Gene expression patterns that characterize advanced stage serous ovarian cancers
JM Lancaster, HK Dressman… - Journal of the …, 2004 - journals.sagepub.com
JM Lancaster, HK Dressman, RS Whitaker, L Havrilesky, J Gray, JR Marks, JR Nevins…
Journal of the Society for Gynecologic Investigation, 2004•journals.sagepub.comObjective: To identify gene expression patterns that characterize advanced stage serous
ovarian cancers by using microarray expression analysis. Methods: Using genome-wide
expression analysis, we compared a series of 31 advanced stage (III or IV) serous ovarian
cancers from patients who survived either less than 2 years or more than 7 years with three
normal ovarian epithelial samples. Array findings were validated by analysis of expression
of the insulin-like growth factor binding protein 2 (IGFBP2) and tumor necrosis factor-related …
ovarian cancers by using microarray expression analysis. Methods: Using genome-wide
expression analysis, we compared a series of 31 advanced stage (III or IV) serous ovarian
cancers from patients who survived either less than 2 years or more than 7 years with three
normal ovarian epithelial samples. Array findings were validated by analysis of expression
of the insulin-like growth factor binding protein 2 (IGFBP2) and tumor necrosis factor-related …
Objective: To identify gene expression patterns that characterize advanced stage serous ovarian cancers by using microarray expression analysis.
Methods: Using genome-wide expression analysis, we compared a series of 31 advanced stage (III or IV) serous ovarian cancers from patients who survived either less than 2 years or more than 7 years with three normal ovarian epithelial samples. Array findings were validated by analysis of expression of the insulin-like growth factor binding protein 2 (IGFBP2) and tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) genes using quantitative real-time polymerase chain reaction (QRT-PCR).
Results: Hierarchical clustering identified patterns of gene expression that distinguished cancer from normal ovarian epithelium. We also identified gene expression patterns that distinguish cancers on the basis of patient survival. These genes include many that we associated with immune function. Expression of IGFBP2 and TRAIL genes measured by array and QRT-PCR analysis demonstrated correlation coefficients of 0.63 and 0.78, respectively.
Conclusion: Global expression analysis can identify expression patterns and individual genes that contribute to ovarian cancer development and outcome. Many of the genes that determine ovarian cancer survival are associated with the immune response, suggesting that immune function influences ovarian cancer virulence. With the generation of newer arrays with more transcripts, larger sutdies are possible to fully characterize genetic signatures fthat predict survival that may ultimately be used to guide therapeutic decision-making.
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