Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan, Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA, Institute of Basic Medical Sciences, National Cheng Kung University, Tainan 701, Taiwan, Department of Pediatrics and Rady Children's Hospital, University of California San Diego, La Jolla, CA 92093, USA, Department of General, Visceral and Cancer Surgery, University of Cologne, Köln, Germany, Institute for Genomic Medicine, University of California San Diego, La Jolla, CA 92093, USA, Department of Pharmacology and Institute of Medical Informatics, National Cheng Kung University, Tainan 701, Taiwan Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan, Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA, Institute of Basic Medical Sciences, National Cheng Kung University, Tainan 701, Taiwan, Department of Pediatrics and Rady Children's Hospital, University of California San Diego, La Jolla, CA 92093, USA, Department of General, Visceral and Cancer Surgery, University of Cologne, Köln, Germany, Institute for Genomic Medicine, University of California San Diego, La Jolla, CA 92093, USA, Department of Pharmacology and Institute of Medical Informatics, National Cheng Kung University, Tainan 701, Taiwan.
Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan, Moores Cancer Center, University of California San Diego, La Jolla, CA 92093, USA, Institute of Basic Medical Sciences, National Cheng Kung University, Tainan 701, Taiwan, Department of Pediatrics and Rady Children's Hospital, University of California San Diego, La Jolla, CA 92093, USA, Department of General, Visceral and Cancer Surgery, University of Cologne, Köln, Germany, Institute for Genomic Medicine, University of California San Diego, La Jolla, CA 92093, USA, Department of Pharmacology and Institute of Medical Informatics, National Cheng Kung University, Tainan 701, Taiwan.
Bioinformatics. 2014 Nov 1;30(21):3054-61. doi: 10.1093/bioinformatics/btu433. Epub 2014 Jul 10.
A rapid progression of esophageal squamous cell carcinoma (ESCC) causes a high mortality rate because of the propensity for metastasis driven by genetic and epigenetic alterations. The identification of prognostic biomarkers would help prevent or control metastatic progression. Expression analyses have been used to find such markers, but do not always validate in separate cohorts. Epigenetic marks, such as DNA methylation, are a potential source of more reliable and stable biomarkers. Importantly, the integration of both expression and epigenetic alterations is more likely to identify relevant biomarkers.
We present a new analysis framework, using ESCC progression-associated gene regulatory network (GRN escc), to identify differentially methylated CpG sites prognostic of ESCC progression. From the CpG loci differentially methylated in 50 tumor-normal pairs, we selected 44 CpG loci most highly associated with survival and located in the promoters of genes more likely to belong to GRN escc. Using an independent ESCC cohort, we confirmed that 8/10 of CpG loci in the promoter of GRN escc genes significantly correlated with patient survival. In contrast, 0/10 CpG loci in the promoter genes outside the GRN escc were correlated with patient survival. We further characterized the GRN escc network topology and observed that the genes with methylated CpG loci associated with survival deviated from the center of mass and were less likely to be hubs in the GRN escc. We postulate that our analysis framework improves the identification of bona fide prognostic biomarkers from DNA methylation studies, especially with partial genome coverage.
食管鳞状细胞癌（ESCC）的快速进展导致高死亡率，这是由于遗传和表观遗传改变驱动的转移倾向。预后生物标志物的鉴定将有助于预防或控制转移进展。表达分析已被用于寻找此类标志物，但在单独的队列中并不总是有效。表观遗传标记，如 DNA 甲基化，是更可靠和稳定的生物标志物的潜在来源。重要的是，表达和表观遗传改变的综合更有可能识别相关的生物标志物。
我们提出了一种新的分析框架，使用 ESCC 进展相关基因调控网络（GRN escc），来识别与 ESCC 进展相关的差异甲基化 CpG 位点。从 50 对肿瘤-正常样本中差异甲基化的 CpG 位点中，我们选择了 44 个与生存最相关且位于 GRN escc 基因启动子中的 CpG 位点。使用独立的 ESCC 队列，我们证实了 GRN escc 基因启动子中 10 个 CpG 位点中的 8 个与患者生存显著相关。相比之下，GRN escc 之外的启动子基因中的 10 个 CpG 位点与患者生存无关。我们进一步表征了 GRN escc 网络拓扑结构，观察到与生存相关的甲基化 CpG 位点的基因偏离质心，并且在 GRN escc 中不太可能成为枢纽。我们假设我们的分析框架可以从 DNA 甲基化研究中更有效地识别真正的预后生物标志物，尤其是在基因组覆盖部分的情况下。
