Integrative Approach to Biomarker Discovery: Comparative Analysis of Two Cancers Using Genomics and Transcriptomics from RNA Sequencing Data
Jean-Noel Billaud, PhD
Wednesday, December 7, 2016
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Chapters
Introduction
Outline of the presentation
Overall conclusions from comparative analysis of ‘omics of EEC or HCC patients
Background on Endometrioid Endometrial Carcinoma (EEC)
Background on Hepatocellular Carcinoma (HCC)
QIAGEN Sample to Insight
Description of the studies
Promise of combining omics data together for cancer research…
Simple to use RNA-seq solution (FASTQ to Insight)
Simple to use RNA-seq solution
BxWB to IPA: data analysis to interpretation, FASTQ to Tracks
BxWB to IPA: data analysis to interpretation, Tracks to STATS
EEC and HCC are clearly separated by transcript expression levels
Example of processed data: Differential expression of transcripts
IPA analysis: Comparison of Diseases and Functions of EEC vs HCC (pool)
Looking with IsoProfiler for key differentially expressed isoforms
760 Isoforms protein-coding with at least two transcript differentially expressed have been filtered across the 2 cancers
Only 3 isoforms from HCC have known specific findings related to invasion of tumor cell lines
CEACAM1-001 and CEACAM1-002 are differentially expressed in HCC
CEACAM1 isoform view
CEACAM1-001 and CEACAM1-002: potential biomarkers for HCC
CEACAM1 has not been described as biomarker in any liver cancer
Comparison of Canonical Pathways for 6 patients (using all DEGs)
Pathway Activity Analysis of Actin Cytoskeleton Signaling
Looking with IsoProfiler for key differentially expressed isoforms
Finding ITGB1 isoforms as potential biomarker
Upstream Analysis: looking for common Master Regulators
EEC P47, CTGF-driven Causal Network predicted to be activated
CTGF (CCN2): Connective Tissue Growth Factor
FG-3019 is a potential new therapeutic for EEC patients?
Conclusion: Comparative analysis of omics of patients with EEC or HCC
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