<scp>STC2</scp> Serves as a Critical Hypoxic Effector in Keloid Pathogenesis by Orchestrating Fibroblasts Activation and <scp>ECM</scp> Remodelling
Bibliographic record
Abstract
Abnormal activation of keloid fibroblasts (KFs) within a hypoxic microenvironment is a hallmark of keloid pathogenesis. However, the precise molecular mechanisms by which hypoxia drives fibroblast dysfunction remain insufficiently understood. This study aimed to investigate the role of Stanniocalcin 2 (STC2), a hypoxia-responsive glycoprotein, in modulating keloid fibroblast behaviour under hypoxic conditions and to elucidate its upstream and downstream regulatory networks. We found the expression of STC2 was significantly upregulated in keloid tissues and primary KFs, with expression levels positively correlating with clinical severity, as assessed by the Vancouver Scar Scale. Mechanistically, hypoxia induced STC2 expression via hypoxia-inducible factor-1α. Functional assays revealed that STC2 silencing under hypoxia markedly reduced KF proliferation, migration and extracellular matrix remodelling, as evidenced by downregulation of fibrosis-associated markers including collagen I, α-SMA, MMP2 and MMP9. These inhibitory effects were accompanied by attenuation of ERK and AKT signalling pathway activation. Thus, targeting STC2 disrupts pro-fibrotic signalling and may represent a promising therapeutic strategy for the clinical management of keloid scars by modulating the aberrant hypoxic microenvironment.
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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 | 0.000 | 0.000 |
| 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".