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Gene expression profile analysis of pancreatic cancer based on microarray data.

ABSTRACT: The present study identified differentially?expressed genes (DEGs) between pancreatic cancer (PC) tissues and normal tissues, and assessed genetic factors associated with the pathogenesis of PC. The mRNA expression microarray dataset, GSE16515, containing 52 samples, including 16 paired tumor and normal tissue samples, and 20 tumor samples, was downloaded from Gene Expression Omnibus. Raw data were normalized and DEGs were identified. Subsequently, clustering was performed, protein?protein interaction networks were drawn, and functional and pathway enrichment analyses of the DEGs were performed. Copy number variations of DEGs were also identified. A total of 1,765 DEGs between PC and normal tissues were identified, including 1,312 upregulated and 453 downregulated DEGs. Upregulated DEGs were associated with the regulation of nucleocytoplasmic and intracellular transport, whereas downregulated DEGs were associated with the response to organic substances and hormone stimulus. The pancreatic cancer pathway was connected to three DEGs, namely transforming growth factor ?1 (TGFB1), TGF? receptor 1 (TGFBR1) and epidermal growth factor (EGF), which had 2, 3 and 5 CNVs, respectively. These results indicated the important roles of TGFB1, TGFBR1 and EGF in the pathogenesis of PC. These genes may be potential therapeutic targets for the treatment of PC.

SUBMITTER: Long J

PROVIDER: S-EPMC4838162 | biostudies-literature | 2016 May

REPOSITORIES: biostudies-literature

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Gene expression profile analysis of pancreatic cancer based on microarray data.

Long Jin J Liu Zhe Z Wu Xingda X Xu Yuanhong Y Ge Chunlin C

Molecular medicine reports 20160321 5

The present study identified differentially‑expressed genes (DEGs) between pancreatic cancer (PC) tissues and normal tissues, and assessed genetic factors associated with the pathogenesis of PC. The mRNA expression microarray dataset, GSE16515, containing 52 samples, including 16 paired tumor and normal tissue samples, and 20 tumor samples, was downloaded from Gene Expression Omnibus. Raw data were normalized and DEGs were identified. Subsequently, clustering was performed, protein‑protein inter ...[more]

PMID: 27035876

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Project description:BackgroundThe extensive use of DNA microarray technology in the characterization of the cell transcriptome is leading to an ever increasing amount of microarray data from cancer studies. Although similar questions for the same type of cancer are addressed in these different studies, a comparative analysis of their results is hampered by the use of heterogeneous microarray platforms and analysis methods.ResultsIn contrast to a meta-analysis approach where results of different studies are combined on an interpretative level, we investigate here how to directly integrate raw microarray data from different studies for the purpose of supervised classification analysis. We use median rank scores and quantile discretization to derive numerically comparable measures of gene expression from different platforms. These transformed data are then used for training of classifiers based on support vector machines. We apply this approach to six publicly available cancer microarray gene expression data sets, which consist of three pairs of studies, each examining the same type of cancer, i.e. breast cancer, prostate cancer or acute myeloid leukemia. For each pair, one study was performed by means of cDNA microarrays and the other by means of oligonucleotide microarrays. In each pair, high classification accuracies (> 85%) were achieved with training and testing on data instances randomly chosen from both data sets in a cross-validation analysis. To exemplify the potential of this cross-platform classification analysis, we use two leukemia microarray data sets to show that important genes with regard to the biology of leukemia are selected in an integrated analysis, which are missed in either single-set analysis.ConclusionCross-platform classification of multiple cancer microarray data sets yields discriminative gene expression signatures that are found and validated on a large number of microarray samples, generated by different laboratories and microarray technologies. Predictive models generated by this approach are better validated than those generated on a single data set, while showing high predictive power and improved generalization performance.

Project description:BACKGROUND: An important use of data obtained from microarray measurements is the classification of tumor types with respect to genes that are either up or down regulated in specific cancer types. A number of algorithms have been proposed to obtain such classifications. These algorithms usually require parameter optimization to obtain accurate results depending on the type of data. Additionally, it is highly critical to find an optimal set of markers among those up or down regulated genes that can be clinically utilized to build assays for the diagnosis or to follow progression of specific cancer types. In this paper, we employ a mixed integer programming based classification algorithm named hyper-box enclosure method (HBE) for the classification of some cancer types with a minimal set of predictor genes. This optimization based method which is a user friendly and efficient classifier may allow the clinicians to diagnose and follow progression of certain cancer types. METHODOLOGY/PRINCIPAL FINDINGS: We apply HBE algorithm to some well known data sets such as leukemia, prostate cancer, diffuse large B-cell lymphoma (DLBCL), small round blue cell tumors (SRBCT) to find some predictor genes that can be utilized for diagnosis and prognosis in a robust manner with a high accuracy. Our approach does not require any modification or parameter optimization for each data set. Additionally, information gain attribute evaluator, relief attribute evaluator and correlation-based feature selection methods are employed for the gene selection. The results are compared with those from other studies and biological roles of selected genes in corresponding cancer type are described. CONCLUSIONS/SIGNIFICANCE: The performance of our algorithm overall was better than the other algorithms reported in the literature and classifiers found in WEKA data-mining package. Since it does not require a parameter optimization and it performs consistently very high prediction rate on different type of data sets, HBE method is an effective and consistent tool for cancer type prediction with a small number of gene markers.

Dataset Information

Gene expression profile analysis of pancreatic cancer based on microarray data.

Publications

Gene expression profile analysis of pancreatic cancer based on microarray data.

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