Department of Biochemistry and Medical Genetics, University of Manitoba, Canada.
Dalla Lana School of Public Health, University of Toronto, Canada.
Genomics. 2022 Sep;114(5):110474. doi: 10.1016/j.ygeno.2022.110474. Epub 2022 Aug 31.
It has become increasingly important to identify molecular markers for accurately diagnosing prostate cancer (PCa) stages between localized PCa (LPC) and locally advanced PCa (LAPC). However, there is a lack of profiling both epigenome-wide DNA methylation and transcriptome for the same patients with PCa at different stages. This study aims to identify epitranscriptomic biomarkers screened in the peri-prostatic (PP) adipose tissue for predicting LPC and LAPC.
We profiled gene expression and DNA methylation of 10 PCa patients' PP adipose tissue (4 LPC and 6 LAPC). Differential analysis was used to identify differentially methylated CpG sites and expressed genes. An integrative analysis of the microarray gene expression profiles and DNA methylation profiles was conducted using LASSO (least absolute shrinkage and selection operator) between each studied gene and the CpG sites in their promoter region. This epitranscriptomic signature was constructed by combining the association and differential analyses. The signature was then refined using the genetic mutation data of >1500 primary PCa and metastasis PCa samples from 4 different studies. We determined genes that were the most significantly affected by mutations. Machine learning models were built to evaluate the classification ability of the identified signature using the gene expression profiles from three external cohorts.
From the LASSO-based association analysis, we identified 56 genes presenting significant anti-correlation between the expression level and the methylation level of at least one CpG site in the promoter region (p-value<5 × 10). From the differential analysis, we detected 16,405 downregulated genes and 9485 genes containing at least one hypermethylated CpG site. We identified 30 genes that showed anti-correlation, down-regulation and hyper-methylation simultaneously. Using genetic mutation data, we determined that 6 of the 30 genes showed significant differences (adjusted p-value<0.05) in mutation frequencies between the primary PCa and metastasis PCa samples. The identified 30 genes performed well in distinguishing PCa patients with metastasis from PCa patient without metastasis (area under the receiver operating characteristic curve (AUC) = 0.81). The gene signature also performed well in distinguishing PCa patients with high risk of progression from PCa patients with low risk of progression (AUC = 0.88).
We established an integrative framework to identify differentially expressed genes with an aberrant methylation pattern on PP adipose tissue that may represent novel candidate molecular markers for distinguishing between LPC and LAPC.
准确诊断局限性前列腺癌（LPC）和局部进展性前列腺癌（LAPC）之间的前列腺癌（PCa）阶段，确定分子标志物变得越来越重要。然而，对于处于不同阶段的同一患者的表观基因组范围 DNA 甲基化和转录组，缺乏分析。本研究旨在鉴定预测 LPC 和 LAPC 的前列腺旁（PP）脂肪组织中筛选出的表转录组生物标志物。
我们对 10 例 PCa 患者的 PP 脂肪组织（4 例 LPC 和 6 例 LAPC）进行了基因表达和 DNA 甲基化分析。使用差异分析鉴定差异甲基化 CpG 位点和表达基因。使用 LASSO（最小绝对收缩和选择算子）对每个研究基因与其启动子区域中的 CpG 位点之间进行微阵列基因表达谱和 DNA 甲基化谱的综合分析。通过结合关联和差异分析构建表转录组签名。然后使用来自 4 项不同研究的 >1500 例原发性 PCa 和转移性 PCa 样本的遗传突变数据对该签名进行细化。我们确定了受突变影响最显著的基因。使用来自三个外部队列的基因表达谱构建机器学习模型，以评估所鉴定签名的分类能力。
基于 LASSO 的关联分析，我们确定了 56 个基因，这些基因的表达水平与启动子区域中至少一个 CpG 位点的甲基化水平之间存在显著的反相关关系（p 值<5×10）。通过差异分析，我们检测到 16405 个下调基因和 9485 个包含至少一个 hypermethylated CpG 位点的基因。我们鉴定了 30 个同时显示反相关、下调和 hyper-methylation 的基因。使用遗传突变数据，我们确定在原发性 PCa 和转移性 PCa 样本中，有 6 个基因的突变频率存在显著差异（调整后 p 值<0.05）。所鉴定的 30 个基因在区分转移性和非转移性 PCa 患者方面表现良好（受试者工作特征曲线下面积（AUC）=0.81）。该基因特征在区分高危进展性和低危进展性 PCa 患者方面也表现良好（AUC=0.88）。
我们建立了一个综合框架，以鉴定 PP 脂肪组织中具有异常甲基化模式的差异表达基因，这些基因可能代表区分 LPC 和 LAPC 的新型候选分子标志物。
