OBJECTIVES: DNA hypermethylation in promoter regions has been studied for various types of cancer. However, there is no clear evidence that shows whether methylation status can predict long-term survival in patients with lung cancer. METHODS: We collected tissues from 72 patients with lung adenocarcinomas. The cancer and normal lung tissues were tested for DNA hypermethylation by using methylation-specific polymerase chain reaction. The genes investigated were p16INK4alpha(p16), retinoic acid receptor beta-promoter (RARbetaP2), death-associated protein kinase (DAPK), O6-methylguanine-DNA-methyltransferase (MGMT), and glutathione-S-transferase P1 (GSTP1). The status of the DNA methylation was analyzed, and we focused on long-term outcomes, as well as other clinical variables. RESULTS: DNA hypermethylation was observed in 83% for p16, 63% for RARbetaP2, 32% for DAPK, 17% for MGMT, and 46% for GSTP1 from the cancer tissue. From normal lung tissue, the results of methylation were positive in 75% for p16, 24% for RARbetaP2, 10% for DAPK, 6% for MGMT, and 33% for GSTP1. During the mean follow-up period of 18 +/- 11 months (1-40 months), 25 (35%) patients experienced recurrence, and 13 died. In multivariable analysis, old age (>60 years, P = .007), male sex (P = .004), unmethylation of DAPK from cancer tissue (P = .045), and hypermethylation of RARbetaP2 from normal tissue (P = .000) were risk factors for poor survival. Pathologic stage (P = .023), unmethylation of DAPK from normal tissue (P = .043), and hypermethylation of RARbetaP2 from normal tissue (P = .030) were risk factors for disease-free survival. CONCLUSIONS: DNA methylation status of CpG islands seems to be a useful predictor of long-term outcome for adenocarcinoma of the lung. However, because the predictive power is still low, further studies, including those with multiple genes, are necessary to increase its usefulness in the clinical setting.
OBJECTIVES: DNA hypermethylation in promoter regions has been studied for various types of cancer. However, there is no clear evidence that shows whether methylation status can predict long-term survival in patients with lung cancer. METHODS: We collected tissues from 72 patients with lung adenocarcinomas. The cancer and normal lung tissues were tested for DNA hypermethylation by using methylation-specific polymerase chain reaction. The genes investigated were p16INK4alpha(p16), retinoic acid receptor beta-promoter (RARbetaP2), death-associated protein kinase (DAPK), O6-methylguanine-DNA-methyltransferase (MGMT), and glutathione-S-transferase P1 (GSTP1). The status of the DNA methylation was analyzed, and we focused on long-term outcomes, as well as other clinical variables. RESULTS: DNA hypermethylation was observed in 83% for p16, 63% for RARbetaP2, 32% for DAPK, 17% for MGMT, and 46% for GSTP1 from the cancer tissue. From normal lung tissue, the results of methylation were positive in 75% for p16, 24% for RARbetaP2, 10% for DAPK, 6% for MGMT, and 33% for GSTP1. During the mean follow-up period of 18 +/- 11 months (1-40 months), 25 (35%) patients experienced recurrence, and 13 died. In multivariable analysis, old age (>60 years, P = .007), male sex (P = .004), unmethylation of DAPK from cancer tissue (P = .045), and hypermethylation of RARbetaP2 from normal tissue (P = .000) were risk factors for poor survival. Pathologic stage (P = .023), unmethylation of DAPK from normal tissue (P = .043), and hypermethylation of RARbetaP2 from normal tissue (P = .030) were risk factors for disease-free survival. CONCLUSIONS: DNA methylation status of CpG islands seems to be a useful predictor of long-term outcome for adenocarcinoma of the lung. However, because the predictive power is still low, further studies, including those with multiple genes, are necessary to increase its usefulness in the clinical setting.
Authors: Kristy L Richards; Baili Zhang; Menghong Sun; Wenli Dong; Jennifer Churchill; Linda L Bachinski; Charmaine D Wilson; Keith A Baggerly; Guosheng Yin; D Neil Hayes; Ignacio I Wistuba; Ralf Krahe Journal: Cancer Date: 2010-10-13 Impact factor: 6.860
Authors: Qinghua Feng; Stephen E Hawes; Joshua E Stern; Linda Wiens; Hiep Lu; Zhao Ming Dong; C Diana Jordan; Nancy B Kiviat; Hubert Vesselle Journal: Cancer Epidemiol Biomarkers Prev Date: 2008-03 Impact factor: 4.254
Authors: Maribel Botana-Rial; Loretta De Chiara; Diana Valverde; Virginia Leiro-Fernández; Cristina Represas-Represas; Victor Del Campo-Pérez; Alberto Fernández-Villar Journal: Cancer Biol Ther Date: 2012-09-06 Impact factor: 4.742
Authors: Hongdo Do; Nicholas C Wong; Carmel Murone; Thomas John; Benjamin Solomon; Paul L Mitchell; Alexander Dobrovic Journal: Sci Rep Date: 2014-02-26 Impact factor: 4.379