Dataset Information

Identification of candidate cancer-causing genes in mouse brain tumors by retroviral tagging.

ABSTRACT: Murine retroviruses may cause malignant tumors in mice by insertional mutagenesis of host genes. The use of retroviral tagging as a means of identifying cancer-causing genes has, however, almost entirely been restricted to hematopoietic tumors. The aim of this study was to develop a system allowing for the retroviral tagging of candidate genes in malignant brain tumors. Mouse gliomas were induced by a recombinant Moloney murine leukemia virus encoding platelet-derived growth factor (PDGF) B-chain. The underlying idea was that tumors evolve through a combination of PDGF-mediated autocrine growth stimulation and insertional mutagenesis of genes that cooperate with PDGF in gliomagenesis. Common insertion sites (loci that were tagged in more than one tumor) were identified by cloning and sequencing retroviral flanking segments, followed by blast searches of mouse genome databases. A number of candidate brain tumor loci (Btls) were identified. Several of these Btls correspond to known tumor-causing genes; these findings strongly support the underlying idea of our experimental approach. Other Btls harbor genes with a hitherto unproven role in transformation or oncogenesis. Our findings indicate that retroviral tagging with a growth factor-encoding virus may be a powerful means of identifying candidate tumor-causing genes in nonhematopoietic tumors.

SUBMITTER: Johansson FK

PROVIDER: S-EPMC509203 | biostudies-literature | 2004 Aug

REPOSITORIES: biostudies-literature

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Identification of candidate cancer-causing genes in mouse brain tumors by retroviral tagging.

Johansson Fredrik K FK Brodd Josefin J Eklöf Charlotta C Ferletta Maria M Hesselager Göran G Tiger Carl-Fredrik CF Uhrbom Lene L Westermark Bengt B

Proceedings of the National Academy of Sciences of the United States of America 20040723 31

Murine retroviruses may cause malignant tumors in mice by insertional mutagenesis of host genes. The use of retroviral tagging as a means of identifying cancer-causing genes has, however, almost entirely been restricted to hematopoietic tumors. The aim of this study was to develop a system allowing for the retroviral tagging of candidate genes in malignant brain tumors. Mouse gliomas were induced by a recombinant Moloney murine leukemia virus encoding platelet-derived growth factor (PDGF) B-chai ...[more]

PMID: 15273287

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Project description:Colorectal cancer (CRC) is the third leading cause of cancer-related death and the fourth most commonly diagnosed cancer worldwide. Due to a lack of diagnostic biomarkers and understanding of the underlying molecular mechanisms, CRC's mortality rate continues to grow. CRC occurrence and progression are dynamic processes. The expression levels of specific molecules vary at various stages of CRC, rendering its early detection and diagnosis challenging and the need for identifying accurate and meaningful CRC biomarkers more pressing. The advances in high-throughput sequencing technologies have been used to explore novel gene expression, targeted treatments, and colon cancer pathogenesis. Such approaches are routinely being applied and result in large datasets whose analysis is increasingly becoming dependent on machine learning (ML) algorithms that have been demonstrated to be computationally efficient platforms for the identification of variables across such high-dimensional datasets. We developed a novel ML-based experimental design to study CRC gene associations. Six different machine learning methods were employed as classifiers to identify genes that can be used as diagnostics for CRC using gene expression and clinical datasets. The accuracy, sensitivity, specificity, F1 score, and area under receiver operating characteristic (AUROC) curve were derived to explore the differentially expressed genes (DEGs) for CRC diagnosis. Gene ontology enrichment analyses of these DEGs were performed and predicted gene signatures were linked with miRNAs. We evaluated six machine learning classification methods (Adaboost, ExtraTrees, logistic regression, naïve Bayes classifier, random forest, and XGBoost) across different combinations of training and test datasets over GEO datasets. The accuracy and the AUROC of each combination of training and test data with different algorithms were used as comparison metrics. Random forest (RF) models consistently performed better than other models. In total, 34 genes were identified and used for pathway and gene set enrichment analysis. Further mapping of the 34 genes with miRNA identified interesting miRNA hubs genes. We identified 34 genes with high accuracy that can be used as a diagnostics panel for CRC.

Dataset Information

Identification of candidate cancer-causing genes in mouse brain tumors by retroviral tagging.

Publications

Identification of candidate cancer-causing genes in mouse brain tumors by retroviral tagging.

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