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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

For the top 100 genes ranked by the network-adjusted model, concordance indices for the network-adjusted model (red) and the unadjusted model (blue).
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f5-cin-suppl.1-2015-023: For the top 100 genes ranked by the network-adjusted model, concordance indices for the network-adjusted model (red) and the unadjusted model (blue).

Mentions: We also evaluated the prediction performance of the network-adjusted and unadjusted models using Harrell’s concordance indices. The index is a rank-correlation measure to evaluate predictive accuracy for censored survival outcome and is defined as the proportion of all usable patient pairs in which the predictions and outcomes are concordant.24 Therefore, the larger index value indicates the more accurate model. Figure 5 shows Harrell’s concordance indices of both models for the top 100 genes from the network-adjusted model. For most of the top 100 genes, the indices for the network-adjusted model are larger than those for the unadjusted model. Notably, the SLC30A5 gene shows the greatest increase in the indices after adding the parent genes, AGGF1, GFM2, GMCL1, HEXB, IPO11, MIER3, and NUDT18 (Table 1). We display survival curves for low-expressed and high-expressed groups for the TAF6 gene (the top ranked gene in Table 1) at the median expression levels of its parent genes (Fig. 6A).


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

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

For the top 100 genes ranked by the network-adjusted model, concordance indices for the network-adjusted model (red) and the unadjusted model (blue).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f5-cin-suppl.1-2015-023: For the top 100 genes ranked by the network-adjusted model, concordance indices for the network-adjusted model (red) and the unadjusted model (blue).
Mentions: We also evaluated the prediction performance of the network-adjusted and unadjusted models using Harrell’s concordance indices. The index is a rank-correlation measure to evaluate predictive accuracy for censored survival outcome and is defined as the proportion of all usable patient pairs in which the predictions and outcomes are concordant.24 Therefore, the larger index value indicates the more accurate model. Figure 5 shows Harrell’s concordance indices of both models for the top 100 genes from the network-adjusted model. For most of the top 100 genes, the indices for the network-adjusted model are larger than those for the unadjusted model. Notably, the SLC30A5 gene shows the greatest increase in the indices after adding the parent genes, AGGF1, GFM2, GMCL1, HEXB, IPO11, MIER3, and NUDT18 (Table 1). We display survival curves for low-expressed and high-expressed groups for the TAF6 gene (the top ranked gene in Table 1) at the median expression levels of its parent genes (Fig. 6A).

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