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Dementia subtype prediction models constructed by penalized regression methods for multiclass classification using serum microRNA expression data

ABSTRACT: There are many subtypes of dementia, and identification of diagnostic biomarkers that are minimally-invasive, low-cost, and efficient is desired. Circulating microRNAs (miRNAs) have recently gained attention as easily accessible and non-invasive biomarkers. We conducted a comprehensive miRNA expression analysis of serum samples from 1348 Japanese dementia patients, composed of four subtypes—Alzheimer’s disease (AD), vascular dementia, dementia with Lewy bodies (DLB), and normal pressure hydrocephalus—and 246 control subjects. We used this data to construct dementia subtype prediction models based on penalized regression models with the multiclass classification. We constructed a final prediction model using 46 miRNAs, which classified dementia patients from an independent validation set into four subtypes of dementia. Network analysis of miRNA target genes revealed important hub genes, SRC and CHD3, associated with the AD pathogenesis. Moreover, MCU and CASP3, which are known to be associated with DLB pathogenesis, were identified from our DLB-specific target genes. Our study demonstrates the potential of blood-based biomarkers for use in dementia-subtype prediction models. We believe that further investigation using larger sample sizes will contribute to the accurate classification of subtypes of dementia.

ORGANISM(S): Homo sapiens

PROVIDER: GSE167559 | GEO | 2021/10/22

REPOSITORIES: GEO

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Project description:Dementia with Lewy bodies (DLB) is the second most common cause of degenerative dementia after Alzheimer’s disease (AD) and presents several overlapping pathological and clinical features with both AD and Parkinson’s disease (PD). Consequently, only one in three DLB cases is diagnosed correctly. Platelets are anucleate cells containing cellular structures and molecules, such as microRNA (miRNA), the analysis of which provides biomarkers for several disorders. In this cross-sectional study, we profiled the whole platelet miRNA transcriptome from 7 DLB patients and 7 healthy controls using Next-Generation Sequencing (NGS) and found 22 differentially expressed miRNAs. These miRNAs were further validated with miRCURY LNA miRNA Custom PCR panels in three consecutive validation studies carried out from 2017-2019 with a total of 162 individuals including DLB (n=59), AD (n=28), and PD patients (n=24), and healthy controls (n=51). As a result, three different groups of miRNAs were identified as related to DLB, AD and PD, all in comparison with controls. Additionally, our results demonstrated a significant down-regulation of hsa-miR-26b-5p and hsa-miR-150-5p in DLB compared to PD; and a signature composed of seven miRNAs that had lower expression in DLB compared to AD. The predictive diagnostic value of this bio-signature for the differentiation of DLB from AD was assessed calculating the corresponding ROC curve, which yielded an area under the curve of 1.00. Predictive analyses using specialized software (miRTarbase, miRGate) were performed for the disease-specific miRNAs to identify target genes and corresponding pathways. Three disease-specific clusters of pathways and biological processes were identified as a result of platelet-miRNA deregulation. In DLB, pathways related to gene expression and small RNA metabolism; in AD, pathways related to stress response and RNA stress granules; and in PD pathways related to protein phosphorylation, and protein metabolism and degradation were identified. In conclusion, we describe the identification of a novel, highly specific and sensitive platelet-associated miRNA-based bio-signature, which permits us to distinguish between DLB and AD.

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Dementia subtype prediction models constructed by penalized regression methods for multiclass classification using serum microRNA expression data

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