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Obesity is associated with increased risk for developing pancreatic cancer, and it is suggested that insulin resistance provides the missing link. Here we demonstrate that under the context of genetic susceptibility, a high fat diet (HFD) predisposes mice with oncogenic K-ras activation to accelerated pancreatic intraepithelial neoplasm (PanIN) development. Tumor promotion is closely associated with increased inflammation and abrogation of TNFR1 signaling significantly blocks this process underlining a central role for TNF[alpha] in obesity-mediated enhancement of PanIN lesions. Interestingly, however, despite increased TNF[alpha] levels, mice remain insulin sensitive. We show that, while aggravating tumor promotion, a HFD exerts dramatic changes in energy metabolism through enhancement of pancreatic exocrine insufficiency, metabolic rates, and expression of genes involved in mitochondrial fatty acid (FA) [beta]-oxidation that collectively contribute to improved glucose tolerance in these mice. While on one hand these findings provide significant evidence that obesity is linked to tumor promotion in the pancreas, on the other it suggests alterations in inflammatory responses and bioenergetic pathways as the potential underlying cause. energy metabolism | cancer | TNF[alpha] | mitochondria | pancreas

A major advantage of the mouse model lies in the increasing information on its genome, transcriptome, and proteome, as well as in the availability of a fast growing number of targeted and induced mutant alleles. However, data from comparative transcriptome and proteome analyses in this model organism are very limited. We use DNA chip-based RNA expression profiling and 2D gel electrophoresis, combined with peptide mass fingerprinting of liver and kidney, to explore the feasibility of such comprehensive gene expression analyses. Although protein analyses mostly identify known metabolic enzymes and structural proteins, transcriptome analyses reveal the differential expression of functionally diverse and not yet described genes. The comparative analysis suggests correlation between transcriptional and translational expression for the majority of genes. Significant exceptions from this correlation confirm the complementarities of both approaches. Based on RNA expression data from the 200 most differentially expressed genes, we identify chromosomal colocalization of known, as well as not yet described, gene clusters. The determination of 29 such clusters may suggest that coexpression of colocalizing genes is probably rather common. coexpression and colocalization | comparative expression profiles