An individualized prognostic signature and multi‑omics distinction for early stage hepatocellular carcinoma patients with surgical resection
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Lu Ao1,2, Xuekun Song1, Xiangyu Li2, Mengsha Tong2, You Guo2, Jing Li2, Hongdong Li2, Hao Cai2, Mengyao Li2, Qingzhou Guan2, Haidan Yan2, Zheng Guo1,2
1College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150086, China
2Key Laboratory of Ministry of Education for Gastrointestinal Cancer, Department of Bioinformatics, School of Basic Medical Sciences, Fujian Medical University, Fuzhou 350001, China
Zheng Guo, e-mail: firstname.lastname@example.org
Keywords: hepatocellular carcinoma, relative expression orderings, gene expression, prognostic signature, multi-omics
Received: December 20, 2015 Accepted: March 02, 2016 Published: March 19, 2016
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.
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