1. Urinary glycoproteins associated with aggressive prostate cancer
- Author
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Hui Zhang, Weiming Yang, Rodrigo Vargas Eguez, Lori J. Sokoll, Zhen Zhang, Alan W. Partin, Leslie A. Mangold, Daniel W. Chan, Kyung Cho Cho, Mingming Dong, Shao Yung Chen, T. Mamie Lih, Yangying Zhou, and Naseruddin Höti
- Subjects
Adult ,Male ,0301 basic medicine ,Oncology ,medicine.medical_specialty ,Urinary system ,Medicine (miscellaneous) ,Urine ,Logistic regression ,Cohort Studies ,03 medical and health sciences ,Prostate cancer ,0302 clinical medicine ,Text mining ,Internal medicine ,Biomarkers, Tumor ,medicine ,Humans ,glycoproteomics ,urinary biomarkers ,Pharmacology, Toxicology and Pharmaceutics (miscellaneous) ,Aged ,Digital Rectal Examination ,Glycoproteins ,mass spectrometry ,Genitourinary system ,business.industry ,Prostatic Neoplasms ,Middle Aged ,Prostate-Specific Antigen ,medicine.disease ,Confidence interval ,030104 developmental biology ,ROC Curve ,aggressive prostate cancer ,noninvasive prostate cancer ,030220 oncology & carcinogenesis ,Cohort ,Feasibility Studies ,Kallikreins ,Neoplasm Grading ,business ,Research Paper - Abstract
Background: There is an urgent need for the detection of aggressive prostate cancer. Glycoproteins play essential roles in cancer development, while urine is a noninvasive and easily obtainable biological fluid that contains secretory glycoproteins from the urogenital system. Therefore, here we aimed to identify urinary glycoproteins that are capable of differentiating aggressive from non-aggressive prostate cancer. Methods: Quantitative mass spectrometry data of glycopeptides from a discovery cohort comprised of 74 aggressive (Gleason score ≥8) and 68 non-aggressive (Gleason score = 6) prostate cancer urine specimens were acquired via a data independent acquisition approach. The glycopeptides showing distinct expression profiles in aggressive relative to non-aggressive prostate cancer were further evaluated for their performance in distinguishing the two groups either individually or in combination with others using repeated 5-fold cross validation with logistic regression to build predictive models. Predictive models showing good performance from the discovery cohort were further evaluated using a validation cohort. Results: Among the 20 candidate glycoproteins, urinary ACPP outperformed the other candidates. Urinary ACPP can also serve as an adjunct to serum PSA to further improve the discrimination power for aggressive prostate cancer (AUC= 0.82, 95% confidence interval 0.75 to 0.89). A three-signature panel including urinary ACPP, urinary CLU, and serum PSA displayed the ability to distinguish aggressive prostate cancer from non-aggressive prostate cancer with an AUC of 0.86 (95% confidence interval 0.8 to 0.92). Another three-signature panel containing urinary ACPP, urinary LOX, and serum PSA also demonstrated its ability in recognizing aggressive prostate cancer (AUC=0.82, 95% confidence interval 0.75 to 0.9). Moreover, consistent performance was observed from each panel when evaluated using a validation cohort. Conclusion: We have identified glycopeptides of urinary glycoproteins associated with aggressive prostate cancer using a quantitative mass spectrometry-based glycoproteomic approach and demonstrated their potential to serve as noninvasive urinary glycoprotein biomarkers worthy of further validation by a multi-center study.
- Published
- 2020