BDNF-AS is a promising downregulated biomarker for the diagnosis in different disease types in multiple sclerosis

Abstract

Multiple sclerosis (MS) is an autoimmune demyelinating disease of the central nervous system and the most common neurological disease in young adults, affecting more than 2.5 million people worldwide. The disease progression is likely to be the result of neurodegeneration. Numerous therapies that have emerged to treat relapsing forms of MS have been unsuccessful in slowing or halting progressive forms of the disease, during which inflammatory activity is replaced by axonal loss and atrophy. BDNF-AS is a type of long non-coding RNAs that has a negative transcriptional regulation over the neurotrophin BDNF, which is known to be highly expressed in actively demyelinating MS lesions and which has a function in neuron survival and plasticity necessary for neuroprotection during inflammation. BDNF has been shown to be low in older and chronic MS plaques, which could be a contributing factor to the ongoing degenerative changes in the chronic progressive stages of MS. However, few studies have investigated the role of BDNF-AS and how it may contribute to the role of BDNF in MS. Therefore, we studied the relative expression of BDNF-AS in the serum of patients with different disease types of MS via qRT-PCR. We found that BDNF-AS is significantly downregulated in MS as compared to controls. We also determined a relative expression cut-off of (0.31) fold change, which can be used for the diagnosis of MS with high specificity and sensitivity.

Diagnostic efficacy of long non-coding RNAs in multiple sclerosis: a systematic review and meta-analysis

“…Subsequent careful screening of abstracts led to the exclusion of 295 articles. Final inclusion criteria-excluding animal cell experiments, non-blood sample studies, data extraction issues, reviews, and comments-resulted in 13 studies (Gharesouran et al, 2019b;Sayad et al, 2019;Shaker et al, 2019;Ghaiad et al, 2020;Senousy et al, 2020;Safa et al, 2021;Soltanmoradi et al, 2021;Akbari et al, 2022;Amiri et al, 2022;Attia et al, 2023;Ghafouri-Fard et al, 2023;Kamal et al, 2023;Kortam et al, 2023) qualifying for the meta-analysis. This process is detailed in Figure 1.…”