Last week, we offered five questions you could answer using NextBio’s lastest new application, the Body Atlas. This week we have five more:
#6. Do you know how your favorite gene’s expression compares in cancer cell line models compared to the normal cell type counterpart for a given cancer?
Example: Microseminoprotein-beta (MSMB), a known tumor suppressor in prostate cancer is over 500-fold less in each of the six prostate cancer cell-lines displayed compared to its expression in either the prostate gland as a whole or in luminal cells of the prostate.
#7. If your favorite gene is suspected to play a role in a cancer type, how does its expression compare across all popular cell line models for that cancer? And which might be the best model in which to study it based on its expression level?
Example: The PIM1 proto-oncogene shows quite variable expression in leukemia cell lines, being about 30-fold higher in the KU812 chronic myelogenous leukemia cell line, for instance, than in the CCRF-CEM T-cell acute lymphoblastic leukemia cell line. Thus, the KU812 cell line may be a better candidate for studying potential PIM1 oncogenic promoting roles and methods to decrease over-active PIM1 expression.
#8. Is there a tissue in which your gene’s expression may be unappreciated and relatively unexplored? Compare the tissue tag cloud in NextBio’s literature search to Body Atlas results and uncover a potentially new avenue of research.
Example: Doublecortin (DCX) is a gene whose expression in immature neurons is well-established but whose potential role in lymphocytes may not be so well understood yet.
#9. If you want to study your favorite pathway in a given cancer, which of the cancer cell line models for that cancer are most strongly correlated to the pathway?
Example: Which glioblastoma cell line is most positively correlated with the EGF signaling pathway? Answer: The SF-268 line. Since there can be multiple paths to achieving the cancer phenotype, glioblastoma cell lines less correlated to the EGF signaling pathway may rely on other signaling pathways to achieve it.
#10. Which normal tissue does a signature generated from your dataset of interest most closely match?
Example: A ChIP-seq-derived signature corresponding to genes which have occupied c-Myc sites in ES cells is most positively correlated to hematopoietic stem cell of bone marrow where it is known to play an essential role [Baena E et al (2007) Exp Hematol 35:1333].
Did we miss some other interesting uses of the Body Atlas? Please send us feedback@nextbio.com and share your insights with us.
Anoop Grewal
Director of Customer Support
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