Department of General Surgery, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110004, China.
Department of Obstetrics & Gynecology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany.
Epigenomics. 2020 Mar;12(6):507-524. doi: 10.2217/epi-2019-0374. Epub 2020 Feb 12.
Integrated analysis of genomics, epigenomics, transcriptomics and clinical information contributes to identify specific molecular subgroups and find novel biomarkers for pancreatic cancer. The DNA copy number variation, the simple nucleotide variation, methylation and mRNA data of pancreatic cancer patients were obtained from The Cancer Genome Atlas. Four molecular subgroups (iC1, iC2, iC3 and iC4) of pancreatic cancer were identified by integrating analysis. The iC1 subgroup harbors better prognosis, higher immune score, lesser DNA copy number variation mutations and better genomic stability compared with iC2, iC3 and iC4 subgroups. Three new genes (, and ) correlated with prognosis were identified. Integrated multi-omics analysis provides fresh insight into molecular classification of pancreatic cancer, which may help discover new prognostic biomarkers and reveal the underlying mechanism of pancreatic cancer.
基因组学、表观基因组学、转录组学和临床信息的综合分析有助于确定特定的分子亚群，并为胰腺癌找到新的生物标志物。从癌症基因组图谱中获取了胰腺癌患者的 DNA 拷贝数变异、简单核苷酸变异、甲基化和 mRNA 数据。通过整合分析，确定了胰腺癌的四个分子亚群（iC1、iC2、iC3 和 iC4）。与 iC2、iC3 和 iC4 亚群相比，iC1 亚群具有更好的预后、更高的免疫评分、较少的 DNA 拷贝数变异突变和更好的基因组稳定性。鉴定出三个与预后相关的新基因（、和）。综合多组学分析为胰腺癌的分子分类提供了新的见解，这可能有助于发现新的预后生物标志物并揭示胰腺癌的潜在机制。
