School of Life Sciences, Forman Christian College (A Chartered University), Lahore 54600, Pakistan.
Rashid Latif Medical College, Lahore, Pakistan.
Mol Immunol. 2022 Jul;147:147-156. doi: 10.1016/j.molimm.2022.05.002. Epub 2022 May 17.
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 (R adj = 0.10844; p= 0.0060), APP (R adj = 0.1107; p= 0.0098), and PSEN1 (R 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.
在众多用于研究生物标志物的侵入性操作中，血液标志物被认为是诊断和预测脱髓鞘疾病（包括多发性硬化症）的非侵入性操作。在这项研究中，我们研究了多发性硬化症患者的血液衍生基因表达谱，以探讨其临床特征，并将其与失调基因表达相关联，以建立诊断和预后指标。
我们纳入了 51 名复发性缓解型多发性硬化症（RRMS，n=31）、临床孤立综合征（CIS，n=12）、原发性进展型多发性硬化症（PPMS，n=8）和对照组（n=51）。通过相关性分析，研究了选定基因组合的转录模式，并将其与疾病持续时间和扩展残疾状态评分（EDSS）相关联。此外，我们还进行了主成分分析、单变量回归和逻辑回归分析，以突出基因的不同特征，并预测该疾病的优秀生物标志物。
我们的研究结果表明，神经丝轻链（NEFL）、肿瘤坏死因子-α（TNF-α）、Tau 和载脂蛋白 E（CLU）在入组患者中均升高，而早老素-1（PSEN1）和细胞表面糖蛋白 44（CD44）则下调。主成分分析揭示了 MS 组和对照组之间的不同模式。相关性分析表明，存在共依赖失调基因，其表达与临床发现有关。此外，逻辑回归表明，Clusterin（AUC=0.940）、NEFL（AUC=0.775）、TNF-α（AUC=0.817）、Tau（AUC=0.749）、PSEN1（AUC=0.6913）和 CD44（AUC=0.832）具有诊断相关性。在单变量线性回归之后，发现 EDSS 与 IGF-1（R adj=0.10844；p=0.0060）、APP（R adj=0.1107；p=0.0098）和 PSEN1（R adj=0.1266；p=0.0102）之间存在显著的回归方程。
本研究展示了动态的基因表达模式，代表了特定基因作为多发性硬化症有前途的诊断和预后生物标志物的重要性。
