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Transcriptomics and machine learning predict diagnosis and severity of growth hormone deficiency
Philip G. Murray, … , Pierre Chatelain, Peter E. Clayton
Philip G. Murray, … , Pierre Chatelain, Peter E. Clayton
Published April 5, 2018
Citation Information: JCI Insight. 2018;3(7):e93247. https://doi.org/10.1172/jci.insight.93247.
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Clinical Medicine Endocrinology

Transcriptomics and machine learning predict diagnosis and severity of growth hormone deficiency

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Abstract

BACKGROUND. The effect of gene expression data on diagnosis remains limited. Here, we show how diagnosis and classification of growth hormone deficiency (GHD) can be achieved from a single blood sample using a combination of transcriptomics and random forest analysis. METHODS. Prepubertal treatment-naive children with GHD (n = 98) were enrolled from the PREDICT study, and controls (n = 26) were acquired from online data sets. Whole blood gene expression was correlated with peak growth hormone (GH) using rank regression and a random forest algorithm tested for prediction of the presence of GHD and in classification of GHD as severe (peak GH <4 μg/l) and nonsevere (peak ≥4 μg/l). Performance was assessed using area under the receiver operating characteristic curve (AUC-ROC). RESULTS. Rank regression identified 347 probe sets in which gene expression correlated with peak GH concentrations (r = ± 0.28, P < 0.01). These 347 probe sets yielded an AUC-ROC of 0.95 for prediction of GHD status versus controls and an AUC-ROC of 0.93 for prediction of GHD severity. CONCLUSION. This study demonstrates highly accurate diagnosis and disease classification for GHD using a combination of transcriptomics and random forest analysis. TRIAL REGISTRATION. NCT00256126 and NCT00699855. FUNDING. Merck and the National Institute for Health Research (CL-2012-06-005).

Authors

Philip G. Murray, Adam Stevens, Chiara De Leonibus, Ekaterina Koledova, Pierre Chatelain, Peter E. Clayton

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Figure 3

Network modeling of the overlap of gene expression between clinical markers.

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Network modeling of the overlap of gene expression between clinical mark...
(A) Network models generated using BioGRID (version 3.2.117) were analyzed to define modules of functionally related genes. The “community structure” of these modules was assessed and ranked by their “centrality” score to form a hierarchy related to the biological action of the network. (B) Community structure of modules within the network was assessed using the ModuLand algorithm in Cytoscape 2.8.3. Hierarchy of the first 15 network modules in each of the network models of gene expression overlap between clinical markers. Modules are shown as octagons labeled with the most central gene in the cluster and ranked by network centrality (1st through 15th).

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