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Co-acting gene networks predict TRAIL responsiveness of tumour cells with high accuracy.

O'Reilly P, Ortutay C, Gernon G, O'Connell E, Seoighe C, Boyce S, Serrano L, Szegezdi E - BMC Genomics (2014)

Bottom Line: Importantly, only 12% of the TRAIL-predictor genes were differentially expressed highlighting the importance of functional interactions in predicting the biological response.The advantage of co-acting gene clusters is that this analysis does not depend on differential expression and is able to incorporate direct- and indirect gene interactions as well as tissue- and cell-specific characteristics.This approach (1) identified a descriptor of TRAIL sensitivity which performs significantly better as a predictor of TRAIL sensitivity than any previously reported gene signatures, (2) identified potential novel regulators of TRAIL-responsiveness and (3) provided a systematic view highlighting fundamental differences between the molecular wiring of sensitive and resistant cell types.

View Article: PubMed Central - PubMed

Affiliation: Apoptosis Research Centre, National University of Ireland Galway, University Rd, Galway, Ireland. eva.szegezdi@nuigalway.ie.

ABSTRACT

Background: Identification of differentially expressed genes from transcriptomic studies is one of the most common mechanisms to identify tumor biomarkers. This approach however is not well suited to identify interaction between genes whose protein products potentially influence each other, which limits its power to identify molecular wiring of tumour cells dictating response to a drug. Due to the fact that signal transduction pathways are not linear and highly interlinked, the biological response they drive may be better described by the relative amount of their components and their functional relationships than by their individual, absolute expression.

Results: Gene expression microarray data for 109 tumor cell lines with known sensitivity to the death ligand cytokine tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) was used to identify genes with potential functional relationships determining responsiveness to TRAIL-induced apoptosis. The machine learning technique Random Forest in the statistical environment "R" with backward elimination was used to identify the key predictors of TRAIL sensitivity and differentially expressed genes were identified using the software GeneSpring. Gene co-regulation and statistical interaction was assessed with q-order partial correlation analysis and non-rejection rate. Biological (functional) interactions amongst the co-acting genes were studied with Ingenuity network analysis. Prediction accuracy was assessed by calculating the area under the receiver operator curve using an independent dataset. We show that the gene panel identified could predict TRAIL-sensitivity with a very high degree of sensitivity and specificity (AUC=0·84). The genes in the panel are co-regulated and at least 40% of them functionally interact in signal transduction pathways that regulate cell death and cell survival, cellular differentiation and morphogenesis. Importantly, only 12% of the TRAIL-predictor genes were differentially expressed highlighting the importance of functional interactions in predicting the biological response.

Conclusions: The advantage of co-acting gene clusters is that this analysis does not depend on differential expression and is able to incorporate direct- and indirect gene interactions as well as tissue- and cell-specific characteristics. This approach (1) identified a descriptor of TRAIL sensitivity which performs significantly better as a predictor of TRAIL sensitivity than any previously reported gene signatures, (2) identified potential novel regulators of TRAIL-responsiveness and (3) provided a systematic view highlighting fundamental differences between the molecular wiring of sensitive and resistant cell types.

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Related in: MedlinePlus

The genes determining TRAIL-response-tend to be upstream regulators of components of cell death and cell survival signal transduction pathways. The figure shows direct (solid lines) and indirect interactions (dashed lines) amongst components of the cell death and survival signal transduction pathways. Genes from the 350 gene panel are coloured grey. Lines with arrowheads indicate functional interaction, such as regulation of expression or activity, while lines without arrowheads indicate protein-protein interactions.
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Fig5: The genes determining TRAIL-response-tend to be upstream regulators of components of cell death and cell survival signal transduction pathways. The figure shows direct (solid lines) and indirect interactions (dashed lines) amongst components of the cell death and survival signal transduction pathways. Genes from the 350 gene panel are coloured grey. Lines with arrowheads indicate functional interaction, such as regulation of expression or activity, while lines without arrowheads indicate protein-protein interactions.

Mentions: Analysis of biological interactions showed that over 40% of the genes (141 in total) were proven or predicted components of three interconnected signaling networks controlling (1) cell death and pro-survival signal transduction (65 genes, Figure 5), (2) cellular differentiation and morphogenesis (45 genes, Additional file 3: Figure S2), and (3) cancer related signaling pathways (31 genes, Additional file 4: Figure S3). The remaining genes could not be grouped into signaling networks based on the current literature about them in the searched databases. These networks confirm that interactions between the 350 genes exist at the biological level. Notably, in the network of cell death and pro-survival signaling pathways, the majority of the 350 genes are upstream regulators of protein kinases and transcription factors, such as NF-κB (nuclear factor-kappa B), PI3K (phosphatidylinositol 3-kinase), JNK (c-Jun N-terminal kinase), and ERK (extracellular signal-regulated kinase) (Figure 5).Figure 5


Co-acting gene networks predict TRAIL responsiveness of tumour cells with high accuracy.

O'Reilly P, Ortutay C, Gernon G, O'Connell E, Seoighe C, Boyce S, Serrano L, Szegezdi E - BMC Genomics (2014)

The genes determining TRAIL-response-tend to be upstream regulators of components of cell death and cell survival signal transduction pathways. The figure shows direct (solid lines) and indirect interactions (dashed lines) amongst components of the cell death and survival signal transduction pathways. Genes from the 350 gene panel are coloured grey. Lines with arrowheads indicate functional interaction, such as regulation of expression or activity, while lines without arrowheads indicate protein-protein interactions.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4378270&req=5

Fig5: The genes determining TRAIL-response-tend to be upstream regulators of components of cell death and cell survival signal transduction pathways. The figure shows direct (solid lines) and indirect interactions (dashed lines) amongst components of the cell death and survival signal transduction pathways. Genes from the 350 gene panel are coloured grey. Lines with arrowheads indicate functional interaction, such as regulation of expression or activity, while lines without arrowheads indicate protein-protein interactions.
Mentions: Analysis of biological interactions showed that over 40% of the genes (141 in total) were proven or predicted components of three interconnected signaling networks controlling (1) cell death and pro-survival signal transduction (65 genes, Figure 5), (2) cellular differentiation and morphogenesis (45 genes, Additional file 3: Figure S2), and (3) cancer related signaling pathways (31 genes, Additional file 4: Figure S3). The remaining genes could not be grouped into signaling networks based on the current literature about them in the searched databases. These networks confirm that interactions between the 350 genes exist at the biological level. Notably, in the network of cell death and pro-survival signaling pathways, the majority of the 350 genes are upstream regulators of protein kinases and transcription factors, such as NF-κB (nuclear factor-kappa B), PI3K (phosphatidylinositol 3-kinase), JNK (c-Jun N-terminal kinase), and ERK (extracellular signal-regulated kinase) (Figure 5).Figure 5

Bottom Line: Importantly, only 12% of the TRAIL-predictor genes were differentially expressed highlighting the importance of functional interactions in predicting the biological response.The advantage of co-acting gene clusters is that this analysis does not depend on differential expression and is able to incorporate direct- and indirect gene interactions as well as tissue- and cell-specific characteristics.This approach (1) identified a descriptor of TRAIL sensitivity which performs significantly better as a predictor of TRAIL sensitivity than any previously reported gene signatures, (2) identified potential novel regulators of TRAIL-responsiveness and (3) provided a systematic view highlighting fundamental differences between the molecular wiring of sensitive and resistant cell types.

View Article: PubMed Central - PubMed

Affiliation: Apoptosis Research Centre, National University of Ireland Galway, University Rd, Galway, Ireland. eva.szegezdi@nuigalway.ie.

ABSTRACT

Background: Identification of differentially expressed genes from transcriptomic studies is one of the most common mechanisms to identify tumor biomarkers. This approach however is not well suited to identify interaction between genes whose protein products potentially influence each other, which limits its power to identify molecular wiring of tumour cells dictating response to a drug. Due to the fact that signal transduction pathways are not linear and highly interlinked, the biological response they drive may be better described by the relative amount of their components and their functional relationships than by their individual, absolute expression.

Results: Gene expression microarray data for 109 tumor cell lines with known sensitivity to the death ligand cytokine tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) was used to identify genes with potential functional relationships determining responsiveness to TRAIL-induced apoptosis. The machine learning technique Random Forest in the statistical environment "R" with backward elimination was used to identify the key predictors of TRAIL sensitivity and differentially expressed genes were identified using the software GeneSpring. Gene co-regulation and statistical interaction was assessed with q-order partial correlation analysis and non-rejection rate. Biological (functional) interactions amongst the co-acting genes were studied with Ingenuity network analysis. Prediction accuracy was assessed by calculating the area under the receiver operator curve using an independent dataset. We show that the gene panel identified could predict TRAIL-sensitivity with a very high degree of sensitivity and specificity (AUC=0·84). The genes in the panel are co-regulated and at least 40% of them functionally interact in signal transduction pathways that regulate cell death and cell survival, cellular differentiation and morphogenesis. Importantly, only 12% of the TRAIL-predictor genes were differentially expressed highlighting the importance of functional interactions in predicting the biological response.

Conclusions: The advantage of co-acting gene clusters is that this analysis does not depend on differential expression and is able to incorporate direct- and indirect gene interactions as well as tissue- and cell-specific characteristics. This approach (1) identified a descriptor of TRAIL sensitivity which performs significantly better as a predictor of TRAIL sensitivity than any previously reported gene signatures, (2) identified potential novel regulators of TRAIL-responsiveness and (3) provided a systematic view highlighting fundamental differences between the molecular wiring of sensitive and resistant cell types.

Show MeSH
Related in: MedlinePlus