Project description:This SuperSeries is composed of the following subset Series: GSE29248: Genome-wide analysis of microRNA expression in Non-small Cell Lung Cancer GSE29249: Genome-wide analysis of gene expression in Non-small Cell Lung Cancer Refer to individual Series
Project description:In addition to the generation and analysis of metabolomics data on cell lines, samples of normal lung tissue, adenocarcinoma lung tissue and small cell lung carcinoma tissue (seven samples/group) were processed and evaluated metabolite profile differences under the scope of the pilot and feasibility study. These data can be correlated to the metabolite profiles defined in the SCLC and NSCLC cell lines and integrated with the ABPP-determined metabolic kinases to identify distinct metabolic signatures or biomarkers (?oncometabolites?) that distinguish small cell lung cancer from non-small cell lung cancer.
Project description:We present comprehensive epigenomic and transcriptomic mapping of 18 tumor and 20 non-neoplastic tissues from non-small cell lung adenocarcinoma patients. Our profiling covers active marks (H3K4me3, H3K4me1, and H3K27ac), repressive marks (H3K27me3 and H3K9me3) and gene expression using only 20 mg of tissue. Genome-wide differentially modified peaks and differentially expressed genes associated with non-small cell lung cancer were identified. Key pathways related to cancer and cell proliferation were found to be enriched among these regions. 118 differential transcription factors (TF) were identified by Taiji, showing a global impact on TF in tumor. Regulation associated modules (RAM) analysis by findRAM uncovered consensus RAMs (cRAM) specific to non-plastic or tumor samples, most of which were marked with differential histone modification and gene expression. Integrative analysis by EpiSig identified 6 sections of genomic regions with distinct co-modification patterns. These clusters showed a clear distribution pattern over the specific cRAMs and were enriched in key cancer related pathways. Our results demonstrate the power of integrated analysis of multiple epigenomic and transcriptomic marks in patient samples.
