Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Transcription profiling of human non-small cell lung cancer adenocarcinoma and squamous cell carcinoma subtypes

ABSTRACT: Non-small cell lung cancer (NSCLC) can be classified into the major subtypes adenocarcinoma (AC) and squamous cell carcinoma (SCC) subtypes. Although explicit molecular, histological and clinical characteristics have been reported for both subtypes, no specific therapy exists so far. However, the characterization of suitable molecular targets holds great promises to develop novel therapies in NSCLC. In the present study, global gene expression profiling of 58 human high grade NSCLC specimens revealed large transcriptomic differences between AC and SCC subtypes: More than 1.700 genes were found to be differentially expressed. Experiment Overall Design: The NSCLC patient collective was composed of the histological subtype adenocarcinoma (n=40) and squamous cell carcinoma (n=18). We subjected gene expression profiles of 40 AC and 18 SCC samples into further analysis. Unsupervised hierarchical clustering of all 58 NSCLC tumors using the 500 most variably expressed transcripts revealed two different clusters, which were strongly associated with the histological subtypes AC and SCC of NSCLC. Our result indicated that the major impact on global transcriptional changes was due to the NSCLC histology.

ORGANISM(S): Homo sapiens

SUBMITTER: Ruprecht Kuner

PROVIDER: E-GEOD-10245 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Similar Datasets

Project description:Squamous cell lung cancer (SCC) and adenocarcinoma are the most common subtypes of the lung tumours. The search for cancer-directed treatments has increased the need for understanding molecular features of either histological subtype. The aim of this study was to identify transcriptional regulation differences due to miRNA expression profiles between SCC and adenocarcinoma. For this propose, a total of 44 patients were evaluated to assess the correlation between the miRNA and messenger RNA (mRNA) expression levels. Total RNA were isolated, amplified, labeled and hybridized on Agilent human whole genome V2 22 K microarray chip. qRT-PCR was conducted to validate our microarray data. MicroRNA expression was detected and quantified using the TaqMan Low Density Arrays. Predicted miRNA-mRNA interactions were taken from miRanda, miRWalk and TargetScan. After processing data, changes in 56 mRNAs as well as in 9 miRNAs were detected between SCC and adenocarcinoma. Nearly 20% of overall deregulated genes were targeted by at least one of the 9 miRNAs (miR-149, miR-205, miR-375, miR-378, miR-422a, miR-483-5p, miR-494, miR-601 and miR-708) differentially expressed between SCC and adenocarcinoma. Genes predicted (CEACAM6, CGN, CLDN3, ABCC3, MLPH, ACSL5, TMEM45B, MUC1) to be targeted by several miRNAs were individually validated by qRT-PCR. We found genes involved in tight junctions and other involved in resistance to anticancer agents. These genes were reliable biomarkers, with high sensitivity and specificity, to detect differences between the two most common histological subtypes of lung cancer. In conclusion, our data demonstrate that the transcriptional regulation differences through miRNA expression play an important role in key hallmarks of non-small cell lung cancer. Identification of new molecular markers in lung carcinoma 44 tumour samples following surgical resection for clinical early stage NSCLC (SCC and adenocarcinoma) This series represents the mRNA profiling only (not miRNA). The related miRNA data are in Series GSE43000.

Project description:Lung cancers are a heterogeneous group of diseases with respect to biology and clinical behavior. So far, diagnosis and classification are based on histological morphology and immunohistological methods for discrimination between two main histologic groups: small cell lung cancer (SCLC) and non-small cell lung cancer which account for 20% and 80% of lung carcinomas, respectively. While SCLCs express properties of neuroendocrine cells, NSCLCs, which are divided into the three major subtypes adenocarcinoma, squamous cell carcinoma and dedifferentiated large cell carcinoma, show different characteristics such as the expression of certain keratins or production of mucin and lack neuroedocrine differentiation. The molecular pathogenesis of lung cancer involves the accumulation of genetic und epigenetic alterations including the activation of proto-oncogenes and inactivation of tumor suppressor genes which are different for lung cancer subgroups. The development of microarray technologies opened up the possibility to quantify the expression of a large number of genes simultaneously in a given sample. There are several recent reports on expression profiling on lung cancers but the analysis interpretation of the results might be difficult because of the heterogeneity of cellular components. A contamination of the tumor sample with normal epithelia, blood vessels, stromal cells, leucocytes and tumor necrosis may confound the true expression profile of the tumor. The use of laser capture microdissection (LCM) greatly improves the sample preparation for microarray expression analysis. Consequently, we used advanced technology including LCM and microarray analysis. In detail, we examined gene expression profiles of tumor cells from 29 previously untreated patients with lung cancer (10 adenocarcinomas (AC), 10 squamous cell carcinomas (SCC), 9 small cell lung cancer (SCLC)) in comparison to normal lung tissue (LT) of 5 control patients without tumor. Bronchoscopical biopsies from the primary lung tumor were taken before treatment. Biopsies were cut into 8Âµm sections and from each section cancer cells were isolated using laser capture microdissection in order to obtain pure samples of tumor cells. Total RNA was extracted, reversely transcribed, in-vitro transcribed, labelled and hybridized to the array. For expression analysis, microarrays covering 8793 defined genes (Human HG Focus Array, Affymetrix) were used. Following quality control, array data were normalized and analysed for significant differences using variance stabilizing transformation (VSN) and significance analysis of microarrays (SAM), respectively. Based on differentially expressed genes cancer samples could be clearly separated from non cancer samples using hierarchical clustering. Comparing AC, SCC and SCLC with normal lung tissue, we found 205, 335 and 404 genes, respectively, that were at least 2-fold differentially expressed with an estimated false discovery rate < 2.6%. Each histological subtype showed a distinct expression profile. Further, using a genetic programming approach we constructed a classificator to discriminate AC, SCC, NT and SCLC. To this end, the 50 genes with the greatest signal-to-noise ratio were selected to train the classificator. By leave-one-out cross validation all 34 samples were correctly classified in this training set. In order to validate the 50-gene-classificator on a test set, further 13 microdissected lung cancer samples were used and correctly classified in concordance to pathologic finding. In conclusion, the different lung cancer subtypes have distinct molecular phenotypes which reflect biological characteristics of the tumor cells and which might be the basis for development of targeted therapy. Moreover, gene expression profiling and genetic programming is a suitable tool for classification and discrimination of different histological subtypes in lung cancer in comparison to normal lung tissue. Experiment Overall Design: Comparison of gene expression profiles of normal lung tissues, adenocarcinomas, squamous cell carcinomas and small cell lung cancers.

Dataset Information

Transcription profiling of human non-small cell lung cancer adenocarcinoma and squamous cell carcinoma subtypes

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets