Ao Lu, Song Xuekun, Li Xiangyu, Tong Mengsha, Guo You, Li Jing, Li Hongdong, Cai Hao, Li Mengyao, Guan Qingzhou, Yan Haidan, Guo Zheng
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China.
Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350001, China.
Oncotarget. 2016 Apr 26;7(17):24097-110. doi: 10.18632/oncotarget.8212.
Previously reported prognostic signatures for predicting the prognoses of postsurgical hepatocellular carcinoma (HCC) patients are commonly based on predefined risk scores, which are hardly applicable to samples measured by different laboratories. To solve this problem, using gene expression profiles of 170 stage I/II HCC samples, we identified a prognostic signature consisting of 20 gene pairs whose within-sample relative expression orderings (REOs) could robustly predict the disease-free survival and overall survival of HCC patients. This REOs-based prognostic signature was validated in two independent datasets. Functional enrichment analysis showed that the patients with high-risk of recurrence were characterized by the activations of pathways related to cell proliferation and tumor microenvironment, whereas the low-risk patients were characterized by the activations of various metabolism pathways. We further investigated the distinct epigenomic and genomic characteristics of the two prognostic groups using The Cancer Genome Atlas samples with multi-omics data. Epigenetic analysis showed that the transcriptional differences between the two prognostic groups were significantly concordant with DNA methylation alternations. The signaling network analysis identified several key genes (e.g. TP53, MYC) with epigenomic or genomic alternations driving poor prognoses of HCC patients. These results help us understand the multi-omics mechanisms determining the outcomes of HCC patients.
先前报道的用于预测肝细胞癌(HCC)术后患者预后的预后特征通常基于预定义的风险评分,这些评分很难应用于不同实验室检测的样本。为了解决这个问题,我们使用170例I/II期HCC样本的基因表达谱,鉴定出一个由20个基因对组成的预后特征,其样本内相对表达顺序(REO)能够可靠地预测HCC患者的无病生存期和总生存期。这个基于REO的预后特征在两个独立的数据集中得到了验证。功能富集分析表明,复发高风险患者的特征是与细胞增殖和肿瘤微环境相关的通路激活,而低风险患者的特征是各种代谢通路的激活。我们使用具有多组学数据的癌症基因组图谱样本,进一步研究了两个预后组不同的表观基因组和基因组特征。表观遗传分析表明,两个预后组之间的转录差异与DNA甲基化变化显著一致。信号网络分析确定了几个关键基因(如TP53、MYC),其表观基因组或基因组变化导致HCC患者预后不良。这些结果有助于我们理解决定HCC患者预后的多组学机制。