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Inferring Aberrant Signal Transduction Pathways in Ovarian Cancer from TCGA Data.

Neapolitan R, Jiang X - Cancer Inform (2014)

Bottom Line: We identified all the genes related to each of the 26 pathways, and developed separate gene expression datasets for each pathway.The results of the two methods were highly correlated.Finally, CASA ranked the cancer-related STPs over the randomly selected STPs at a significance level below 0.05 (P = 0.047), but SPIA did not (P = 0.083).

View Article: PubMed Central - PubMed

Affiliation: Department of Preventive Medicine, Northwestern University, Feinberg School of Medicine, Chicago, IL, USA.

ABSTRACT
This paper concerns a new method for identifying aberrant signal transduction pathways (STPs) in cancer using case/control gene expression-level datasets, and applying that method and an existing method to an ovarian carcinoma dataset. Both methods identify STPs that are plausibly linked to all cancers based on current knowledge. Thus, the paper is most appropriate for the cancer informatics community. Our hypothesis is that STPs that are altered in tumorous tissue can be identified by applying a new Bayesian network (BN)-based method (causal analysis of STP aberration (CASA)) and an existing method (signaling pathway impact analysis (SPIA)) to the cancer genome atlas (TCGA) gene expression-level datasets. To test this hypothesis, we analyzed 20 cancer-related STPs and 6 randomly chosen STPs using the 591 cases in the TCGA ovarian carcinoma dataset, and the 102 controls in all 5 TCGA cancer datasets. We identified all the genes related to each of the 26 pathways, and developed separate gene expression datasets for each pathway. The results of the two methods were highly correlated. Furthermore, many of the STPs that ranked highest according to both methods are plausibly linked to all cancers based on current knowledge. Finally, CASA ranked the cancer-related STPs over the randomly selected STPs at a significance level below 0.05 (P = 0.047), but SPIA did not (P = 0.083).

No MeSH data available.


Related in: MedlinePlus

A BN containing a subset of the variables related to lung cancer.
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4216062&req=5

f2-cin-suppl.1-2014-029: A BN containing a subset of the variables related to lung cancer.

Mentions: Figure 2 shows a BN representing the causal relationships among a subset of the variables related to lung cancer. Using this BN, we can determine conditional probabilities of interest using a BN inference algorithm.19 For example, we can determine P(L = yes/H = yes, X = yes, T = no).


Inferring Aberrant Signal Transduction Pathways in Ovarian Cancer from TCGA Data.

Neapolitan R, Jiang X - Cancer Inform (2014)

A BN containing a subset of the variables related to lung cancer.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4216062&req=5

f2-cin-suppl.1-2014-029: A BN containing a subset of the variables related to lung cancer.
Mentions: Figure 2 shows a BN representing the causal relationships among a subset of the variables related to lung cancer. Using this BN, we can determine conditional probabilities of interest using a BN inference algorithm.19 For example, we can determine P(L = yes/H = yes, X = yes, T = no).

Bottom Line: We identified all the genes related to each of the 26 pathways, and developed separate gene expression datasets for each pathway.The results of the two methods were highly correlated.Finally, CASA ranked the cancer-related STPs over the randomly selected STPs at a significance level below 0.05 (P = 0.047), but SPIA did not (P = 0.083).

View Article: PubMed Central - PubMed

Affiliation: Department of Preventive Medicine, Northwestern University, Feinberg School of Medicine, Chicago, IL, USA.

ABSTRACT
This paper concerns a new method for identifying aberrant signal transduction pathways (STPs) in cancer using case/control gene expression-level datasets, and applying that method and an existing method to an ovarian carcinoma dataset. Both methods identify STPs that are plausibly linked to all cancers based on current knowledge. Thus, the paper is most appropriate for the cancer informatics community. Our hypothesis is that STPs that are altered in tumorous tissue can be identified by applying a new Bayesian network (BN)-based method (causal analysis of STP aberration (CASA)) and an existing method (signaling pathway impact analysis (SPIA)) to the cancer genome atlas (TCGA) gene expression-level datasets. To test this hypothesis, we analyzed 20 cancer-related STPs and 6 randomly chosen STPs using the 591 cases in the TCGA ovarian carcinoma dataset, and the 102 controls in all 5 TCGA cancer datasets. We identified all the genes related to each of the 26 pathways, and developed separate gene expression datasets for each pathway. The results of the two methods were highly correlated. Furthermore, many of the STPs that ranked highest according to both methods are plausibly linked to all cancers based on current knowledge. Finally, CASA ranked the cancer-related STPs over the randomly selected STPs at a significance level below 0.05 (P = 0.047), but SPIA did not (P = 0.083).

No MeSH data available.


Related in: MedlinePlus