Establishment of differential expression profiles from invasive and non-invasive pituitary adenomas.

Journal: Zhong Nan Da Xue Xue Bao. Yi Xue Ban = Journal Of Central South University. Medical Sciences
Published:
Abstract

Objective: To establish high resolution, reproducible 2-dimensional electrophoresis (2-DE) profiles of invasive and non-invasive pituitary adenoma tissues and to identify differentially expressed proteins between the invasive and non-invasive tissues.

Methods: The proteome from invasive and non-invasive pituitary adenomas tissues was dissected and analyzed by: (1) immobilized pH gradient two-dimensional polyacrylamide gel electrophoresis, (2) silver staining, (3) imageMaster 2-D software analysis, (4) peptide mass fingerprint based (PMS) on matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF-MS), and (5) database comparison.

Results: High-resolution 2-D patterns of invasive and non-invasive pituitary adenoma tissues were successfully produced and repeated 3 times for each sample. An average of 1 080+/-24 and 1 035+/-28 spots were detected for invasive and non-invasive pituitary adenoma tissues, respectively. Additionally, 975+/-45 and 918+/-56 spots were found to have an average matching rate of 90.3% and 88.7% for invasive and non-invasive tissues, respectively. The spot positional deviation was (1.563+/-0.259) mm for IEF and (1.088+/-0.206) mm for SDS-PAGE. A total of 99 spots of differential expression were matched between the invasive and non-invasive pituitary adenoma tissues. Thirty differential proteins, some of which were involved in the regulation of cells cycle and signal transduction, were initially characterized by PMS.

Conclusions: The acquisition of well-resolved and reproducible 2-D patterns of invasive and non-invasive pituitary adenoma tissues and the identification of differentially expressed proteins provides a proteome database for invasive pituitary adenomas.

Authors
Zhixiong Liu, Yunsheng Liu, Wenhua Fang, Wei Chen, Cui Li, Zhiqiang Xiao