Gene expression signature predicting high-grade prostate cancer responses to oxaliplatin

Bioinformatic analyses revealed underlying biological functions correlated with oxaliplatin responsiveness

Colorectal cancer is one of the most common cancers worldwide. Surgery is usually the primary treatment for colon cancers that have not spread to distant sites. However, chemotherapy may be considered after surgery to eliminate remaining cancer cells or in case the cancer has a high risk of recurrence. Oxaliplatin is often used in combination regimens such as FOLFOX, CapeOX, and FOLFOXIRI because of the cost-effectiveness of adjuvant treatment for patients and also the good tolerability profile. However, some patients show resistance to oxaliplatin which causes poor treatment outcomes. Most colon cancer studies focused on treatments and patient survival. Some studies focused on genetic associations of specific genes. However, pathway and network analyses of oxaliplatin resistance in colon cancer cells using gene expression patterns are still lacking. We performed a microarray analysis and found that endothelin-1 (EDN1), dishevelled segment polarity protein (DV1), toll-like receptor 5(TLR5), mitogen-activated protein kinase 3 (MAP2K3), phosphatidylinositol-4,5-bisphosphate 3-kinase, and catalytic subunit beta (PIK3CB) were closely related to responsiveness to oxaliplatin treatment. Furthermore, we found that the signal transduction, melanogenesis, and toll-like receptor signaling pathways might be involved in oxaliplatin-resistant colon cancer. These genes and pathways might be potential targets for improving oxaliplatin treatment in colon cancer patients.

Gene expression analysis in RA: towards personalized medicine

Gene expression has recently been at the forefront of advance in personalized medicine, notably in the field of cancer and transplantation, providing a rational for a similar approach in rheumatoid arthritis (RA). RA is a prototypic inflammatory autoimmune disease with a poorly understood etiopathogenesis. Inflammation is the main feature of RA; however, many biological processes are involved at different stages of the disease. Gene expression signatures offer management tools to meet the current needs for personalization of RA patients' care. This review analyses currently available information with respect to RA diagnostic, prognostic and prediction of response to therapy with a view to highlight the abundance of data, whose comparison is often inconclusive due to the mixed use of material source, experimental methodologies and analysis tools, reinforcing the need for harmonization if gene expression signatures are to become a useful clinical tool in personalized medicine for RA patients.