Department of Urology, School of Medicine, Xiang'an Hospital of Xiamen University, Xiamen University, Xiamen, 361000, China.
School of Life Science and Technology, State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin, 150001, China.
Cell Mol Life Sci. 2022 Jul 21;79(8):436. doi: 10.1007/s00018-022-04456-2.
The molecular heterogeneity of prostate cancer (PCa) gives rise to distinct tumor subclasses based on epigenetic modification and gene expression signatures. Identification of clinically actionable molecular subtypes of PCa is key to improving patient outcome, and the balance between specific pathways may influence PCa outcome. It is also urgent to identify progression-related markers through cytosine-guanine (CpG) methylation in predicting metastasis for patients with PCa.
We performed bioinformatics analysis of transcriptomic, and clinical data in an integrated cohort of 551 prostate samples. The datasets included retrospective The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts. Two algorithms, Least Absolute Shrinkage and Selector Operation and Support Vector Machine-Recursive Feature Elimination, were used to select significant CpGs.
We found that PCa progression is more likely to occur after the third year through conditional survival (CS) analysis, and prostate-specific antigen (PSA) was a better predictor of Progression-free survival (PFS) than Gleason score (GS). Our study first demonstrated that PCa tumors have distinct expression profiles based on the expression of genes involved in androgen receptor (AR) and PI3K-AKT, which influence disease outcome. Our results also indicated that there are multiple phenotypes relevant to the AR-PI3K axis in PCa, where tumors with mixed phenotype may be more aggressive or have worse outcome than quiescent phenotype. In terms of epigenetics, we obtained CpG sites and their corresponding genes which have a good predictive value of PFS. However, various evidences showed that the predictive value of CpGs corresponding genes was much lower than GpG sites in Overall survival (OS) and PFS.
PCa classification specific to AR and PI3K pathways provides novel biological insight into previously established PCa subtypes and may help develop personalized therapies. Our results support the potential clinical utility of DNA methylation signatures to distinguish tumor metastasis and to predict prognosis and outcomes.
前列腺癌（PCa）的分子异质性导致基于表观遗传修饰和基因表达特征的不同肿瘤亚类。确定 PCa 的临床可操作分子亚型是改善患者预后的关键，而特定途径之间的平衡可能会影响 PCa 的结局。通过胞嘧啶-鸟嘌呤（CpG）甲基化鉴定与 PCa 转移相关的标志物对于预测患者转移也非常迫切。
我们对 551 例前列腺样本的转录组学和临床数据进行了生物信息学分析。这些数据集包括回顾性的癌症基因组图谱（TCGA）和基因表达综合数据库（GEO）队列。我们使用了两种算法，最小绝对收缩和选择算子（LASSO）和支持向量机-递归特征消除（SVM-RFE）来选择显著的 CpG。
我们通过条件生存（CS）分析发现，PCa 进展更有可能在第三年后发生，前列腺特异性抗原（PSA）比 Gleason 评分（GS）更能预测无进展生存期（PFS）。我们的研究首次表明，PCa 肿瘤根据参与雄激素受体（AR）和 PI3K-AKT 的基因表达具有不同的表达谱，这些基因影响疾病结局。我们的结果还表明，PCa 中存在与 AR-PI3K 轴相关的多种表型，其中混合表型的肿瘤可能比静止表型更具侵袭性或预后更差。在表观遗传学方面，我们获得了对 PFS 有良好预测价值的 CpG 位点及其相应基因。然而，各种证据表明，CpGs 对应基因在总体生存（OS）和 PFS 中的预测价值远低于 GpG 位点。
针对 AR 和 PI3K 途径的 PCa 分类为以前确定的 PCa 亚型提供了新的生物学见解，并可能有助于开发个性化治疗方法。我们的结果支持 DNA 甲基化特征用于区分肿瘤转移和预测预后和结局的潜在临床应用。
