acquisition mass spectrometry (DIA-MS) for urinary EV proteomics of prostate cancer and prostate inflammation patients. Urinary EVs were isolated by functionalized magnetic beads through chemical affinity on an automatic station, and EV proteins were analyzed by integrating three library-base analyses (Direct-DIA, GPF-DIA, and Fractionated DDA-base DIA) to improve the coverage and quantitation. We assessed the levels of urinary EV-associated proteins based on 40 samples consisting of 20 cases and 20 controls, where 18 EV proteins were identified to be differentiated in prostate cancer outcome, of which three (i.e., SERPINA3, LRG1, and SCGB3A1) were shown to be consistently upregulated. We also observed 6 out of the 18 (33%) EV proteins that had been developed as drug targets, while some of them showed protein-protein interactions. Moreover, the potential mechanistic pathways of 18 significantly different EV proteins were enriched in metabolic, immune, and inflammatory activities. These results showed consistency in an independent cohort with 20 participants. Using a random forest algorithm for classification assessment, including the identified EV proteins, we found that SERPINA3, LRG1, or SCGB3A1 add predictable value in addition to age, prostate size, body mass index (BMI), and prostate-specific antigen (PSA). In summary, the current study demonstrates a translational workflow to identify EV proteins as molecular markers to improve the clinical diagnosis of prostate cancer.
AB - Proteomic profiling of extracellular vesicles (EVs) represents a promising approach for early detection and therapeutic monitoring of diseases such as cancer. The focus of this study was to apply robust EV isolation and subsequent data-independent acquisition mass spectrometry (DIA-MS) for urinary EV proteomics of prostate cancer and prostate inflammation patients. Urinary EVs were isolated by functionalized magnetic beads through chemical affinity on an automatic station, and EV proteins were analyzed by integrating three library-base analyses (Direct-DIA, GPF-DIA, and Fractionated DDA-base DIA) to improve the coverage and quantitation. We assessed the levels of urinary EV-associated proteins based on 40 samples consisting of 20 cases and 20 controls, where 18 EV proteins were identified to be differentiated in prostate cancer outcome, of which three (i.e., SERPINA3, LRG1, and SCGB3A1) were shown to be consistently upregulated. We also observed 6 out of the 18 (33%) EV proteins that had been developed as drug targets, while some of them showed protein-protein interactions. Moreover, the potential mechanistic pathways of 18 significantly different EV proteins were enriched in metabolic, immune, and inflammatory activities. These results showed consistency in an independent cohort with 20 participants. Using a random forest algorithm for classification assessment, including the identified EV proteins, we found that SERPINA3, LRG1, or SCGB3A1 add predictable value in addition to age, prostate size, body mass index (BMI), and prostate-specific antigen (PSA). In summary, the current study demonstrates a translational workflow to identify EV proteins as molecular markers to improve the clinical diagnosis of prostate cancer.
KW - Male
KW - Humans
KW - Prostate
KW - Proteomics/methods
KW - Mass Spectrometry/methods
KW - Extracellular Vesicles/metabolism
KW - Prostatic Neoplasms/diagnosis
U2 - 10.3390/molecules27238155
DO - 10.3390/molecules27238155
M3 - Article
C2 - 36500247
SN - 1420-3049
VL - 27
JO - Molecules
JF - Molecules
IS - 23
M1 - 8155
ER -