Novel molecular signaling and classification of human clinically nonfunctional pituitary adenomas identified by gene expression profiling and proteomic analyses

Academic Article


  • Pituitary adenomas comprise 10% of intracranial tumors and occur in about 20% of the population. They cause significant morbidity by compression of regional structures or the inappropriate expression of pituitary hormones. Their molecular pathogenesis is unclear, and the current classification of clinically nonfunctional tumors does not reflect any molecular distinctions between the subtypes. To further elucidate the molecular changes that contribute to the development of these tumors and reclassify them according to the molecular basis, we investigated 11 nonfunctional pituitary adenomas and eight normal pituitary glands, using 33 oligonucleotide GeneChip microarrays. We validated microarray results with the reverse transcription real-time quantitative PCR, using a larger number of nonfunctional adenomas. We also used proteomic analysis to examine protein expression in these nonfunctional adenomas. Microarray analysis identified significant increases in the expression of 115 genes and decreases in 169 genes, whereas proteomic analysis identified 21 up-regulated and 29 down-regulated proteins. We observed changes in expression of SFRP1, TLE2, PITX2, NOTCH3, and DLK1, suggesting that the developmental Wnt and Notch pathways are activated and important for the progression of nonfunctional pituitary adenomas. We further analyzed gene expression profiles of all nonfunctional pituitary subtypes to each other and identified genes that were affected uniquely in each subtype. These results show distinct gene and protein expression patterns in adenomas, provide new insight into the pathogenesis and molecular classification of nonfunctional pituitary adenomas, and suggest that therapeutic targeting of the Notch pathway could be effective for these tumors. ©2005 American Association for Cancer Research.
  • Authors

    Published In

  • Cancer Research  Journal
  • Digital Object Identifier (doi)

    Pubmed Id

  • 3804105
  • Author List

  • Moreno CS; Evans CO; Zhan X; Okor M; Desiderio DM; Oyesiku NM
  • Start Page

  • 10214
  • End Page

  • 10222
  • Volume

  • 65
  • Issue

  • 22