Genome and Gene Expression Data Analysis Division, Bioinformatics Institute, A-STAR, 30 Biopolis Street, #07-01 Matrix, Singapore, 138671, Singapore.
School of Computer Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore.
BMC Genomics. 2017 Oct 3;18(Suppl 6):692. doi: 10.1186/s12864-017-4027-5.
High-grade serous ovarian carcinoma (HG-SOC) is the dominant tumor histologic type in epithelial ovarian cancers, exhibiting highly aberrant microRNA expression profiles and diverse pathways that collectively determine the disease aggressiveness and clinical outcomes. However, the functional relationships between microRNAs, the common pathways controlled by the microRNAs and their prognostic and therapeutic significance remain poorly understood.
We investigated the gene expression patterns of microRNAs in the tumors of 582 HG-SOC patients to identify prognosis signatures and pathways controlled by tumor miRNAs. We developed a variable selection and prognostic method, which performs a robust selection of small-sized subsets of the predictive features (e.g., expressed microRNAs) that collectively serves as the biomarkers of cancer risk and progression stratification system, interconnecting these features with common cancer-related pathways.
Across different cohorts, our meta-analysis revealed two robust and unbiased miRNA-based prognostic classifiers. Each classifier reproducibly discriminates HG-SOC patients into high-confidence low-, intermediate- or high-prognostic risk subgroups with essentially different 5-year overall survival rates of 51.6-85%, 20-38.1%, and 0-10%, respectively. Significant correlations of the risk subgroup's stratification with chemotherapy treatment response were observed. We predicted specific target genes involved in nine cancer-related and two oocyte maturation pathways (neurotrophin and progesterone-mediated oocyte maturation), where each gene can be controlled by more than one miRNA species of the distinct miRNA HG-SOC prognostic classifiers.
We identified robust and reproducible miRNA-based prognostic subsets of the of HG-SOC classifiers. The miRNAs of these classifiers could control nine oncogenic and two developmental pathways, highlighting common underlying pathologic mechanisms and perspective targets for the further development of a personalized prognosis assay(s) and the development of miRNA-interconnected pathway-centric and multi-agent therapeutic intervention.
高级别浆液性卵巢癌（HG-SOC）是上皮性卵巢癌的主要肿瘤组织学类型，表现出高度异常的 microRNA 表达谱和多种途径，这些途径共同决定了疾病的侵袭性和临床结局。然而，microRNAs 之间的功能关系、microRNAs 控制的常见途径及其预后和治疗意义仍知之甚少。
我们调查了 582 名 HG-SOC 患者肿瘤中 microRNA 的基因表达模式，以确定肿瘤 microRNA 控制的预后特征和途径。我们开发了一种变量选择和预后方法，该方法对预测特征（如表达的 microRNA）的小尺寸子集进行稳健选择，这些子集共同作为癌症风险和进展分层系统的生物标志物，将这些特征与常见的癌症相关途径联系起来。
在不同的队列中，我们的荟萃分析揭示了两个稳健且无偏倚的基于 microRNA 的预后分类器。每个分类器可重复地将 HG-SOC 患者分为高置信度的低、中或高预后风险亚组，其 5 年总生存率分别为 51.6-85%、20-38.1%和 0-10%。观察到风险亚组分层与化疗治疗反应之间存在显著相关性。我们预测了涉及九个癌症相关和两个卵母细胞成熟途径（神经营养因子和孕激素介导的卵母细胞成熟）的特定靶基因，其中每个基因都可以由不同的 miRNA 物种控制，这些 miRNA 物种是 HG-SOC 预后分类器的一部分。
我们确定了 HG-SOC 分类器中稳健且可重复的基于 microRNA 的预后子集。这些分类器中的 microRNAs 可以控制九个致癌和两个发育途径，突出了常见的潜在病理机制和针对进一步开发个性化预后检测以及 miRNA 互联途径为中心和多药物治疗干预的潜在靶点。
