Dataset Information

Gene Expression data from 56 lung cancer cell lines

ABSTRACT: Highlighting the gene expression characteristics of a large panel of lung cancer cell lines. Microarrays were used to explore and detail the global gene expression profile of 56 lung cancer cell lines

ORGANISM(S): Homo sapiens

PROVIDER: GSE47206 | GEO | 2013/05/24

SECONDARY ACCESSION(S): PRJNA205106

REPOSITORIES: GEO

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Project description:Non-small cell lung cancer (NSCLC) cell lines are widely used model systems to study molecular aspects of lung cancer. Comparative and in-depth proteome expression data across many NSCLC cell lines has not been generated yet, but would be of utility for the investigation of candidate targets and markers in oncogenesis. We employed a SILAC reference approach to perform replicate proteome quantifications across 23 distinct NSCLC cell lines. On average, close to 4000 distinct proteins were identified and quantified per cell line. These included many known targets and diagnostic markers, indicating that our proteome expression data represents a useful resource for NSCLC pre-clinical research. To assess proteome diversity within the NSCLC cell line panel, we performed hierarchical clustering and principal component analysis of proteome expression data. Our results indicate that general proteome diversity among NSCLC cell lines supersedes potential effects common to K-Ras or epidermal growth factor receptor (EGFR) oncoprotein expression. However, we observed partial segregation of EGFR or KRAS mutant cell lines for certain principal components, which reflected biological differences according to gene ontology enrichment analyses. Moreover, statistical analysis revealed several proteins that were significantly overexpressed in KRAS or EGFR mutant cell lines. Biological significance Despite enormous progress in molecular characterization and targeted therapy NSCLC represents a major cause for cancer-related deaths. While pre-clinical models such as NSCLC cell lines have been studied on the genomic and transcriptional level, proteome composition is poorly characterized. We conducted quantitative profiling across 23 NSCLC cell lines and studied global proteome diversity in relation to the presence of oncogenic KRAS or EGFR mutations. Notably, in-depth bioinformatics analysis pointed to prominent biological processes as well as up-regulated proteins in KRAS and EGFR mutant cells, highlighting the utility of cancer cell proteomics to identify target or biomarker candidates in the context of specific oncogenic mechanisms.

Dataset Information

Gene Expression data from 56 lung cancer cell lines

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