# Proteome-wide association study of prostate cancer risk across populations

## TL;DR

This study identifies proteins linked to prostate cancer risk across different populations, revealing both shared and unique associations.

## Contribution

The novel contribution is the development of population-specific genetic prediction models for protein expression and their association with prostate cancer risk.

## Key findings

- Identified 83 PCa-associated proteins in a trans-population meta-analysis.
- Found both pan-population and population-specific associations with PCa risk.
- Developed genetic prediction models for protein expression in four major populations.

## Abstract

There is insufficient understanding of the molecular basis of prostate cancer (PCa) across different populations. We perform a large-scale proteome-wide association study (PWAS) to identify proteins with genetically regulated expression in plasma to be associated with PCa risk across populations. We develop genetic prediction models for expression of 1578, 1993, 1218, and 1390 proteins for African (n = 450), European (n = 758), Asian (n = 289), and Hispanic/Latino (n = 474) males, respectively, and evaluate associations of genetically regulated protein expression with PCa risk in 19,391 PCa cases and 61,608 controls of African population, 122,188 cases and 604,640 controls of European population, 10,809 cases and 95,790 controls of Asian population, and 3931 cases and 26,405 controls of Hispanic/Latino population. We identify three, four, 15, and 73 PCa-associated proteins in African, Hispanic/Latino, Asian, and European populations, respectively, and 83 in trans-population meta-analysis. There are both pan-population and population-specific associations. Our findings provide valuable insights into etiology of PCa.

Prostate cancer incidence and mortality rates vary across males from diverse populations. Here, the authors perform a proteome-wide association study across different populations and establish population-specific genetic prediction models.