Genomic instability in early systemic sclerosis
Bibliographic record
Abstract
OBJECTIVES: Systemic sclerosis (SSc) is associated with secondary malignancies. Previous studies have suggested that mutated cancer proteins, such as RNA polymerase III, are autoantigens promoting an inflammatory response in SSc. However, it has never been previously investigated whether non-neoplastic tissue in SSc harbors mutations which may play a role in SSc pathogenesis. METHODS: Skin biopsies were obtained from 8 sequential patients with a progressive form of early stage SSc (with severe skin and/or lung involvement). Areas of dermal fibrosis were microdissected and analyzed with deep, whole exome sequencing. Gene mutation patterns were compared to autologous buccal mucosal cells as a control. RESULTS: base pairs. The mutational pattern in all samples exhibited a clock-like signature, which is ubiquitous in cancers and in senescent cells. Of the 1997 genes we identified which were mutated in at least two SSc patients, 39 genes represented cancer drivers (i.e. tumor suppressor genes or oncogenes) which are commonly found in gynecological, squamous and gastrointestinal cancer signatures. Of all the mutations, the most common mutated genes were important in regulating pathways related to epigenetic histone modifications, DNA repair and genome integrity. CONCLUSIONS: Somatic hypermutation occurs in fibrotic skin in patients with early progressive SSc. Cancer driver gene mutations may potentially play a fundamental role in the pathogenesis of SSc.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.002 | 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".