BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW Sydney, Sydney, NSW 2052, Australia.
Data Analytic Lab, Department of Computing, Macquarie University, Sydney, NSW 2109, Australia.
Int J Mol Sci. 2023 Jan 27;24(3):2472. doi: 10.3390/ijms24032472.
Prostate cancer (PC) is the most frequently diagnosed non-skin cancer in the world. Previous studies have shown that genomic alterations represent the most common mechanism for molecular alterations responsible for the development and progression of PC. This highlights the importance of identifying functional genomic variants for early detection in high-risk PC individuals. Great efforts have been made to identify common protein-coding genetic variations; however, the impact of non-coding variations, including regulatory genetic variants, is not well understood. Identification of these variants and the underlying target genes will be a key step in improving the detection and treatment of PC. To gain an understanding of the functional impact of genetic variants, and in particular, regulatory variants in PC, we developed an integrative pipeline (AGV) that uses whole genome/exome sequences, GWAS SNPs, chromosome conformation capture data, and ChIP-Seq signals to investigate the potential impact of genomic variants on the underlying target genes in PC. We identified 646 putative regulatory variants, of which 30 significantly altered the expression of at least one protein-coding gene. Our analysis of chromatin interactions data (Hi-C) revealed that the 30 putative regulatory variants could affect 131 coding and non-coding genes. Interestingly, our study identified the 131 protein-coding genes that are involved in disease-related pathways, including Reactome and MSigDB, for most of which targeted treatment options are currently available. Notably, our analysis revealed several non-coding RNAs, including and , as potential enhancer elements of the protein-coding genes and , respectively. Our results provide a comprehensive map of genomic variants in PC and reveal their potential contribution to prostate cancer progression and development.
前列腺癌（PC）是世界上最常见的非皮肤癌。先前的研究表明，基因组改变代表了导致 PC 发生和发展的分子改变的最常见机制。这突出表明了鉴定功能性基因组变异以用于高危 PC 个体的早期检测的重要性。已经做出了巨大的努力来鉴定常见的蛋白质编码遗传变异；然而，非编码变异（包括调节遗传变异）的影响尚不清楚。鉴定这些变体及其潜在的靶基因将是提高 PC 检测和治疗水平的关键步骤。为了了解遗传变异，特别是 PC 中的调节变异的功能影响，我们开发了一种综合分析流程（AGV），该流程使用全基因组/外显子序列、GWAS SNPs、染色体构象捕获数据和 ChIP-Seq 信号来研究基因组变异对 PC 中潜在靶基因的潜在影响。我们鉴定了 646 个推定的调节变异，其中 30 个显著改变了至少一个蛋白质编码基因的表达。我们对染色质相互作用数据（Hi-C）的分析表明，这 30 个假定的调节变体可能影响 131 个编码和非编码基因。有趣的是，我们的研究鉴定了 131 个参与疾病相关途径的蛋白质编码基因，其中大多数都有针对这些基因的靶向治疗方案。值得注意的是，我们的分析揭示了几个非编码 RNA，包括 和 ，分别作为蛋白质编码基因 和 的潜在增强子元件。我们的研究结果提供了 PC 中基因组变异的全面图谱，并揭示了它们对前列腺癌进展和发展的潜在贡献。
