UCHIDA Kazuhiko University of Tsukuba, Graduate School of Comprehensive Human Sciences, Associate professor, 大学院・人間総合科学研究科, 助教授 (90211078)
YOSHIKAWA Kazuhiro Aichi Medical University, Department of Pathology, Assistant professor, 病理, 講師 (60109759)
|Budget Amount *help
¥3,500,000 (Direct Cost : ¥3,500,000)
Fiscal Year 2005 : ¥800,000 (Direct Cost : ¥800,000)
Fiscal Year 2004 : ¥1,200,000 (Direct Cost : ¥1,200,000)
Fiscal Year 2003 : ¥1,500,000 (Direct Cost : ¥1,500,000)
Background. We analyzed the correlation between interferon-α (IFNα) response and gene expression profiles to predict IFNα sensitivity and identified key molecules regulating the IFNα response in renal cell carcinoma (RCC).
Methods. To evaluate patients with RCC according to IFNα-response, gene expression profiling was done using 3,840 clones microarray, and then IFNα-responder related genes were selected by clustering analysis. Basically, IFNα-response of RCC cell lines was evaluated by MTT assay with 300 to 10,000 IU/ml of IFNα treatment. Microarray, followed by supervised hierarchical clustering analysis, was applied to selected genes according to IFNα sensitivity. In order to find alteration of expression profiles induced by IFNα, sequential microarray analyses were performed at 3,6, and 12 hrs after IFNα treatment of RCC cell lines and mRNA expression level was confirmed using quantitative RT-PCR. A model for prediction of IFNα response was created according to the statistical correlation between gene expression profiles and IFNα-sensitivity.
Results. According to microarray analysis in 14 patients with RCC,IFNα-responder might be identified by the selected gene sets. Microarray followed clustering analysis between IFNα-sensitive and IFNα-resistant line, seven genes, ADFP,MITF,MTUS1,PME-1,MLLT3,MLF1, and TNNT1, were eventually selected as candidates for IFNα-sensitivity-related genes in RCC cell lines. We further developed a model to predict tumor-inhibition with four molecules, i.e., ADFP,MITF,MTUS1, and TNNT1, using multiple linear regression analysis (coefficient=0.948,p=0.0291) and validated the model using primary cultured RCC cells.
Conclusions. The expression levels of the combined selected genes can provide predictive information on the IFNα response in RCC. Furthermore, the IFNα-response to RCC might be modulated by regulation of the expression level of these molecules.