Psoriasis is an immune-mediated, inflammatory skin disease that affects approximately 3% of the world’s population. Although the exact cause isn’t known, a number of treatments are available for sufferers. Narrow band UVB treatment is a common treatment for psoriasis, but not every treatment works for every patient. This makes biomarkers for treatment efficacy an important for finding the right solution for patients.
In this webinar, we will discuss using MetaCore to hypothesize biomarkers for psoriasis patients being treated with narrow-band UVB. We will be comparing experimental data in MetaCore and exporting subsets of these results. For this training session, we will analyze transcriptomic data from psoriasis patients taken from the lesion samples available in the GEO Series entry GSE53431 as the input datasets, to answer these questions:
• How is gene expression for excellent responders changing over the course of the treatment?
• Is the trend observed in the excellent responders reflected in the good responders? What about the poor responders?
• What processes are related to these potential biomarkers?
Understanding the mechanisms and biomarkers for T-lymphocyte infiltration into tumors is important for immuno-oncology. Gene expression can provide a glimpse into the current state of the studied T-lymphocytes. To fully understand what is driving the infiltration, analyses needs to uncover biomarkers and the mechanisms driving them.
In this webinar, we will discuss incorporating MetaCore analysis with the Key Pathway Advisor tool. We will be comparing experimental data in MetaCore before going to the Key Pathway Advisor to hypothesize key hubs via the causal reasoning algorithm. For this training session, we will analyze transcriptomic data from breast cancer patients taken from the tumor and peripheral blood samples available in the GEO Series entry GSE36765 as the input datasets, to answer these questions:
• What common genes and pathways are there between the high and low infiltrating T-lymphocytes?
• What direct and indirect regulators could be responsible for the unique gene signature observed in highly infiltrating T-lymphocytes? How does this differ from the common gene signature or the unique signature of low infiltrating T-lymphocytes?
• Do any immune checkpoints come up as significant regulation hubs?
In this webinar, we will discuss MetaCore’s new Key Pathway Advisor tool and how it can aid in discovering key hubs and pathways. The Key Pathway Advisor uses the causal reasoning and overconnectivity algorithm to relate differentially expressed genes to their upstream regulators. Here we will analyze transcriptomic data from multiple sclerosis patients treated with IFN beta-1a taken from GEO Series entry GSE26104 as the input datasets, to answer these questions:
• What key pathways have a significant union of differentially expressed genes and key hubs?
• What direct and indirect regulators could be responsible for the gene signature observed due to IFN beta-1a treatment?
• How does the IFN beta-1a treatment signature compare with previously reported literature knowledge?
Using various cell lines is a common practice for testing the efficacy of drugs before moving on to animal models. For a particular disease or cancer, there can be a large number cell lines to test against, each with their own characteristics and variants. Testing against multiple different cell lines is an effective way to tease out particular traits leading to drug treatment sensitivities.
In this webinar, we will use knowledge mining, enrichment analysis, and network building tools in MetaCore to analyze transcriptomic data from multiple ovarian cancer cell lines treated with Eribulin taken from GEO Series entry GSE50831 as the input datasets, to answer these questions:
- What can I learn about genes being overly expressed in ovarian cancer?
- What are the commonalities and differences in expression between these cell lines being treated with Eribulin?
- What signaling pathways are being significantly affected by the drug treatment across multiple cell lines?
Recently, the role of stromal tissue has been revealed as significant in the cancer progression. Much of the influence from the stroma for tumor growth has been shown via secretion of soluble factors. Finding the way in which stromal and epithelial tissues interact and understanding the importance of supporting tissues in tumor development can lead to new therapeutic targets.
In this webinar, we will use enrichment analysis, interactome and network building tools in MetaCore to analyse transcriptomic data from stroma and epithelia of invasive breast cancer taken from PMID: 18373191 with GEO Series entry GSE10797 as the input datasets, to answer these questions:
- What are the commonalities and differences in expression between the epithelial and stromal tissues in aggressive breast cancer, and how does this translate into pathway perturbation?
- How significantly do the two tissues interact and what if any is the direction of that interactivity?
- What are the key molecules responsible for this interaction and what can we understand about the mechanisms that drive this relationship; how does stromal tissue help promote tumor development?