الفهرس 
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Abstract
It is the commonest non-traumatic disabling disease to affect young adults. There is an increasing incidence and prevalence of MS in both developed and developing countries, the underlying cause of which remains uncertain.
Clinical and imaging findings that may be seen in MS, can also be mimicked by some infectious, neoplastic, genetic, metabolic, vascular, and other idiopathic inflammatory demyelinating disorders (IIDD). Therefore, to identify MS-related attacks and determine the final diagnosis is vital for the correct treatment choice and long-term disability prevention.
Molecular biomarkers, on the other hand, are easily quantifiable and can excellently complement MRI and clinical characteristics.
This study aimed to assess gene expression signatures that could help MS patients’ diagnosis.
This study was conducted on 28 diagnosed multiple sclerosis patients at the Clinical Pathology Department, Faculty of Medicine, Menoufia University, from August 2021 till April 2022 and involved. 28 age and sexmatched healthy participants served as a control group.
Patients were diagnosed with MS based on clinical and MRI findings, according to Modified McDonald criteria (2017). Patients were excluded if they have Co-existent other neurological disease, autoimmune disease, vascular, infectious, and other inflammatory causes of myelopathy.
All patients were subjected to Detailed patient history including family history, Thorough general examination, neurological examination, and MRI imaging. The selected patients included twenty-two patients with relapsing remitting subtype, two patients with primary progressive subtype, and four patients with the clinically isolated syndrome.
Two ml of EDTA blood was sampled and RNA was extracted with Reverse transcription of RNA to cDNA was immediately performed. cDNA was used in a real-time pcr reaction to estimate gene expression of (TP53, CTSS, TAF11, LLGL2, and TGM2) genes using the B-actin gene as an endogenous reference gene. Resulted CT values were used to calculate the following ratios for the MS and control group:
(CTSS × LLGL22 × TGM2) ÷ (TAF112 × TP532) CTSS ÷TAF11
There was a lower expression of TP53 (P=0.002) and TAF11 (P=0.003) in the MS group than in the controls. The levels of CTSS, LLGL2, or TGM2 RQ were insignificant between the MS group and controls. Regarding (CTSS × LLGL22 × TGM2) ÷ (TAF112 × TP532), the median score was higher in MS group than controls (13.44 VS. 0.45) (p<0.001). According to the (CTSS÷ TAF11) ratio, the median score was higher in the MS group than in controls (1.71 VS. 0.88) (p= 0.008).
The diagnostic performance of TP53, TAF11 RQ, and both (CTSS÷ TAF11) ratio and (CTSS × LLGL22 × TGM2) ÷ (TAF112 × TP532) ratio was demonstrated by ROC curve analysis which revealed that the best cutoff value for TP53 RQ was ≤ 0.91 with sensitivity 89.3% and specificity 60.9%. The best cutoff value for TAF11 RQ was ≤ 0.68 with a 71.4% sensitivity and 67.9%specificity. The best cutoff value for (CTSS÷ TAF11) ratio was ≥ 1.14 with a sensitivity of 67.9% and specificity of 67.9%.