Dataset Information

Analysis of promoters and expression-targeted gene therapy optimization based on doubling time and transfectability.

ABSTRACT: Genes under the control of the cyclooxygenase-2 (Cox-2), human epidermal growth factor receptor 2 (Her-2), and survivin promoters were constructed and delivered to murine and human carcinoma cells. It was found that (P)Cox-2-driven reporter expression was strong and correlated well with endogenous Cox-2 levels, while (P)Her-2 and (P)survivin yielded poor results, consistent with the three distinct expression mechanisms used by cancer cells to overexpress the endogenous versions of the selected genes. The (P)Cox-2 was then used to drive the expression of caspase genes both in vitro and in vivo to bring about targeted apoptosis of carcinoma cells successfully. The results led to the following conclusions. 1) When selecting a promoter/enhancer for expression-targeted gene delivery, it is not enough to perform a microarray on some tumor tissue and select the control element associated with the greatest amount of gene up-regulation vs. normal controls. The mechanism of expression for the particular gene should be taken into account to prevent lengthy and costly research trials. 2) When overexpression is due to activator binding, a predictive model based on endogenous gene expression levels, overall cell transfectability, and cell doubling rates can be used to predict expression-targeted gene delivery outcomes with significant accuracy.

SUBMITTER: Dobek GL

PROVIDER: S-EPMC6188461 | biostudies-literature | 2011 Sep

REPOSITORIES: biostudies-literature

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Analysis of promoters and expression-targeted gene therapy optimization based on doubling time and transfectability.

Dobek Georgina L GL Zhang Xiujuan X Balazs Daniel A DA Godbey W T WT

FASEB journal : official publication of the Federation of American Societies for Experimental Biology 20110520 9

Genes under the control of the cyclooxygenase-2 (Cox-2), human epidermal growth factor receptor 2 (Her-2), and survivin promoters were constructed and delivered to murine and human carcinoma cells. It was found that (P)Cox-2-driven reporter expression was strong and correlated well with endogenous Cox-2 levels, while (P)Her-2 and (P)survivin yielded poor results, consistent with the three distinct expression mechanisms used by cancer cells to overexpress the endogenous versions of the selected g ...[more]

PMID: 21602450

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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

Analysis of promoters and expression-targeted gene therapy optimization based on doubling time and transfectability.

Publications

Analysis of promoters and expression-targeted gene therapy optimization based on doubling time and transfectability.

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