Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

0

The DNA methylation landscape of small cell lung cancer


ABSTRACT: Small cell lung cancer (SCLC) is a disease characterized by aggressive clinical behavior and lack of effective therapy. Due to its high tendency of early dissemination, only a third of patients have limited-stage disease at the time of diagnosis. SCLC is thought to derive from neuroendocrine cells of the lung. Although several molecular abnormalities in SCLC have been described, there are relatively few studies on epigenetic alterations in this type of tumor. Here, we have used methylation profiling with the methylated CpG island recovery assay (MIRA) in combination with microarrays and conducted the first genome-scale analysis of methylation changes that occur in primary SCLC and SCLC cell lines. Among hundreds of tumor-specifically methylated genes, we identified 73 gene targets that are methylated in more than 77% of primary SCLC tumors, most of which have never been linked to aberrant methylation in tumors. These targets have excellent potential for development of biomarkers for early detection of SCLC. SCLC cell lines had an ~3-fold greater number of hypermethylated genes than primary tumors. Gene ontology analysis indicated a significant enrichment of methylated genes functioning as transcription factors and in processes of neuronal differentiation. Motif analysis of tumor-specific methylated regions identified enrichment of binding sites for several neural cell fate-specifying transcription factors including NEUROD1, HAND1, ZNF423 and REST. We hypothesize that functional inactivation of their corresponding binding sites by DNA methylation can lead to an escape route for early progenitor cells towards the malignant state and may contribute to the origin of SCLC. Comparison between healthy and SCLC tumor DNA methylation profiles

ORGANISM(S): Homo sapiens

SUBMITTER: Marc Jung 

PROVIDER: E-GEOD-35341 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

Similar Datasets

2012-01-26 | GSE35341 | GEO
2012-11-26 | E-GEOD-32496 | biostudies-arrayexpress
2014-10-04 | E-GEOD-62021 | biostudies-arrayexpress
2010-04-29 | E-GEOD-21532 | biostudies-arrayexpress
2014-10-04 | GSE62021 | GEO
2013-08-20 | E-GEOD-49221 | biostudies-arrayexpress
2014-10-23 | E-GEOD-51517 | biostudies-arrayexpress
2010-05-17 | E-GEOD-19413 | biostudies-arrayexpress
2011-03-31 | E-GEOD-26040 | biostudies-arrayexpress
2008-10-21 | E-TABM-442 | biostudies-arrayexpress