Category: Parkinsonism, Atypical: MSA
Objective: To establish an inclusive data analytic scheme to identify and characterize plasma microRNA biomarkers with pathophysiological significance for differentiating the atypical Parkinsonian, Multiple System Atrophy, patients from healthy control and typical Parkinson’s Disease (PD) patients.
Background: Having overlapped symptoms with typical PD patients but response differently to medication and much faster disease progression, early differential diagnosis of MSA patients is critical for disease management and for facilitating enrollment to suitable clinical trials.
Method: In total of 174 patients categorizing into typical PD, MSA and healthy control were recruited with their plasma small RNA sequenced and subject to our newly developed “Biomedical Oriented Dantzig Selector (BOLD Selector)” containing data analytic scheme: Bioinformatic analysis, normalization/trimming; BOLD-Selector biomarker identification followed by principle component based logistic regression formula building and stringent 5-fold cross validation of the prediction power; identifying and characterizing the biological pathways predicted for the selected biomarkers.
Results: In total of 23 and 19 plasma microRNAs out of 2700 candidates were identified for differentiating MSA patients from healthy control and from PD patients, respectively. Many of these microRNAs are directly or indirectly targeting genes categorized in myelination and demyelination related pathways or diseases. After principle component analysis, 12 and 13 microRNAs selected from significant PCs were used to build logistic regression formulas. With the stringent 5-fold cross validation evaluation, the sensitivity and specificity is high for distinguishing MSA from HC (average AUC: 0.908) and less satisfactory for distinguishing MSA patients from typical PD patients. Oligodendrocyte progenitor cell line with inducible alpha-synuclein expression in the undifferentiated or differentiating stages were also established to perform functional analysis of these microRNA candidates.
Conclusion: BOLD Selector-inclusive data analytic scheme can identify biomarkers with pathophysiological significance and generate logistic regression formula for differential diagnosis of MSA from healthy control or typical PD patients, as well as paving ways to novel therapeutic strategies.
To cite this abstract in AMA style:CC. Lu, MC. Kuo, JW. Huang, Y-T. Tsai, H-H. Lin Wang, PJ. Kung, C-C. Wu, Y-Y. Hsueh, T. Ochiya, FKH. Phoa, S-P. Lin, R-M. Wu. Myelination relevant plasma microRNA biomarkers identified via an innovative data analytic scheme for differential diagnosis of MSA, an Oligodendroglial Synucleinopathy [abstract]. Mov Disord. 2022; 37 (suppl 2). https://www.mdsabstracts.org/abstract/myelination-relevant-plasma-microrna-biomarkers-identified-via-an-innovative-data-analytic-scheme-for-differential-diagnosis-of-msa-an-oligodendroglial-synucleinopathy/. Accessed March 1, 2024.
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MDS Abstracts - https://www.mdsabstracts.org/abstract/myelination-relevant-plasma-microrna-biomarkers-identified-via-an-innovative-data-analytic-scheme-for-differential-diagnosis-of-msa-an-oligodendroglial-synucleinopathy/