The analysis of dynamic gene expression patterns in peripheral blood of multiple sclerosis patients indicates...

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.

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.

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).

This study exhibits dynamic gene expression patterns that represent the significance of specified genes that are prospective diagnostic and prognostic biomarkers for multiple sclerosis.