tailieunhanh - Báo cáo hóa học: " Multiplexed methylation profiles of tumor suppressor genes and clinical outcome in lung cancer"

Multiplexed methylation profiles of tumor suppressor genes and clinical outcome in lung cancer | Castro et al. Journal of Translational Medicine 2010 8 86 http content 8 1 86 RESEARCH JOURNAL OF TRANSLATIONAL MEDICINE Open Access Multiplexed methylation profiles of tumor suppressor genes and clinical outcome in lung cancer 2 1 1 2 3 3 Mónica Castro Laura Grau Patricia Puerta Liliana Gimenez Julio Venditti Silvia Quadrelli Marta Sánchez-Carbayo1 Abstract Background Changes in DNA methylation of crucial cancer genes including tumor suppressors can occur early in carcinogenesis being potentially important early indicators of cancer. The objective of this study was to examine a multiplexed approach to assess the methylation of tumor suppressor genes as tumor stratification and clinical outcome prognostic biomarkers for lung cancer. Methods A multicandidate probe panel interrogated DNA for aberrant methylation status in 18 tumor suppressor genes in lung cancer using a methylation-specific multiplex ligation-dependent probe amplification assay MS-MLPA . Lung cancer cell lines n 7 and primary lung tumors n 54 were examined using MS-MLPA. Results Genes frequently methylated in lung cancer cell lines including SCGB3A1 ID4 CCND2 were found among the most commonly methylated in the lung tumors analyzed. HLTF BNIP3 H2AFX CACNA1G TGIF ID4 and CACNA1A were identified as novel tumor suppressor candidates methylated in lung tumors. The most frequently methylated genes in lung tumors were SCGB3A1 and DLC1 both . Methylation rates for ID4 DCL1 BNIP3 H2AFX CACNA1G and TIMP3 were significantly different between squamous and adenocarcinomas. Methylation of RUNX3 SCGB3A1 SFRP4 and DLC1 was significantly associated with the extent of the disease when comparing localized versus metastatic tumors. Moreover methylation of HTLF SFRP5 and TIMP3 were significantly associated with overall survival. Conclusions MS-MLPA can be used for classification of certain types of lung tumors and clinical outcome prediction. This latter is clinically relevant by .