Identification of blood protein biomarkers associated with prostate cancer risk using genetic prediction models: analysis of over 140,000 subjects

Author:

Zhong Hua1,Zhu Jingjing1,Liu Shuai1,Ghoneim Dalia H1,Surendran Praveen2,Liu Tao3,Fahle Sarah2,Butterworth Adam24,Ashad Alam Md56,Deng Hong-Wen5,Yu Herbert1,Wu Chong7,Wu Lang1ORCID

Affiliation:

1. University of Hawaii Cancer Center, University of Hawaii at Manoa Cancer Epidemiology Division, Population Sciences in the Pacific Program, , 701 Ilalo Street, Honolulu, HI 96813 , United States

2. University of Cambridge MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, , Papworth Road, Cambridge Biomedical Campus, Cambridge, CB2 0BB , United Kingdom

3. Pacific Northwest National Laboratory Biological Sciences Division, , Richland, WA 99354 , United States

4. University of Cambridge NIHR Blood and Transplant Research Unit in Donor Health and Genomics, Department of Public Health and Primary Care, , Papworth Road, Cambridge Biomedical Campus, Cambridge, CB2 0BB , United Kingdom

5. Tulane University Tulane Center for Biomedical Informatics and Genomics, Division of Biomedical Informatics and Genomics, Deming Department of Medicine, , 1440 Canal Street, New Orleans, LA 70112 , United States

6. Center for Outcomes Research, Ochsner Clinic Foundation , New Orleans, LA 70121 , United States

7. The University of Texas MD Anderson Cancer Center Department of Biostatistics, , 1400 Pressler Street, Houston, TX 77030 , United States

Abstract

Abstract Prostate cancer (PCa) brings huge public health burden in men. A growing number of conventional observational studies report associations of multiple circulating proteins with PCa risk. However, the existing findings may be subject to incoherent biases of conventional epidemiologic studies. To better characterize their associations, herein, we evaluated associations of genetically predicted concentrations of plasma proteins with PCa risk. We developed comprehensive genetic prediction models for protein levels in plasma. After testing 1308 proteins in 79 194 cases and 61 112 controls of European ancestry included in the consortia of BPC3, CAPS, CRUK, PEGASUS, and PRACTICAL, 24 proteins showed significant associations with PCa risk, including 16 previously reported proteins and eight novel proteins. Of them, 14 proteins showed negative associations and 10 showed positive associations with PCa risk. For 18 of the identified proteins, potential functional somatic changes of encoding genes were detected in PCa patients in The Cancer Genome Atlas (TCGA). Genes encoding these proteins were significantly involved in cancer-related pathways. We further identified drugs targeting the identified proteins, which may serve as candidates for drug repurposing for treating PCa. In conclusion, this study identifies novel protein biomarker candidates for PCa risk, which may provide new perspectives on the etiology of PCa and improve its therapeutic strategies.

Funder

NCI T32 Postdoctoral Fellowship

NIA

Publisher

Oxford University Press (OUP)

Subject

Genetics (clinical),Genetics,Molecular Biology,General Medicine

Reference69 articles.

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