Hirsch and colleagues generated expression profiles of cell transformation using two isogenic cell models — MCF10A cells expressing tamoxifen-inducible SRC (MCF10A–ER–SRC cells) and three isogenic fibroblast cell lines representing different stages of HRAS-G12V transformation. They combined the SRC and HRAS transformation profiles to produce the 'cancer gene signature' (CGS), comprising 343 differentially expressed genes, which was validated by literature mining and comparison to published expression profiles associated with cancer. They found that the genes in the CGS that associated with the widest range of cancer types were predominantly those involved in inflammation. Next, using ingenuity pathway analysis, they identified three groups of biofunctions and diseases that correlated with the CGS: cancer-related, inflammation and immunity, and, unexpectedly, lipid metabolism. They also found that the CGS overlapped with published expression profiles from individuals with obesity, atherosclerosis and metabolic syndrome.
They then organized the two transformation expression profiles into a network, which they compared with expression profiles from individuals with metabolic syndrome to identify 24 common nodes, including insulin, low density lipoprotein (LDL) and proteins involved in inflammation such as interferon-γ (IFNγ), interleukin-6 (IL-6) and nuclear factor-κB (NF-κΒ). Suppressing the activity of each of the 24 common nodes in the transformed MCF10A–ER–SRC cells reduced transformation, as determined by morphology or focus formation in soft agar. These data suggest that these groups of diseases exhibit overlapping alterations in certain pathways, which are defined by the nodes in the transformation-associated network.
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