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Urothelial cancer gene regulatory networks inferred from large-scale RNAseq, Bead and Oligo gene expression data
Ricardo de Matos Simoes1, Sabine Dalleau2, Kate E Williamson3
1Centre for Cancer Research and Cell Biology (CCRCB), Queens University Belfast, 97 Lisburn Road, Belfast, County Antrim, Northern Ireland, UK. r.dematossimoes@qub.ac.uk.
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.
Insights
This study infers genome-wide gene regulatory networks (GRNs) for urothelial cancer (UC) using various gene expression datasets. RNA sequencing data provides the best platform for identifying novel diagnostic targets in bladder tumors.
Area of Science:
- Genomics
- Bioinformatics
- Cancer Research
Background:
- Urothelial pathogenesis involves complex gene networks.
- Identifying novel genomic targets is crucial for understanding urothelial cancer (UC).
- Genome-wide gene regulatory network (GRN) inference aids in investigating UC's molecular underpinnings.
Purpose of the Study:
- To infer and compare three GRNs using different gene expression datasets.
- To investigate the structural and functional properties of these GRNs.
- To identify molecular targets associated with urothelial cancer.
Main Methods:
- Applied the BC3Net algorithm to infer GRNs from Illumina RNAseq, Illumina Bead arrays, and Affymetrix Oligo microarray data.
- Analyzed structural and functional properties of the inferred GRNs.
- Compared the performance of GRNs inferred from different data platforms.
Main Results:
- UC GRNs showed enrichment of subnetworks linked to cancer hallmarks like cell cycle and immune response.
- Prominent subnetworks were identified on specific chromosome regions (5q31.3, 8q24.3, 1q23.3) frequently deregulated in cancer.
- Identified hub genes (e.g., HID1, RNF17, CYP4A11) are known cancer-associated markers.
- RNAseq-based GRNs demonstrated superior performance and a higher proportion of significant functional subnetworks.
Conclusions:
- This is the first study to investigate genome-scale UC GRNs.
- RNAseq data is the preferred platform for GRN inference in UC research.
- The study provides novel avenues for identifying potential diagnostic targets for bladder tumors.
