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Comprehensive analysis of pathway or functionally related gene expression in the National Cancer Institute's
Ruili Huang1, Anders Wallqvist, David G Covell
1Laboratory of Computational Technologies, Developmental Therapeutics Program, Screening Technologies Branch, National Cancer Institute at Frederick, National Institutes of Health, Frederick, MD 21702, USA.
Abstract:
We have analyzed the level of gene coregulation, using gene expression patterns measured across the National Cancer Institute's 60 tumor cell panels (NCI(60)), in the context of predefined pathways or functional categories annotated by KEGG (Kyoto Encyclopedia of Genes and Genomes), BioCarta, and GO (Gene Ontology). Statistical methods were used to evaluate the level of gene expression coherence (coordinated expression) by comparing intra- and interpathway gene-gene correlations. Our results show that gene expression in pathways, or groups of functionally related genes, has a significantly higher level of coherence than that of a randomly selected set of genes. Transcriptional-level gene regulation appears to be on a "need to be" basis, such that pathways comprising genes encoding closely interacting proteins and pathways responsible for vital cellular processes or processes that are related to growth or proliferation, specifically in cancer cells, such as those engaged in genetic information processing, cell cycle, energy metabolism, and nucleotide metabolism, tend to be more modular (lower degree of gene sharing) and to have genes significantly more coherently expressed than most signaling and regular metabolic pathways. Hierarchical clustering of pathways based on their differential gene expression in the NCI(60) further revealed interesting interpathway communications or interactions indicative of a higher level of pathway regulation. The knowledge of the nature of gene expression regulation and biological pathways can be applied to understanding the mechanism by which small drug molecules interfere with biological systems.
Insights
Gene expression within biological pathways is more coordinated than random gene sets. Pathways crucial for cell growth and vital processes exhibit higher gene expression coherence, offering insights into drug interactions.
Area of Science:
- Genomics
- Systems Biology
- Bioinformatics
Background:
- Understanding gene coregulation is crucial for deciphering complex biological processes.
- Gene expression patterns provide insights into cellular functions and disease mechanisms.
Purpose of the Study:
- To analyze gene coregulation using gene expression data from the National Cancer Institute's 60 tumor cell panels (NCI-60).
- To evaluate gene expression coherence within predefined biological pathways (KEGG, BioCarta, GO).
Main Methods:
- Utilized statistical methods to compare intra- and interpathway gene-gene correlations.
- Applied hierarchical clustering to analyze differential gene expression across pathways in the NCI-60 dataset.
Main Results:
- Gene expression within biological pathways shows significantly higher coherence than random gene sets.
- Pathways involved in vital cellular processes (e.g., cell cycle, metabolism) and cancer-related growth/proliferation exhibit greater coherence and modularity.
- Identified interpathway communications indicative of higher-level pathway regulation.
Conclusions:
- Gene expression regulation operates on a 'need-to-be' basis, with essential pathways being more coherently expressed.
- Findings enhance understanding of biological pathway regulation and gene sharing.
- This knowledge can inform the study of how small drug molecules interact with biological systems.
