Understanding Disease States Through Gene Regulatory Networks
Author Information
Author(s): Tuck David P, Kluger Harriet M, Kluger Yuval
Primary Institution: Yale University School of Medicine
Hypothesis
Each individual sample from a cohort of cancer patients has a distinct regulatory network that can be used for separating conditions.
Conclusion
Transcription factor-regulated gene links and centrality of genes on transcriptional networks can be used to differentiate between cell types.
Supporting Evidence
- Samples can often be separated based on measures of gene centrality more robustly than using individual links.
- Genes that optimally classify samples are concentrated in neighborhoods in the gene regulatory networks.
- The classification performance based on links was effective with estimated error rates of 9% and 6% for leukemia datasets.
Takeaway
Scientists studied how genes work together in cancer to find out what makes sick cells different from healthy ones.
Methodology
Constructed regulatory networks by combining TF binding data with gene expression data from various cancer studies.
Potential Biases
Potential noise and information loss due to inferred links in the connectivity network.
Limitations
The connectivity network does not include high throughput binding data, which remains limited for human cells.
Participant Demographics
Included patients with different types of acute leukemia and breast cancer.
Statistical Information
P-Value
p<0.01
Statistical Significance
p<0.01
Digital Object Identifier (DOI)
Want to read the original?
Access the complete publication on the publisher's website