Lingming Kong1, Peng Liu1, Mingjun Zheng2, Busheng Xue3, Keke Liang1, Xiaodong Tan1. 1. Department of General Surgery, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110004, China. 2. Department of Obstetrics & Gynecology, University Hospital, LMU Munich, Marchioninistr. 15, 81377 Munich, Germany. 3. Department of Pediatrics, Children's Cancer Research Center, Kinderklinik München Schwabing, School of Medicine, Technical University of Munich, Munich 80804, Germany.
Abstract
Aim: Integrated analysis of genomics, epigenomics, transcriptomics and clinical information contributes to identify specific molecular subgroups and find novel biomarkers for pancreatic cancer. Materials & methods: 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. Results: 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 (GRAP2, ICAM3 and A2ML1) correlated with prognosis were identified. Conclusion: 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.
Aim: Integrated analysis of genomics, epigenomics, transcriptomics and clinical information contributes to identify specific molecular subgroups and find novel biomarkers for pancreatic cancer. Materials & methods: The DNA copy number variation, the simple nucleotide variation, methylation and mRNA data of pancreatic cancerpatients were obtained from The Cancer Genome Atlas. Four molecular subgroups (iC1, iC2, iC3 and iC4) of pancreatic cancer were identified by integrating analysis. Results: 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 (GRAP2, ICAM3 and A2ML1) correlated with prognosis were identified. Conclusion: 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.
Authors: Akila J Seneviratne; Sean Peters; David Clarke; Michael Dausmann; Michael Hecker; Brett Tully; Peter G Hains; Qing Zhong Journal: Bioinformatics Date: 2021-07-29 Impact factor: 6.937