Urothelial cancer gene regulatory networks inferred from large-scale RNAseq, Bead and Oligo gene expression data.

BACKGROUND: Urothelial pathogenesis is a complex process driven by an underlying network of interconnected genes. The identification of novel genomic target regions and gene targets that drive urothelial carcinogenesis is crucial in order to improve our current limited understanding of urothelial cancer (UC) on the molecular level. The inference of genome-wide gene regulatory networks (GRN) from large-scale gene expression data provides a promising approach for a detailed investigation of the underlying network structure associated to urothelial carcinogenesis. METHODS: In our study we inferred and compared three GRNs by the application of the BC3Net inference algorithm to large-scale transitional cell carcinoma gene expression data sets from Illumina RNAseq (179 samples), Illumina Bead arrays (165 samples) and Affymetrix Oligo microarrays (188 samples). We investigated the structural and functional properties of GRNs for the identification of molecular targets associated to urothelial cancer. RESULTS: We found that the urothelial cancer (UC) GRNs show a significant enrichment of subnetworks that are associated with known cancer hallmarks including cell cycle, immune response, signaling, differentiation and translation. Interestingly, the most prominent subnetworks of co-located genes were found on chromosome regions 5q31.3 (RNAseq), 8q24.3 (Oligo) and 1q23.3 (Bead), which all represent known genomic regions frequently deregulated or aberated in urothelial cancer and other cancer types. Furthermore, the identified hub genes of the individual GRNs, e.g., HID1/DMC1 (tumor development), RNF17/TDRD4 (cancer antigen) and CYP4A11 (angiogenesis/ metastasis) are known cancer associated markers. The GRNs were highly dataset specific on the interaction level between individual genes, but showed large similarities on the biological function level represented by subnetworks. Remarkably, the RNAseq UC GRN showed twice the proportion of significant functional subnetworks. Based on our analysis of inferential and experimental networks the Bead UC GRN showed the lowest performance compared to the RNAseq and Oligo UC GRNs. CONCLUSION: To our knowledge, this is the first study investigating genome-scale UC GRNs. RNAseq based gene expression data is the data platform of choice for a GRN inference. Our study offers new avenues for the identification of novel putative diagnostic targets for subsequent studies in bladder tumors.

4-Hydroxy-2(E)-nonenal metabolism differs in Apc(+/+) cells and in Apc(Min/+) cells: it may explain colon cancer promotion by heme iron.

Animal and epidemiological studies suggest that dietary heme iron would promote colorectal cancer. Oxidative properties of heme could lead to the formation of cytotoxic and genotoxic secondary lipid oxidation products, such as 4-hydroxy-2(E)-nonenal (HNE). This compound is more cytotoxic to mouse wild-type colon cells than to isogenic cells with a mutation on the adenomatous polyposis coli (APC) gene. The latter thus have a selective advantage, possibly leading to cancer promotion. This mutation is an early and frequent event in human colorectal cancer. To explain this difference, the HNE biotransformation capacities of the two cell types have been studied using radiolabeled and stable isotope-labeled HNE. Apc-mutated cells showed better biotransformation capacities than nonmutated cells did. Thiol compound conjugation capacities were higher for mutated cells, with an important advantage for the extracellular conjugation to cysteine. Both cells types were able to reduce HNE to 4-hydroxynonanal, a biotransformation pathway that has not been reported for other intestinal cells. Mutated cells showed higher capacities to oxidize 4-hydroxynonanal into 4-hydroxynonanoic acid. The mRNA expression of different enzymes involved in HNE metabolism such as aldehyde dehydrogenase 1A1, 2 and 3A1, glutathione transferase A4-4, or cystine transporter xCT was upregulated in mutated cells compared with wild-type cells. In conclusion, this study suggests that Apc-mutated cells are more efficient than wild-type cells in metabolizing HNE into thiol conjugates and 4-hydroxynonanoic acid due to the higher expression of key biotransformation enzymes. These differential biotransformation capacities would explain the differences of susceptibility between normal and Apc-mutated cells regarding secondary lipid oxidation products.