UNLABELLED: High density DNA methylation microarrays were used to study the differences of gene methylation level in six pairs of colorectal cancer (CRC) and adjacent normal mucosa. We analyzed the profile of methylated genes by NimbleGen Microarray and the biologic functions by NIH-NAVID. In addition, preliminary validation studies were done in six pairs of samples by MSP (methylation-specific PCR). A total of 4,644 genes had a difference in methylation levels. Among them 2,296 were hypermethylated (log2ratio > 1), 2,348 genes were hypomethylated (log2ratio < -1), in which 293 hypermethylated and 313 hypomethylated genes were unmapped according to the NIH-NAVID. All these genes were randomly distributed on all the chromosomes. However, chromosome 1 contained the most of the hypermethylated genes (232 genes), followed by chromosome 19 (149 genes), chromosome 11 (144 genes), chromosome 2 (141 genes), chromosomes 3 (127 genes). Through the analysis of the statistics, There were 2 hypermethylated/3 hypomethylated genes involved in six pairs of samples simultaneously, followed by 10/14 in five samples, 34/37 in four samples, 101/113 in three samples, 341/377 in two samples, 1,808/1,804 in one sample. According to gene ontology analysis, some physiological processes play important roles in the cell division and the development of tumor, such as apoptosis, DNA repair, immune, cell cycle, cell cycle checkpoint, cell adhesion and invasion etc. Through Preliminary validation, there were two genes (St3gal6, Opcml) in thirty top-ranking genes shown hypermethylated in six pairs of CRC and adjacent normal mucosa. CONCLUSIONS: High density DNA methylation microarrays is an effective method for screening aberrantly methylated genes in CRC. The methylated genes should be further studied for diagnostic or prognostic markers for CRC.
UNLABELLED: High density DNA methylation microarrays were used to study the differences of gene methylation level in six pairs of colorectal cancer (CRC) and adjacent normal mucosa. We analyzed the profile of methylated genes by NimbleGen Microarray and the biologic functions by NIH-NAVID. In addition, preliminary validation studies were done in six pairs of samples by MSP (methylation-specific PCR). A total of 4,644 genes had a difference in methylation levels. Among them 2,296 were hypermethylated (log2ratio > 1), 2,348 genes were hypomethylated (log2ratio < -1), in which 293 hypermethylated and 313 hypomethylated genes were unmapped according to the NIH-NAVID. All these genes were randomly distributed on all the chromosomes. However, chromosome 1 contained the most of the hypermethylated genes (232 genes), followed by chromosome 19 (149 genes), chromosome 11 (144 genes), chromosome 2 (141 genes), chromosomes 3 (127 genes). Through the analysis of the statistics, There were 2 hypermethylated/3 hypomethylated genes involved in six pairs of samples simultaneously, followed by 10/14 in five samples, 34/37 in four samples, 101/113 in three samples, 341/377 in two samples, 1,808/1,804 in one sample. According to gene ontology analysis, some physiological processes play important roles in the cell division and the development of tumor, such as apoptosis, DNA repair, immune, cell cycle, cell cycle checkpoint, cell adhesion and invasion etc. Through Preliminary validation, there were two genes (St3gal6, Opcml) in thirty top-ranking genes shown hypermethylated in six pairs of CRC and adjacent normal mucosa. CONCLUSIONS: High density DNA methylation microarrays is an effective method for screening aberrantly methylated genes in CRC. The methylated genes should be further studied for diagnostic or prognostic markers for CRC.
Authors: Toshinori Hinoue; Daniel J Weisenberger; Christopher P E Lange; Hui Shen; Hyang-Min Byun; David Van Den Berg; Simeen Malik; Fei Pan; Houtan Noushmehr; Cornelis M van Dijk; Rob A E M Tollenaar; Peter W Laird Journal: Genome Res Date: 2011-06-09 Impact factor: 9.043
Authors: Quynh N Vo; Wan-Ju Kim; Luke Cvitanovic; Donald A Boudreau; David G Ginzinger; Kevin D Brown Journal: Oncogene Date: 2004-12-16 Impact factor: 9.867
Authors: J M Cunningham; E R Christensen; D J Tester; C Y Kim; P C Roche; L J Burgart; S N Thibodeau Journal: Cancer Res Date: 1998-08-01 Impact factor: 12.701
Authors: Marcos R H Estécio; Pearlly S Yan; Ashraf E K Ibrahim; Carmen S Tellez; Lanlan Shen; Tim H-M Huang; Jean-Pierre J Issa Journal: Genome Res Date: 2007-09-04 Impact factor: 9.043
Authors: Wael M Abdel-Rahman; Johanna E Lotsari-Salomaa; Sippy Kaur; Anni Niskakoski; Sakari Knuutila; Heikki Järvinen; Jukka-Pekka Mecklin; Päivi Peltomäki Journal: Gastroenterol Res Pract Date: 2016-03-07 Impact factor: 2.260
Authors: Dylan M Glubb; Sharon E Johnatty; Michael C J Quinn; Tracy A O'Mara; Jonathan P Tyrer; Bo Gao; Peter A Fasching; Matthias W Beckmann; Diether Lambrechts; Ignace Vergote; Digna R Velez Edwards; Alicia Beeghly-Fadiel; Javier Benitez; Maria J Garcia; Marc T Goodman; Pamela J Thompson; Thilo Dörk; Matthias Dürst; Francesmary Modungo; Kirsten Moysich; Florian Heitz; Andreas du Bois; Jacobus Pfisterer; Peter Hillemanns; Beth Y Karlan; Jenny Lester; Ellen L Goode; Julie M Cunningham; Stacey J Winham; Melissa C Larson; Bryan M McCauley; Susanne Krüger Kjær; Allan Jensen; Joellen M Schildkraut; Andrew Berchuck; Daniel W Cramer; Kathryn L Terry; Helga B Salvesen; Line Bjorge; Penny M Webb; Peter Grant; Tanja Pejovic; Melissa Moffitt; Claus K Hogdall; Estrid Hogdall; James Paul; Rosalind Glasspool; Marcus Bernardini; Alicia Tone; David Huntsman; Michelle Woo; Aocs Group; Anna deFazio; Catherine J Kennedy; Paul D P Pharoah; Stuart MacGregor; Georgia Chenevix-Trench Journal: Oncotarget Date: 2017-06-15