Novel role of long non-coding RNAs in autoimmune cutaneous disease
Bibliographic record
Abstract
Systemic autoimmune rheumatic diseases (SARDs) are a heterogeneous group of chronic multisystem inflammatory disorders that are thought to have a complex pathophysiology, which is not yet fully understood. Recently, the role of non-coding RNAs, including long non-coding RNA (lncRNA), has been of particular interest in the pathogenesis of SARDs. We aimed to summarize the potential roles of lncRNA in SARDs affecting the skin including, systemic sclerosis (SSc), dermatomyositis (DM) and cutaneous lupus erythematosus (CLE). We conducted a narrative review summarizing original articles published until July 19, 2021, regarding lncRNA associated with SSc, DM, and CLE. Several lncRNAs were hypothesized to play an important role in disease pathogenesis of SSc, DM and CLE. In SSc, Negative Regulator of IFN Response (NRIR) was thought to modulate Interferon (IFN) response in monocytes, anti-sense gene to X-inactivation specific transcript (TSIX) to regulate increased collagen stability, HOX transcript antisense RNA (HOTAIR) to increase numbers of myofibroblasts, OTUD6B-Anti-Sense RNA 1 to decrease fibroblast apoptosis, ncRNA00201 to regulate pathways in SSc pathogenesis and carcinogenesis, H19X potentiating TGF-β-driven extracellular matrix production, and finally PSMB8-AS1 potentiates IFN response. In DM, linc-DGCR6-1 expression was hypothesized to target the USP18 protein, a type 1 IFN-inducible protein that is considered a key regulator of IFN signaling. Additionally, AL136018.1 is suggested to regulate the expression Cathepsin G, which increases the permeability of vascular endothelial cells and the chemotaxis of inflammatory cells in peripheral blood and muscle tissue in DM. Lastly, lnc-MIPOL1-6 and lnc-DDX47-3 in discoid CLE were thought to be associated with the expression of chemokines, which are significant in Th1 mediated disease. In this review, we summarize the key lncRNAs that may drive pathogenesis of these connective tissue diseases and could potentially serve as therapeutic targets in the future.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".