Abstract
Background and purpose: Diagnosis of dementia with Lewy bodies (DLB) is highly challenging, primarily due to a lack of valid and reliable diagnostic tools. To date, there is no report of qualitative signature for the diagnosis of DLB. We aimed to develop a blood-based qualitative signature for differentiating DLB patients from healthy controls. Methods: The GSE120584 dataset was downloaded from the public database Gene Expression Omnibus (GEO). We combined multiple methods to select features based on the within-sample relative expression orderings (REOs) of microRNA (miRNA) pairs. Specifically, we first quickly selected miRNA pairs related to DLB by identifying reversal stable miRNA pairs. Then, an optimal miRNA pair subset was extracted by random forest (RF) and support vector machine-recursive feature elimination (SVM-RFE) methods. Furthermore, we applied logistic regression (LR) and SVM to build several prediction models. The model performance was assessed using the receiver operating characteristic curve (ROC) analysis. Lastly, we conducted bioinformatics analyses to explore the molecular mechanisms of the discovered miRNAs. Results: A qualitative signature consisted of 17 miRNA pairs and two clinical factors was identified for discriminating DLB patients from healthy controls. The signature is robust against experimental batch effects and applicable at the individual levels. The accuracies of the-signature-based models on the test set are 82.61 and 79.35%, respectively, indicating that the signature has acceptable discrimination performance. Moreover, bioinformatics analyses revealed that predicted target genes were enriched in 11 Go terms and 2 KEGG pathways. Moreover, five potential hub genes were found for DLB, including SRF, MAPK1, YWHAE, RPS6KA3, and KDM7A. Conclusion: This study provided a blood-based qualitative signature with the potential to be used as an effective tool to improve the accuracy of DLB diagnosis.
Highlights
Dementia with Lewy bodies (DLB) is the second most common cause of neurodegenerative dementia, accounting for up to 15–20% of dementia patients (Mueller et al, 2017; Arnaoutoglou et al, 2019)
A qualitative signature consisted of 17 miRNA pairs and two clinical factors was identified for discriminating DLB patients from healthy controls
This study provided a blood-based qualitative signature with the potential to be used as an effective tool to improve the accuracy of DLB diagnosis
Summary
Dementia with Lewy bodies (DLB) is the second most common cause of neurodegenerative dementia, accounting for up to 15–20% of dementia patients (Mueller et al, 2017; Arnaoutoglou et al, 2019). An accurate diagnosis of DLB is vital for its treatment. This is mainly because patients with DLB react badly to some traditional and commonly used antipsychotic medications, notable medications with anticholinergic or antidopaminergic actions (McKeith et al, 1992). Diagnosing DLB is highly challenging, mainly due to a lack of valuable and effective biomarkers, and its symptoms are similar to other dementia subtypes, such as Alzheimer’s disease (AD) (Noe et al, 2004). A valid and reliable diagnostic method for DLB is still in demand. Diagnosis of dementia with Lewy bodies (DLB) is highly challenging, primarily due to a lack of valid and reliable diagnostic tools. We aimed to develop a bloodbased qualitative signature for differentiating DLB patients from healthy controls
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