Systems biology approach to identification of biomarkers for metastatic progression in cancer.
BACKGROUND
Metastases are responsible for the majority of cancer fatalities. The molecular mechanisms governing metastasis are poorly understood, hindering early diagnosis and treatment. Previous studies of gene expression patterns in metastasis have concentrated on selection of a small number of "signature" biomarkers.


RESULTS
We propose an alternative approach that puts into focus gene interaction networks and molecular pathways rather than separate genes. We have reanalyzed expression data from a large set of primary solid and metastatic tumors originating from different tissues using the latest available tools for normalization, identification of differentially expressed genes and pathway analysis. Our studies indicate that regardless of the tissue of origin, all metastatic tumors share a number of common features related to changes in basic energy metabolism, cell adhesion/cytoskeleton remodeling, antigen presentation and cell cycle regulation. Analysis of multiple independent datasets indicates significantly reduced oxidative phosphorylation in metastases compared to primary solid tumors.


CONCLUSIONS
Our methods allow identification of robust, although not necessarily highly expressed biomarkers. A systems approach relying on groups of interacting genes rather than single markers is also essential for understanding the cellular processes leading to metastatic progression. We have identified metabolic pathways associated with metastasis that may serve as novel targets for therapeutic intervention.
Ptitsyn
Andrey A
AA
Center for Bioinformatics, Department of Microbiology, Immunology and Pathology, Colorado State University, Colorado, USA. Andrey.Ptitsyn@colostate.edu
Weil
Michael M
MM
Thamm
Douglas H
DH
D000465:Algorithms; D014408:Biomarkers, Tumor / Q000032:analysis / Q000378:metabolism*; D003198:Computer Simulation; D018450:Disease Progression; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D008954:Models, Biological; D009362:Neoplasm Metastasis / Q000503:physiopathology*; D009363:Neoplasm Proteins / Q000032:analysis / Q000378:metabolism*; D010363:Pattern Recognition, Automated / Q000379:methods; D015398:Signal Transduction; D049490:Systems Biology / Q000379:methods*
D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins
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