Dataset Information

Generation of a non-small cell lung cancer transcriptome microarray.

ABSTRACT:

Background

Non-small cell lung cancer (NSCLC) is the leading cause of cancer mortality worldwide. At present no reliable biomarkers are available to guide the management of this condition. Microarray technology may allow appropriate biomarkers to be identified but present platforms are lacking disease focus and are thus likely to miss potentially vital information contained in patient tissue samples.

Methods

A combination of large-scale in-house sequencing, gene expression profiling and public sequence and gene expression data mining were used to characterise the transcriptome of NSCLC and the data used to generate a disease-focused microarray - the Lung Cancer DSA research tool.

Results

Built on the Affymetrix GeneChip platform, the Lung Cancer DSA research tool allows for interrogation of ~60,000 transcripts relevant to Lung Cancer, tens of thousands of which are unavailable on leading commercial microarrays.

Conclusion

We have developed the first high-density disease specific transcriptome microarray. We present the array design process and the results of experiments carried out to demonstrate the array's utility. This approach serves as a template for the development of other disease transcriptome microarrays, including non-neoplastic diseases.

SUBMITTER: Tanney A

PROVIDER: S-EPMC2426710 | biostudies-literature | 2008 May

REPOSITORIES: biostudies-literature

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Publications

Generation of a non-small cell lung cancer transcriptome microarray.

Tanney Austin A Oliver Gavin R GR Farztdinov Vadim V Kennedy Richard D RD Mulligan Jude M JM Fulton Ciaran E CE Farragher Susan M SM Field John K JK Johnston Patrick G PG Harkin D Paul DP Proutski Vitali V Mulligan Karl A KA

BMC medical genomics 20080530

<h4>Background</h4>Non-small cell lung cancer (NSCLC) is the leading cause of cancer mortality worldwide. At present no reliable biomarkers are available to guide the management of this condition. Microarray technology may allow appropriate biomarkers to be identified but present platforms are lacking disease focus and are thus likely to miss potentially vital information contained in patient tissue samples.<h4>Methods</h4>A combination of large-scale in-house sequencing, gene expression profili ...[more]

PMID: 18513400

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Project description:Cancer testis antigens (CTAs) are of clinical interest as biomarkers and present valuable targets for immunotherapy. To comprehensively characterize the CTA landscape of non-small cell lung cancer (NSCLC), we compared RNAseq data of 199 NSCLC tissues to the normal transcriptome of 142 samples from 32 different normal organs. Of 232 CTAs currently annotated in the CTdatabase, 96 were confirmed in NSCLC. To obtain an unbiased CTA profile of NSCLC, we applied stringent criteria on our RNAseq data set and defined 90 genes as CTAs, of which 55 genes were not annotated in the CTdatabase. Cluster analysis revealed that CTA expression is histology-dependent and concurrent expression is common. Immunohistochemistry confirmed tissue specific protein expression of selected genes. Furthermore, methylation was identified as a regulatory mechanism of CTA expression based on independent data from the Cancer Genome Atlas. The proposed prognostic impact of CTAs in lung cancer, was not confirmed, neither in our RNAseq-cohort nor in an independent meta-analysis of 1117 NSCLC cases. Fresh frozen tumor tissue from 199 patients diagnosed with NSCLC and surgically treated 2006-2010 at the Uppsala University Hospital, Uppsala, Sweden and 19 paired normal lung tissues. Clinical data were retrieved from the regional lung cancer registry. Several of the new CTAs are poorly characterized Sample characteristics values represent; pTNM: decided by Hans Brunnstrom, pathologist in Lund Spring 2013 Stage according to pTNM: 1=1a 2=1b 3=2a 4=2b 5=3a 6=3b 7=IV Histology diagnosis spring 2013 HB: 1=squamous cell cancer 2=AC unspecified 3=Large cell/ NOS Surgery date: the date when sample arrived at Patologen UAS Age: age when surgery was performed Vital date: day of death or latest contact Dead: 0=no 1= yes Smoking history : 1=current 2=ex >1year 3=never WHO performance status: Performance status 0-4 Please note that the L608T_2122, L771T_1 data columns (in the processed data files) are associated with L608T and L771T samples, respectively.

Project description:Background: Non-small-cell lung cancer (NSCLC) remains the leading cause of cancer morbidity and mortality worldwide. In the present study, we identified novel biomarkers associated with the pathogenesis of NSCLC aiming to provide new diagnostic and therapeutic approaches for NSCLC. Methods: The microarray datasets of GSE18842, GSE30219, GSE31210, GSE32863 and GSE40791 from Gene Expression Omnibus database were downloaded. The differential expressed genes (DEGs) between NSCLC and normal samples were identified by limma package. The construction of protein-protein interaction (PPI) network, module analysis and enrichment analysis were performed using bioinformatics tools. The expression and prognostic values of hub genes were validated by GEPIA database and real-time quantitative PCR. Based on these DEGs, the candidate small molecules for NSCLC were identified by the CMap database. Results: A total of 408 overlapping DEGs including 109 up-regulated and 296 down-regulated genes were identified; 300 nodes and 1283 interactions were obtained from the PPI network. The most significant biological process and pathway enrichment of DEGs were response to wounding and cell adhesion molecules, respectively. Six DEGs (PTTG1, TYMS, ECT2, COL1A1, SPP1 and CDCA5) which significantly up-regulated in NSCLC tissues, were selected as hub genes according to the results of module analysis. The GEPIA database further confirmed that patients with higher expression levels of these hub genes experienced a shorter overall survival. Additionally, CMap predicted the 20 most significant small molecules as potential therapeutic drugs for NSCLC. DL-thiorphan was the most promising small molecule to reverse the NSCLC gene expression. Conclusions: Based on the gene expression profiles of 696 NSCLC samples and 237 normal samples, we first revealed that PTTG1, TYMS, ECT2, COL1A1, SPP1 and CDCA5 could act as the promising novel diagnostic and therapeutic targets for NSCLC. Our work will contribute to clarifying the molecular mechanisms of NSCLC initiation and progression.

Dataset Information

Generation of a non-small cell lung cancer transcriptome microarray.

Background

Methods

Results

Conclusion

Publications

Generation of a non-small cell lung cancer transcriptome microarray.

Similar Datasets

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