• Digital-Health-Conference-2020

Invited SpeakersProfile Details

Prof. Zhang Zhang
Prof. Zhang Zhang Dr. Zhang is a distinguished Professor of Beijing Institute of Genomics (BIG), Chinese Academy of Sciences (CAS) and Executive Director of the National Genomics Data Center in BIG, CAS.

Biography

Dr. Zhang is a distinguished Professor of Beijing Institute of Genomics (BIG), Chinese Academy of Sciences (CAS) and Executive Director of the National Genomics Data Center in BIG, CAS. He obtained PhD degree in Computer Science from Institute of Computing Technology, CAS in 2007. Prior to joining BIG, he worked as Postdoctoral Associate at Yale University from 2007~2009 and Research Scientist at King Abdullah University of Science and Technology from 2009~2011. Dr. Zhang was elected in the CAS 100-Talents Program in 2011 and evaluated as Excellent Award in the final review of the CAS 100-Talents Program in 2017. His research focuses on big data integration and computational health genomics. He acts as Asian Associate Editor for Briefings in Bioinformatics and Associate Editor-in-Chief for Genomics Proteomics & Bioinformatics.

All sessions by Prof. Zhang Zhang

  • Day 2Tuesday, January 21st
Session 4 : Digital Health and Biotechnology (Chair Stefan Arold)
2:30 pm

Computational genomics of brain tumors: glioma biomarker identification and characterization through multi-omics integrative molecular profiling

Glioma, one of the most lethal human malignancies, represents almost 80% of malignant brain tumors and exhibits low resection rate and high recurrence risk. With the rapid advancement of sequencing technologies, there is an increasing number of high-throughput studies on glioma, resulting in massive multi-omics multi-cohort data generated from different projects and different laboratories throughout the world.

Therefore, it has become critically important on how to make full use of these valuable data for comprehensive integrative characterization of glioma biomarkers. In this study, we collected a large-scale assemble of multi-omics multi-cohort datasets from public resources, involving a total of 17,022 samples across 19 independent studies. We established a methodological strategy on integrative identification of biomarkers with higher specificity and feasible detectability from periphery and identified that PRKCG (Protein Kinase C Gamma) features higher specificity in brain and detectability in CSF (Cerebrospinal Fluid).
Through comprehensive molecular characterization of PRKCG based on multi-omics analyses in RNA expression, DNA methylation and copy number variation, we revealed that PRKCG has the significant potential in glioma diagnosis, prognosis and treatment prediction as testified on multiple independent discovery and validation datasets.

Unlike existing biomarkers that were mostly discovered at single omics level and with limited samples, we found that multi-omics molecular profiles of PRKCG are highly associated with glioma across different populations, bearing great potential for glioma diagnosis, prognosis and therapy.

Building 19, Hall 1 14:30 - 14:55 Details