Immuno-oncology research has been aimed towards modulating the immunosuppressive properties of cancers. A particularly hot topic has been on the programmed death receptor (PD-1) and ligands (PD-L1 or PD-L2). These proteins lead to the inhibition of T-cell function, promotes apoptosis of cytotoxic T cells and induction of immuno-suppressive T-regulatory cells. Current therapeutics in development are focusing on direct inhibition of PD-1 or PD-L1 or PD-L2, but could other genes that regulate these proteins be targets for immune-oncology as well? In this webinar, we will use knowledge mining and network building in MetaCore to better understand what factors can lead to increased expression of the programmed death receptor (PD-1). Questions that will be covered in this session include: • What information can I find about PD-1? What proteins are regulating or influencing the expression of PD-1? • From these proteins, what signaling pathways could lead to modulating the expression of PD-1? Might some of the pathways make good biomarkers for stratifying patients as well as potential therapeutic targets?
Using multiple types of experimental data to corroborate hypotheses of disease mechanisms is a powerful technique. When performing multi-omic analysis, it is important to take careful consideration of the methods used. In this webinar, we will use enrichment analysis and network building in MetaCore to analysis metabolomic and transcriptomic data from patients with Barrett’s Esophagus and Esophageal Adenocarcinoma from PMID 23241138 (metabolomics data) and GSE12657 (expression data) as the input datasets to answer these questions: - What are the differences between the metabolites from Barrett’s Esophagus and Esophageal Adenocarcinoma? Can overlaying transcriptomic data provide additional information on these differences? - How are differentially expressed genes common or unique to these two diseases related to the metabolomics data?
Comparing gene expression profiles from differing forms of brain cancer is important in identifying commonly and uniquely expressed genes that can lead to new biomarkers for disease stratification or treatment targets. In this webinar, we will use enrichment analysis and network building in MetaCore to analysis gene expression data from histological samples for glioblastoma, astrocytoma, and oligodendrioglioma using GSE12657 (expression data from of human glioma samples) as the input data to answer these questions: • What differentially expressed genes are common or unique between these types of brain tumors? What signaling pathways will be impacted by these changes? • Are there any gene signatures unique to glioblastoma that might be interesting for further exploration? Could some of these unique genes make interesting biomarkers or drug targets? Click on image to access webinar
Using multi-omics analysis to uncover the relationships between different biological macromolecules can garner new insights into the underlying mechanisms that drive disease. In this webinar, we will discuss analyzing micro RNA (miRNA) and messenger RNA (mRNA) expression data together to understand their relationship in multiple sclerosis. Using GSE43592 (MicroRNA regulated immune pathways in T-cells in multiple sclerosis (MS)) as the input data in MetaCore, we will demonstrate and identify: • What diseases have been associated with MIR-494? How is MIR-494 related to the differentially expressed genes? • What is the relationship between the differentially expressed miRNA and mRNA in multiple sclerosis patients? • From this interaction network, what pathways and processes are being disrupted? What hypotheses can be drawn from the relationship between miRNA and mRNA expression in multiple sclerosis?
This webinar will walk through MetaCore’s new Key Pathway Advisor tool that uses causal reasoning and overconnectivity to pinpoint key hubs upstream of the DEGs. We will use GDS2794 (T-cell acute lymphoblastic leukemia cell line response to Notch receptor inhibition) as the input data to demonstrate and identify: • What pathways show synergistic behavior with the list of DEGs and key hubs as identified through the causal reasoning algorithm? • What topologically significant direct and indirect upstream regulators could be causing the observed changes for the downstream DEGs? How many steps away are the hubs and what is their predicted change in activity? • How does the NOTCH1 transcriptional regulation profile compare to previous literature knowledge? Is NOTCH1 activity predicated to be activated or inhibited during this cell line treatment protocol?