Larson Nicholas B, McDonnell Shannon K, Fogarty Zachary, Liu Yuanhang, French Amy J, Tillmans Lori S, Cheville John C, Wang Liang, Schaid Daniel J, Thibodeau Stephen N
Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States.
Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, United States.
Front Genet. 2022 Mar 30;13:836841. doi: 10.3389/fgene.2022.836841. eCollection 2022.
Large genome-wide association studies have identified hundreds of single-nucleotide polymorphisms associated with increased risk of prostate cancer (PrCa), and many of these risk loci is presumed to confer regulatory effects on gene expression. While eQTL studies of long RNAs has yielded many potential risk genes, the relationship between PrCa risk genetics and microRNA expression dysregulation is understudied. We performed an microRNA transcriptome-wide association study of PrCa risk using small RNA sequencing and genome-wide genotyping data from N = 441 normal prostate epithelium tissue samples along with N = 411 prostate adenocarcinoma tumor samples from the Cancer Genome Atlas (TCGA). Genetically regulated expression prediction models were trained for all expressed microRNAs using the FUSION TWAS software. TWAS for PrCa risk was performed with both sets of models using single-SNP summary statistics from the recent PRACTICAL consortium PrCa case-control OncoArray GWAS meta-analysis. A total of 613 and 571 distinct expressed microRNAs were identified in the normal and tumor tissue datasets, respectively (overlap: 480). Among these, 79 (13%) normal tissue microRNAs demonstrated significant cis-heritability (median cis-h2 = 0.15, range: 0.03-0.79) for model training. Similar results were obtained from TCGA tumor samples, with 48 (9%) microRNA expression models successfully trained (median cis-h2 = 0.14, range: 0.06-0.60). Using normal tissue models, we identified two significant TWAS microRNA associations with PrCa risk: over-expression of mir-941 family microRNAs (P = 2.9E-04) and reduced expression of miR-3617-5p (P = 1.0E-03). The TCGA tumor TWAS also identified a significant association with miR-941 overexpression (P = 9.7E-04). Subsequent finemapping of the TWAS results using a multi-tissue database indicated limited evidence of causal status for each microRNA with PrCa risk (posterior inclusion probabilities <0.05). Future work will examine downstream regulatory effects of microRNA dysregulation as well as microRNA-mediated risk mechanisms via competing endogenous RNA relationships.
大规模全基因组关联研究已经鉴定出数百个与前列腺癌(PrCa)风险增加相关的单核苷酸多态性,并且推测其中许多风险位点对基因表达具有调控作用。虽然对长链RNA的eQTL研究已经产生了许多潜在的风险基因,但PrCa风险遗传学与微小RNA表达失调之间的关系仍未得到充分研究。我们使用来自癌症基因组图谱(TCGA)的N = 441个正常前列腺上皮组织样本以及N = 411个前列腺腺癌肿瘤样本的小RNA测序和全基因组基因分型数据,对PrCa风险进行了全转录组范围的微小RNA关联研究。使用FUSION TWAS软件为所有表达的微小RNA训练了基因调控表达预测模型。使用来自最近的PRACTICAL联盟PrCa病例对照OncoArray GWAS荟萃分析的单核苷酸多态性汇总统计数据,对两组模型进行了PrCa风险的TWAS分析。在正常和肿瘤组织数据集中分别鉴定出总共613个和571个不同表达的微小RNA(重叠部分:480个)。其中,79个(13%)正常组织微小RNA在模型训练中表现出显著的顺式遗传力(中位顺式h2 = 0.15,范围:0.03 - 0.79)。从TCGA肿瘤样本中也获得了类似的结果,48个(9%)微小RNA表达模型成功训练(中位顺式h2 = 0.14,范围:0.06 - 0.60)。使用正常组织模型,我们鉴定出两个与PrCa风险显著相关的TWAS微小RNA:mir - 941家族微小RNA的过表达(P = 2.9E - 04)和miR - 3617 - 5p的表达降低(P = 1.0E - 03)。TCGA肿瘤TWAS也鉴定出与miR - 941过表达显著相关(P = 9.7E - 04)。随后使用多组织数据库对TWAS结果进行精细定位,表明每个微小RNA与PrCa风险的因果关系证据有限(后验包含概率<0.05)。未来的工作将研究微小RNA失调的下游调控作用以及通过竞争性内源RNA关系的微小RNA介导的风险机制。