SEP 30, 2015 01:30 PM PDT

Role of microRNA-mRNA interactions in Endometrioid Endometrial Carcinoma, a "Sample to Insight" biological exploration

C.E. CREDITS: CE
Speakers
  • Principal Scientist, QIAGEN Bioinformatics
    Biography
      Jean-Noel Billaud, Ph.D. is Principal Scientist at QIAGEN Bioinformatics. He joined Ingenuity Systems (now QIAGEN) in 2008 as staff scientist for in silico research program in oncology and infectious diseases. Jean-Noel Billaud holds a Ph.D. in Blood Cell Biology from Paris VII, and has done his post-doctoral work at the Scripps Research Institute (San Diego, CA).

    Abstract:

    Endometrial adenoarcinoma is a common cause of gynecological cancer death in Europe and North America. The most dominant subtype, Endometrioid Endometrial Cancer (EEC) accounts for >80% of this cancer and is estrogen-dependent. At diagnosis, 75% of women have the disease confined to the uterus which is considered Stage One. Five-year survival for Stage One patients is 80%, however, about 15–20% develop metastasis. EEC is generally associated with good prognosis, however the clinical course may be unpredictable as four different EEC subtypes have been recently defined. One “transcriptome” subtype has been identified and presents a worse prognosis. In this webinar, we will show how in silico solutions developed by QIAGEN Bioinformatics enabled us to analyze and identify the biological parameters involved in EEC tumor progression including signaling pathways, biological processes, and potential transcriptional drivers. Total RNA extracted from tissues obtained after surgical resection from three women at Stage One EEC was subjected to RNA-sequencing. The data was uploaded directly from the Sequence Read Archive and the FASTQ files were processed with Biomedical Genomics WorkBench for secondary analysis including mapping, quantification and differential expression analysis. Through streamlined integration analyzed data was seamlessly uploaded to Ingenuity Pathway Analysis (IPA) and Ingenuity Variant Analysis (IVA) for biological interpretation, providing a sample to insight solution.


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