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Prognostic gene signature identification using causal structure learning: applications in kidney cancer.

Ha MJ, Baladandayuthapani V, Do KA - Cancer Inform (2015)

Bottom Line: The causal structures are represented by directed acyclic graphs (DAGs), wherein we construct gene-specific network modules that constitute a gene and its corresponding regulators.The modules are then subsequently used to correlate with survival times, thus, allowing for a network-oriented approach to gene selection to adjust for potential confounders, as opposed to univariate (gene-by-gene) approaches.Our methods are motivated by and applied to a clear cell renal cell carcinoma (ccRCC) study from The Cancer Genome Atlas (TCGA) where we find several prognostic genes associated with cancer progression - some of which are novel while others confirm existing findings.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA.

ABSTRACT
Identification of molecular-based signatures is one of the critical steps toward finding therapeutic targets in cancer. In this paper, we propose methods to discover prognostic gene signatures under a causal structure learning framework across the whole genome. The causal structures are represented by directed acyclic graphs (DAGs), wherein we construct gene-specific network modules that constitute a gene and its corresponding regulators. The modules are then subsequently used to correlate with survival times, thus, allowing for a network-oriented approach to gene selection to adjust for potential confounders, as opposed to univariate (gene-by-gene) approaches. Our methods are motivated by and applied to a clear cell renal cell carcinoma (ccRCC) study from The Cancer Genome Atlas (TCGA) where we find several prognostic genes associated with cancer progression - some of which are novel while others confirm existing findings.

No MeSH data available.


Related in: MedlinePlus

A modified PC algorithm starting from a GGM.
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4362630&req=5

f2-cin-suppl.1-2015-023: A modified PC algorithm starting from a GGM.

Mentions: Our algorithm performs the partial correlation tests from the first order, l = 1, until l exceeds the maximum size of the neighborhoods in the current graph. We denote ne(i, G) as the set of neighbors for i ∈ V in an undirected graph G. Our algorithm is summarized in detail in Figure 2. It starts from the first-order partial correlation tests because we already tested marginal correlations to estimate the GGM. For a fixed order, l, each edge is tested by partial correlations given the subsets in the neighborhood for either vertex that forms the edge. We changed the order-independent version of the PC algorithm in Colombo et al.20 to an algorithm that can operate in parallel with the vertices and the order l. The main difference between our algorithm and the PC algorithm is in the calculation of the separation sets. While the PC algorithm stops testing an edge when a separation set is obtained, our algorithm exhaustively searches all vertices that participate in any of the separation sets (Step 2.2.1 of Fig. 2). The exhaustive search provides a more accurate estimation of the v-structures.


Prognostic gene signature identification using causal structure learning: applications in kidney cancer.

Ha MJ, Baladandayuthapani V, Do KA - Cancer Inform (2015)

A modified PC algorithm starting from a GGM.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4362630&req=5

f2-cin-suppl.1-2015-023: A modified PC algorithm starting from a GGM.
Mentions: Our algorithm performs the partial correlation tests from the first order, l = 1, until l exceeds the maximum size of the neighborhoods in the current graph. We denote ne(i, G) as the set of neighbors for i ∈ V in an undirected graph G. Our algorithm is summarized in detail in Figure 2. It starts from the first-order partial correlation tests because we already tested marginal correlations to estimate the GGM. For a fixed order, l, each edge is tested by partial correlations given the subsets in the neighborhood for either vertex that forms the edge. We changed the order-independent version of the PC algorithm in Colombo et al.20 to an algorithm that can operate in parallel with the vertices and the order l. The main difference between our algorithm and the PC algorithm is in the calculation of the separation sets. While the PC algorithm stops testing an edge when a separation set is obtained, our algorithm exhaustively searches all vertices that participate in any of the separation sets (Step 2.2.1 of Fig. 2). The exhaustive search provides a more accurate estimation of the v-structures.

Bottom Line: The causal structures are represented by directed acyclic graphs (DAGs), wherein we construct gene-specific network modules that constitute a gene and its corresponding regulators.The modules are then subsequently used to correlate with survival times, thus, allowing for a network-oriented approach to gene selection to adjust for potential confounders, as opposed to univariate (gene-by-gene) approaches.Our methods are motivated by and applied to a clear cell renal cell carcinoma (ccRCC) study from The Cancer Genome Atlas (TCGA) where we find several prognostic genes associated with cancer progression - some of which are novel while others confirm existing findings.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics, The University of Texas, MD Anderson Cancer Center, Houston, TX, USA.

ABSTRACT
Identification of molecular-based signatures is one of the critical steps toward finding therapeutic targets in cancer. In this paper, we propose methods to discover prognostic gene signatures under a causal structure learning framework across the whole genome. The causal structures are represented by directed acyclic graphs (DAGs), wherein we construct gene-specific network modules that constitute a gene and its corresponding regulators. The modules are then subsequently used to correlate with survival times, thus, allowing for a network-oriented approach to gene selection to adjust for potential confounders, as opposed to univariate (gene-by-gene) approaches. Our methods are motivated by and applied to a clear cell renal cell carcinoma (ccRCC) study from The Cancer Genome Atlas (TCGA) where we find several prognostic genes associated with cancer progression - some of which are novel while others confirm existing findings.

No MeSH data available.


Related in: MedlinePlus