Understanding the Biological Basis of Polygenic Risk Scores and Disparities in Prostate Cancer: A Comprehensive Genomic Analysis.

IF 2.4 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Cancer Informatics Pub Date : 2024-10-21 eCollection Date: 2024-01-01 DOI:10.1177/11769351241276319

Wensheng Zhang, Kun Zhang

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引用次数: 0

Abstract

Objectives: For prostate cancer (PCa), hundreds of risk variants have been identified. It remains unknown whether the polygenic risk score (PRS) that combines the effects of these variants is also a sufficiently informative metric with relevance to the molecular mechanisms of carcinogenesis in prostate. We aimed to understand the biological basis of PRS and racial disparities in the cancer.

Methods: We performed a comprehensive analysis of the data generated (deposited in) by several genomic and/or transcriptomic projects (databases), including the GTEx, TCGA, 1000 Genomes, GEO and dbGap. PRS was constructed from 260 PCa risk variants that were identified by a recent trans-ancestry meta-analysis and contained in the GTEx dataset. The dosages of risk variants and the multi-ancestry effects on PCa incidence estimated by the meta-analysis were used in calculating individual PRS values.

Results: The following novel results were obtained from our analyses. (1) In normal prostate samples from healthy European Americans (EAs), the expression levels of 540 genes (termed PRS genes) were associated with the PRS (P < .01). (2) Ubiquitin-proteasome system in high-PRS individuals' prostates was more active than that in low-PRS individuals' prostates. (3) Nine PRS genes play roles in the cancer progression-relevant parts, which are frequently hit by somatic mutations in PCa, of PI3K-Akt/RAS-MAPK/mTOR signaling pathways. (4) The expression profiles of the top significant PRS genes in tumor samples were capable of predicting malignant PCa relapse after prostatectomy. (5) The transcriptomic differences between African American and EA samples were incompatible with the patterns of the aforementioned associations between PRS and gene expression levels.

Conclusions: This study provided unique insights into the relationship between PRS and the molecular mechanisms of carcinogenesis in prostate. The new findings, alongside the moderate but significant heritability of PCa susceptibility contributed by the risk variants, suggest the aptness and inaptness of PRS for explaining PCa and disparities.

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了解前列腺癌多基因风险评分和差异的生物学基础：综合基因组分析

目的：前列腺癌（PCa）的风险变异体已发现数百种。综合这些变异影响的多基因风险评分（PRS）是否也是一种与前列腺癌分子致癌机制相关的、具有足够信息量的指标，目前仍不得而知。我们旨在了解 PRS 的生物学基础以及癌症的种族差异：我们对 GTEx、TCGA、1000 Genomes、GEO 和 dbGap 等多个基因组和/或转录组项目（数据库）生成（存入）的数据进行了综合分析。PRS 是根据 GTEx 数据集中的 260 个 PCa 风险变异构建的，这些风险变异是由最近的一项跨祖先荟萃分析确定的。荟萃分析估计的风险变异剂量和对 PCa 发病率的多基因影响被用于计算单个 PRS 值：我们的分析得出了以下新结果。(1）在健康的欧洲裔美国人（EAs）的正常前列腺样本中，540 个基因（称为 PRS 基因）的表达水平与 PRS 值（P 结论）相关：这项研究为前列腺癌 PRS 与致癌分子机制之间的关系提供了独特的见解。这些新发现以及风险变异对 PCa 易感性的适度但显著的遗传性，表明了 PRS 在解释 PCa 和差异方面的适用性和不适用性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Cancer Informatics Medicine-Oncology

CiteScore

3.00

自引率

5.00%

发文量

审稿时长

8 weeks

期刊介绍： The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.