The cancer-associated glycan polysialic acid is dysregulated in systemic sclerosis and is associated with fibrosis
Bibliographic record
Abstract
OBJECTIVE: Systemic sclerosis (SSc) is a rare but deadly disease characterized by autoimmunity, vasculopathy, and fibrosis. Fibrotic complications associated with SSc correlate with severe morbidity and mortality. Previous studies in SSc have identified fibroblasts as the primary drivers of fibrosis; however, the mechanism(s) promoting this are not well understood. Aberrant glycosylation, particularly polysialylation (polySia), has been described as a prominent feature of aggressive cancers. Inspired by this observation, we aimed to determine if polySia is dysregulated in various forms of SSc. METHODS: All patients with SSc met the 2013 ACR/EULAR. Patients were sub-classified into limited cutaneous (lSSc, N = 5 or 46 patients for polySia quantification in the dermis or serum; respectively), diffuse cutaneous (dSSc, N = 11 or 18 patients for polySia quantification in the dermis or serum; respectively), or patients with dSSc treated with an autologous stem cell transplantation (post-ASCT, N = 4 patients for quantification in the dermis). Dermal polySia levels were measured via immunofluorescence microscopy in 10 μm dermal sections, quantified in each group (healthy volunteers (HC), lSSc, dSSc, and post-ASCT) and correlated with skin fibrosis (via the modified Rodnan skin score (mRSS)). Similarly, serum polySia was quantified in each group, and correlated with the mRSS. RESULTS: Dermal polySia levels were highest in patients with dSSc (compared to HC < 0.001), and correlated with the degree of fibrosis in all of the groups (P = 0.008). Serum polySia was higher in all SSc groups (p < 0.001) and correlated with the severity of mRSS (p < 0.0001). CONCLUSION: Polysia is more abundant in the skin and sera from patients with SSc and correlates with the degree of skin fibrosis. The aberrant expression of polySia highlights its potential use as a biomarker in patients with progressive forms of SSc. Dysregulated polySia levels in SSc further emphasizes the cancer-like phenotype present in SSc, which may promote fibrosis and immune dysregulation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".