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Updated: Jan 10, 2026

RNA Pull-down Procedure to Identify RNA Targets of a Long Non-coding RNA
Published on: April 10, 2018
Long Non-Coding RNAs in Multiple Sclerosis-Differential Expression and Functional Implications
Kaalindi Misra1, Aishwary Nerkar1, Ferdinando Clarelli1
1Laboratory of Human Genetics of Neurological Disorders, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy.
Background/Objectives:
Long non-coding RNAs (lncRNAs) are increasingly recognized as key regulators of immune pathways and may hold diagnostic and therapeutic relevance in autoimmune diseases such as Multiple Sclerosis (MS). However, research on lncRNAs in MS remains fragmented and geographically clustered. This systematic review aimed to collate and critically evaluate studies of lncRNA expression in MS, assess consistency of findings across studies, and synthesize proposed functional implications of the most frequently studied lncRNAs.
Methods:
This PROSPERO-registered review (CRD420250575938), conducted in accordance with PRISMA, searched PubMed, Scopus, Embase, and Web of Science (2010-2024) for studies evaluating lncRNA expression in adult MS (≥18 years of age). Eligible studies included ≥20 participants and assessed lncRNAs in blood, PBMCs, serum, plasma, or CSF using qRT-PCR, RNA-seq, or microarrays. Pediatric, review, animal, and in vitro studies were excluded. Two reviewers independently screened and extracted data, with risk of bias evaluated using QUADAS-2.
Results:
Narrative synthesis of 51 studies identified 77 unique lncRNAs. A limited set (MALAT1, GAS5, MEG3, H19) demonstrated consistent dysregulation in MS, whereas others (THRIL, IFNG-AS1, HOTAIR, TUG1) exhibited context-dependent expression influenced by treatment, relapse status, or demographics. Functional annotations converged on immune pathways, including NF-κB, STAT3, IFN-γ/Th1, and glucocorticoid signaling.
Conclusions:
This review identifies reproducible and context-specific lncRNA dysregulation in MS, emphasizing the need for transcriptome-wide approaches, standardized methods, and multi-center validation. Current evidence is constrained by geographic clustering, preselection bias, and methodological heterogeneity.
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