Diagnosis of childhood and adolescent growth hormone deficiency using transcriptomic data.
Diagnosis of childhood and adolescent growth hormone deficiency using transcriptomic data.
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DOI：
10.3389/fendo.2023.1026187
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发表时间：
2023
影响因子：
5.2
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中科院分区：
文献类型：
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基因表达（GE）数据显示出作为一种新的工具，以帮助诊断儿童生长激素缺乏症（GHD）时，比较GHD儿童正常儿童的承诺。本研究的目的是以非GHD身材矮小儿童作为对照组，评估GE数据在儿童和青少年GHD诊断中的实用性。GE数据来自接受生长激素刺激试验的患者。我们收集了271个基因的数据，这些基因的表达在我们以前的研究中被利用。使用合成少数过采样技术来平衡数据集，并应用随机森林算法来预测GHD状态。24名患者被招募到研究中，8名随后被诊断为GHD。GHD和非GHD受试者之间的性别、年龄、生长学（身高SDS、体重SDS、BMI SDS）或生化（IGF-I SDS、IGFBP-3 SDS）无显著差异。随机森林算法得出GHD诊断的AUC为0.97（95% CI 0.93 - 1.0）。这项研究表明，使用GE数据和随机森林分析相结合的儿童GHD的诊断非常准确。
Gene expression (GE) data have shown promise as a novel tool to aid in the diagnosis of childhood growth hormone deficiency (GHD) when comparing GHD children to normal children. The aim of this study was to assess the utility of GE data in the diagnosis of GHD in childhood and adolescence using non-GHD short stature children as a control group. GE data was obtained from patients undergoing growth hormone stimulation testing. Data were taken for the 271 genes whose expression was utilized in our previous study. The synthetic minority oversampling technique was used to balance the dataset and a random forest algorithm applied to predict GHD status. 24 patients were recruited to the study and eight subsequently diagnosed with GHD. There were no significant differences in gender, age, auxology (height SDS, weight SDS, BMI SDS) or biochemistry (IGF-I SDS, IGFBP-3 SDS) between the GHD and non-GHD subjects. A random forest algorithm gave an AUC of 0.97 (95% CI 0.93 – 1.0) for the diagnosis of GHD. This study demonstrates highly accurate diagnosis of childhood GHD using a combination of GE data and random forest analysis.