METHODS: This study applied logistic regression (LR) model to the blood miRNA expression profile to differentiate patients with MDD (n = 60) from healthy controls (HCs, n = 60). Embedded (L1-regularised logistic regression) feature selector was utilised to extract clinically relevant miRNAs, and optimized for clinical application.
RESULTS: Patients with MDD could be differentiated from HCs with the area under the receiver operating characteristic curve (AUC) of 0.81 on testing data when all available miRNAs were considered (which served as a benchmark). Our LR model selected miRNAs up to 5 (known as LR-5 model) emerged as the best model because it achieved a moderate classification ability (AUC = 0.75), relatively high interpretability (feature number = 5) and stability (ϕ̂Z=0.55) compared to the benchmark. The top-ranking miRNAs identified by our model have demonstrated associations with MDD pathways involving cytokine signalling in the immune system, the reelin signalling pathway, programmed cell death and cellular responses to stress.
CONCLUSIONS: The LR-5 model, which is optimised based on ML design factors, may lead to a robust and clinically usable MDD diagnostic tool.
方法:本研究将逻辑回归(LR)模型应用于血液miRNA表达谱,以区分患有MDD(n=60)的患者与健康对照(HCs,n=60)。利用嵌入式(L1-正则化逻辑回归)特征选择子提取临床相关的miRNA,并优化临床应用。
结果:当考虑所有可用的miRNA(作为基准)时,在测试数据上,MDD患者可以与受试者工作特征曲线下面积(AUC)为0.81的HC区分开。我们的LR模型选择了多达5个miRNA(称为LR-5模型)作为最佳模型,因为它实现了中等分类能力(AUC=0.75),与基准相比,相对较高的可解释性(特征号=5)和稳定性(保险柜Z=0.55)。通过我们的模型鉴定的顶级miRNA已证明与涉及免疫系统中细胞因子信号传导的MDD途径相关。reelin信号通路,程序性细胞死亡和细胞对压力的反应。
结论:LR-5模型,基于ML设计因素进行了优化,可能会导致一个强大的和临床上可用的MDD诊断工具。