Predictive value of α-synuclein expression in peripheral blood of multiple sclerosis patients: A two-dimensional assessment of a selected biomarker.

INTRODUCTION: Our study aimed to evaluate whether assessing α-synuclein expression levels in blood samples could provide a reliable and straightforward alternative to existing diagnostic and prognostic methods for neurodegenerative disorders, including multiple sclerosis (MS). We specifically investigated if α-synuclein and IL-6 expression levels from serum and peripheral blood mononuclear cells (PBMCs) could accurately predict MS severity in patients using a two-dimensional approach. METHODS: We designed a case-control study to analyze the expression of α-synuclein and IL-6 in the peripheral blood of an MS patient group (n = 51) and a control group (n = 51). We statistically evaluated the PBMCs and serum profiles of α-synuclein and IL-6 in MS patients, along with their age of onset, disease duration, tobacco exposure, and Expanded Disability Status Scale (EDSS) score, using SPSS V22.0 software and GraphPad Prism V9.0. RESULTS: Our findings indicate that α-synuclein production was significantly downregulated in MS patients. Principal component analysis also revealed distinct profiles between MS patients and controls. PBMCs and serum profiles of α-synuclein correlated with the EDSS score, suggesting that disease severity can be predicted using α-synuclein profiles. Moreover, α-synuclein showed a significant correlation with IL-6 and age of onset. Lastly, receiver operating characteristic curves of PBMCs and serum activity of α-synuclein profiles displayed discrimination with area under the curve values of 0.856 and 0.705, respectively. CONCLUSION: Our results imply that measuring α-synuclein levels in both serum and PBMCs could be a valuable method for diagnosing and predicting MS severity, potentially serving as a non-invasive biomarker for the disease.

The analysis of dynamic gene expression patterns in peripheral blood of multiple sclerosis patients indicates possible diagnostic and prognostic biomarkers.

INTRODUCTION: Among numerous invasive procedures for the research of biomarkers, blood-based indicators are regarded as marginally non-invasive procedures in the diagnosis and prognosis of demyelinating disorders, including multiple sclerosis (MS). In this study, we looked into the blood-derived gene expression profiles of patients with multiple sclerosis to investigate their clinical traits and linked them with dysregulated gene expressions to establish diagnostic and prognostic indicators. METHODS: We included 51 patients with relapsing-remitting MS (RRMS, n = 31), clinically isolated syndrome (CIS, n = 12), primary progressive MS (PPMS, n = 8) and a control group (n = 51). Using correlational analysis, the transcriptional patterns of chosen gene panels were examined and subsequently related with disease duration and the expanded disease disability score (EDSS). In addition, principal component analysis, univariate regression, and logistic regression analysis were employed to highlight distinct profiles of genes and prognosticate the excellent biomarkers of this illness. RESULTS: Our findings demonstrated that neurofilament light (NEFL), tumor necrosis factor α (TNF-α), Tau, and clusterin (CLU) were revealed to be increased in recruited patients, whereas the presenilin-1 (PSEN1) and cell-surface glycoprotein-44 (CD44) were downregulated. Principal Component Analysis revealed distinct patterns between the MS and control groups. Correlation analysis indicated co-dependent dysregulated genes and their differential expression with clinical findings. Furthermore, logistic regression demonstrated that Clusterin (AUC=0.940), NEFL (AUC=0.775), TNF-α (AUC=0.817), Tau (AUC=0.749), PSEN1 (AUC=0.6913), and CD44 (AUC=0.832) had diagnostic relevance. Following the univariate linear regression, a significant regression equation was found between EDSS and IGF-1 (R2 adj = 0.10844; p= 0.0060), APP (R2 adj = 0.1107; p= 0.0098), and PSEN1 (R2 adj = 0.1266; p=0.0102). CONCLUSION: This study exhibits dynamic gene expression patterns that represent the significance of specified genes that are prospective diagnostic and prognostic biomarkers for multiple sclerosis.