Abstract：
BACKGROUND: Major depressive disorder (MDD) is notably underdiagnosed and undertreated due to its complex nature and subjective diagnostic methods. Biomarker identification would help provide a clearer understanding of MDD aetiology. Although machine learning (ML) has been implemented in previous studies to study the alteration of microRNA (miRNA) levels in MDD cases, clinical translation has not been feasible due to the lack of interpretability (i.e. too many miRNAs for consideration) and stability.
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.
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.
摘要：
背景：重度抑郁症（MDD）由于其复杂的性质和主观的诊断方法而被诊断和治疗不足。生物标志物鉴定将有助于更清楚地了解MDD的病因。尽管机器学习（ML）已在先前的研究中实施，以研究MDD病例中microRNA（miRNA）水平的变化，由于缺乏可解释性(即需要考虑的miRNA太多)和稳定性,临床翻译不可行。
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。
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。
