Volume : VII, Issue : XI, November - 2017

DYNAMIC CANCER PATHWAY INTERACTION ANALYSIS USING PATHWAY RANKINGS BASED ON STATIC DATASETS

Shinuk Kim

Abstract :

 Background: Dynamic pathway interaction analysis provides useful information in assessing progression of complex diseases at different pathologic stages and/or time points. However, high–throughput datasets are obtained statically rather than dynamically, making it difficult to assess dynamic changes occurring over the course of disease progression. Here, we report a simple method based on survival times for discovering dynamic pathway interactions using static cancer datasets such as The Cancer Genome Atlas (TCGA). Gene Set Enrichment Analysis was used to rank gene sets or pathways whose outcomes represent differentially expressed leading edge gene scores between tumor and normal samples. Results: We tested three different cases ordered by survival time and found eight common pairs of positively– or negatively–related pathways. The two most positively correlated pathways were DNA replication and Mismatch repair, whereas the two most inversely correlated pathways were Spliceosome and GRAFT–VERSUS–HOST DISEASE. Conclusions: Our simple method for assessing dynamic pathway interactions will potentially enable the discovery of dynamic pathway networks involved in the pathologic progression of complex diseases.

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Article: Download PDF   DOI : 10.36106/ijar  

Cite This Article:

SHINUK KIM, DYNAMIC CANCER PATHWAY INTERACTION ANALYSIS USING PATHWAY RANKINGS BASED ON STATIC DATASETS, INDIAN JOURNAL OF APPLIED RESEARCH : Volume-7 | Issue-11 | November-2017

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