Generation of a non-small cell lung cancer transcriptome microarray

Almac Diagnostics Ltd, 19 Seagoe Industrial Estate, Craigavon, BT63 5QD, UK.
BMC Medical Genomics (Impact Factor: 2.87). 02/2008; 1(1):20. DOI: 10.1186/1755-8794-1-20
Source: PubMed

ABSTRACT 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.
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.
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.
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.

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Available from: Vadim Farztdinov, Sep 26, 2015
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    • "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. A combination of large-scale in-house sequencing, gene expression profiling and public sequence and gene expression data mining were used to characterize the transcriptome of NSCLC [6]. Identifying a useful prognostic biologic and molecular marker is therefore important to evaluate the biologic and molecular characteristics that differed from tumor, lymph node, metastasis TNM staging in non-small cell lung cancer (NSCLC) in order to predict prognosis and establish preventive methods [7]. "
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    ABSTRACT: Rapid distinction between small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) tumors is very important in diagnosis of this disease. Furthermore sequence-derived structural and physicochemical descriptors are very useful for machine learning prediction of protein structural and functional classes, classifying proteins and the prediction performance. Herein, in this study is the classification of lung tumors based on 1497 attributes derived from structural and physicochemical properties of protein sequences (based on genes defined by microarray analysis) investigated through a combination of attribute weighting, supervised and unsupervised clustering algorithms. Eighty percent of the weighting methods selected features such as autocorrelation, dipeptide composition and distribution of hydrophobicity as the most important protein attributes in classification of SCLC, NSCLC and COMMON classes of lung tumors. The same results were observed by most tree induction algorithms while descriptors of hydrophobicity distribution were high in protein sequences COMMON in both groups and distribution of charge in these proteins was very low; showing COMMON proteins were very hydrophobic. Furthermore, compositions of polar dipeptide in SCLC proteins were higher than NSCLC proteins. Some clustering models (alone or in combination with attribute weighting algorithms) were able to nearly classify SCLC and NSCLC proteins. Random Forest tree induction algorithm, calculated on leaves one-out and 10-fold cross validation) shows more than 86% accuracy in clustering and predicting three different lung cancer tumors. Here for the first time the application of data mining tools to effectively classify three classes of lung cancer tumors regarding the importance of dipeptide composition, autocorrelation and distribution descriptor has been reported.
    PLoS ONE 12/2012; 7(7):e40017. DOI:10.1371/journal.pone.0040017 · 3.23 Impact Factor
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    • "It has been recognized that various genetic changes contribute to cancer invasion and metastasis. High-throughput microarray technology facilitates simultaneous analysis of gene expressions and identification of numerous differentially expressed genes (Khan et al., 1999; Ross et al., 2000; Tanney et al., 2008). Using this technique, many studies have been done to determine the metastatic information of individual genes (Marchetti et al., 2002; Aviel-Ronen et al., 2008), cell lines with different invasive activities (Xu et al., 2008; Li et al., 2010), and differences between the primary tumors and normal lung tissues in gene expression level (Heighway et al., 2002). "
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    ABSTRACT: Distant metastasis is one of the leading causes of lung cancer death. Detecting the early-stage molecular alternations in primary tumors, such as gene expression differences, provides a "prognostic" value to the precaution of tumor metastasis. The aim of this article is to screen and identify the metastasis-related genes in human squamous cell lung carcinoma. Primary tumor tissues of nine patients with subsequent metastasis and eight patients without metastasis were selected to perform the gene microarray experiment. GO and pathway analyses were used to determine the differentially expressed genes. Two identified genes were further validated by real-time quantitative reverse transcription polymerase chain reaction (PCR) (real-time qRT-PCR). Two hundred and thirty-eight differentially expressed genes were detected in gene chip experiment, including 51 up-regulated genes and 187 down-regulated genes. These genes were involved in several cellular processes, including cell adhesion, cell cycle regulation, and apoptosis. GO analysis showed that the differentially expressed genes participated in a wide ranging of metastasis-related processes, including extracellular region and regulation of liquid surface tension. In addition, pathway analysis demonstrated that the differentially expressed genes were enriched in pathways related to cell cycle and Wnt signaling. Real-time qRT-PCR validation experiment of LCN2 and PDZK1IP1 showed a consistent up-regulation in the metastasis group. The metastasis of human squamous cell lung carcinoma is a complex process that is regulated by multiple gene alternations on the expression levels. The 238 differentially expressed genes identified in this study presumably contain a core set of genes involved in tumor metastasis. The real-time qRT-PCR results of PDZK1IP1 and LCN2 validated the reliability of this gene microarray experiment.
    The Anatomical Record Advances in Integrative Anatomy and Evolutionary Biology 05/2012; 295(5):748-57. DOI:10.1002/ar.22441 · 1.54 Impact Factor
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    • "array) or a disease-specific transcriptomic-based microarray (Colorectal DSA). The Colorectal DSA was developed based on the colorectal transcriptome, which was generated from large-scale in-house sequencing, public data mining and experimental investigation [32]. The DSA array is a transcriptome based array as opposed to the Plus 2.0 which a genomic based array. "
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    ABSTRACT: To date, there are no clinically reliable predictive markers of response to the current treatment regimens for advanced colorectal cancer. The aim of the current study was to compare and assess the power of transcriptional profiling using a generic microarray and a disease-specific transcriptome-based microarray. We also examined the biological and clinical relevance of the disease-specific transcriptome. DNA microarray profiling was carried out on isogenic sensitive and 5-FU-resistant HCT116 colorectal cancer cell lines using the Affymetrix HG-U133 Plus2.0 array and the Almac Diagnostics Colorectal cancer disease specific Research tool. In addition, DNA microarray profiling was also carried out on pre-treatment metastatic colorectal cancer biopsies using the colorectal cancer disease specific Research tool. The two microarray platforms were compared based on detection of probesets and biological information. The results demonstrated that the disease-specific transcriptome-based microarray was able to out-perform the generic genomic-based microarray on a number of levels including detection of transcripts and pathway analysis. In addition, the disease-specific microarray contains a high percentage of antisense transcripts and further analysis demonstrated that a number of these exist in sense:antisense pairs. Comparison between cell line models and metastatic CRC patient biopsies further demonstrated that a number of the identified sense:antisense pairs were also detected in CRC patient biopsies, suggesting potential clinical relevance. Analysis from our in vitro and clinical experiments has demonstrated that many transcripts exist in sense:antisense pairs including IGF2BP2, which may have a direct regulatory function in the context of colorectal cancer. While the functional relevance of the antisense transcripts has been established by many studies, their functional role is currently unclear; however, the numbers that have been detected by the disease-specific microarray would suggest that they may be important regulatory transcripts. This study has demonstrated the power of a disease-specific transcriptome-based approach and highlighted the potential novel biologically and clinically relevant information that is gained when using such a methodology.
    BMC Cancer 12/2010; 10(1):687. DOI:10.1186/1471-2407-10-687 · 3.36 Impact Factor
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