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

BMC Genomics

Eva Szegezdi³  Luis Serrano¹  Susan Boyce²  Cathal Seoighe⁴  Enda O’Connell⁵  Grainne Gernon³  Csaba Ortutay⁶  Paul O’Reilly³ 

[1] EMBL/CRG Systems Biology Research Unit, Centre for Genomic Regulation (CRG), ICREA Professor, C/ Dr. Aiguader 88, 08003 Barcelona, Spain;School of Medicine, University College Dublin, Dublin 4, Ireland;Apoptosis Research Centre, National University of Ireland Galway, University Rd, Galway, Ireland;School of Mathematics, Statistics and Applied Mathematics, National University of Ireland Galway, University Rd, Galway, Ireland;National Centre for Biomedical Engineering Sciences, National University of Ireland Galway, University Rd, Galway, Ireland;HiDucator Ltd, Erämiehentie 2 E 22, 36200 Kangasala, Finland

关键词: Random forest;    Gene expression;    Biomarker;    TRAIL;   

Others  :  1122709
DOI  :  10.1186/1471-2164-15-1144

 received in 2014-10-14, accepted in 2014-12-11,  发布年份 2014

【 摘 要 】

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.

【 授权许可】

   
2014 O’Reilly et al.; licensee BioMed Central.

【 图 表 】

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