Dataset Information

Comparison of lung cancer cell lines representing four histopathological subtypes with gene expression profiling using quantitative real-time PCR.

ABSTRACT:

Background

Lung cancers are the most common type of human malignancy and are intractable. Lung cancers are generally classified into four histopathological subtypes: adenocarcinoma (AD), squamous cell carcinoma (SQ), large cell carcinoma (LC), and small cell carcinoma (SC). Molecular biological characterization of these subtypes has been performed mainly using DNA microarrays. In this study, we compared the gene expression profiles of these four subtypes using twelve human lung cancer cell lines and the more reliable quantitative real-time PCR (qPCR).

Results

We selected 100 genes from public DNA microarray data and examined them by DNA microarray analysis in eight test cell lines (A549, ABC-1, EBC-1, LK-2, LU65, LU99, STC 1, RERF-LC-MA) and a normal control lung cell line (MRC-9). From this, we extracted 19 candidate genes. We quantified the expression of the 19 genes and a housekeeping gene, GAPDH, with qPCR, using the same eight cell lines plus four additional validation lung cancer cell lines (RERF-LC-MS, LC-1/sq, 86-2, and MS-1-L). Finally, we characterized the four subtypes of lung cancer cell lines using principal component analysis (PCA) of gene expression profiling for 12 of the 19 genes (AMY2A, CDH1, FOXG1, IGSF3, ISL1, MALL, PLAU, RAB25, S100P, SLCO4A1, STMN1, and TGM2). The combined PCA and gene pathway analyses suggested that these genes were related to cell adhesion, growth, and invasion. S100P in AD cells and CDH1 in AD and SQ cells were identified as candidate markers of these lung cancer subtypes based on their upregulation and the results of PCA analysis. Immunohistochemistry for S100P and RAB25 was closely correlated to gene expression.

Conclusions

These results show that the four subtypes, represented by 12 lung cancer cell lines, were well characterized using qPCR and PCA for the 12 genes examined. Certain genes, in particular S100P and CDH1, may be especially important for distinguishing the different subtypes. Our results confirm that qPCR and PCA analysis provide a useful tool for characterizing cancer cell subtypes, and we discuss the possible clinical applications of this approach.

SUBMITTER: Watanabe T

PROVIDER: S-EPMC2817686 | biostudies-literature | 2010 Jan

REPOSITORIES: biostudies-literature

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Publications

Comparison of lung cancer cell lines representing four histopathological subtypes with gene expression profiling using quantitative real-time PCR.

Watanabe Takashi T Miura Tomohiro T Degawa Yusuke Y Fujita Yuna Y Inoue Masaaki M Kawaguchi Makoto M Furihata Chie C

Cancer cell international 20100121

<h4>Background</h4>Lung cancers are the most common type of human malignancy and are intractable. Lung cancers are generally classified into four histopathological subtypes: adenocarcinoma (AD), squamous cell carcinoma (SQ), large cell carcinoma (LC), and small cell carcinoma (SC). Molecular biological characterization of these subtypes has been performed mainly using DNA microarrays. In this study, we compared the gene expression profiles of these four subtypes using twelve human lung cancer ce ...[more]

PMID: 20142997

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Project description:Chlamydia pneumoniae, an important respiratory pathogen, is difficult to culture, and detection rates by conventional PCRs vary considerably. A new quantitative ompA-based real-time PCR assay based on TaqMan technology for detection of C. pneumoniae in respiratory samples is described, and its performance in terms of sensitivity and reproducibility is compared with those of four published conventional PCRs (one single-step PCR targeting a cloned PstI fragment; two nested PCRs, one targeting the 16S rRNA gene followed by hybridization and the other targeting the ompA gene; and a touchdown enzyme time-release [TETR] PCR also targeting the 16S rRNA gene). Both ompA-based PCRs showed the best analytical sensitivity. All five assays could detect even lower target levels from spiked sputum, with the 16S rRNA assays performing better than the ompA-based nested PCR (10(-6) inclusion-forming units [IFU] were detected in four of four and two of four replicates by the 16S rRNA TETR PCR and the 16S rRNA nested PCR, respectively). In general, the ompA-based real-time protocol produced the most consistent positive results for all replicates tested down to 10(-6) IFU. Eight of 45 patient sputum specimens (18%) were C. pneumoniae DNA positive in at least one of four replicates tested by at least one assay. Without taking into consideration the analytical sensitivity or the reproducibility of the test results, the numbers of C. pneumoniae DNA-positive sputum specimens (n = 8) were four, three, two, two, and one for the 16S rRNA TETR assay, the PstI-based single-step PCR, the ompA-based real-time PCR, the ompA-based nested touchdown PCR, and the 16S rRNA-based nested PCR, respectively. However, the overall rate of concordance of positive results was low. Only one cell culture-positive sputum specimen was positive by four of five assays (14 of 16 replicates; mean cycle threshold value, 25; 10(8) particles/ml of sputum). Thirty-seven specimens were C. pneumoniae negative by all five assays for all replicates tested, as were all negative controls (n = 65 to 100 per testing panel). No PCR inhibitors were detected by real-time PCR or by the 16S rRNA-based nested assay. We confirm that the analytical sensitivity of an assay for the detection of C. pneumoniae does not necessarily predict its ability to detect its target in sputum. A quantitative, fast, and easy-to-handle diagnostic approach such as the ompA-based real-time TaqMan PCR described here might improve the detection of C. pneumoniae in respiratory samples.

Dataset Information

Comparison of lung cancer cell lines representing four histopathological subtypes with gene expression profiling using quantitative real-time PCR.

Background

Results

Conclusions

Publications

Comparison of lung cancer cell lines representing four histopathological subtypes with gene expression profiling using quantitative real-time PCR.

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