Multiple Mechanisms for Anti-Fibrotic Functions of Statins on Radiotherapy Induced Fibrosis
Bibliographic record
Abstract
Radiotherapy-induced fibrosis (RTIF) presents a challenge in radiotherapy for cancer patients. Although numerous studies have attempted to elucidate the mechanisms leading to RTIF, the pathogenesis of RTIF at the cellular and molecular level is still incompletely described. One key component involved in the post-radiation injury is the pleuripotent cytokine transforming growth factor (TGF)-β. TGF-β signaling pathway has been under intensive investigation about its critical role in radiation-induced fibroproliferative disease. Connective tissue growth factor (CTGF), also known as insulin-like growth factor binding protein-related protein 2 (IGFBP-rP2) is a potent regulator of fibroblast proliferation, cell adhesion, and stimulation of extracellular matrix production. CTGF is known as a major downstream mediator of the chronic fibrotic effects of TGF-β. Here we have demonstrated that irradiation and TGF-β induced CTGF, subsequently upregulates fibrotic factors such as fibronectin and type IV collagen. Furthermore, as HMG-CoA reductase inhibitors, statins inhibit expressions of CTGF and downstream fibrotic proteins in both normal human fetal fibroblasts (HFL-1) and human dermal fibroblasts (HDF) on TGF-β treatment or irradiation. Our study also demonstrates that simvastatin not only suppressed TGF-β-induced fibrosis through inhibition of CTGF production but also CTGF-induced fibrosis. We further show that simvastatin may act in a TGF-β-independent manner by inhibiting Rho kinase pathway. Taken together, these data suggest that radiotherapy may upregulate CTGF expression in a TGF-β-dependent and -independent manner, thereby enhancing expression of profibrotic factors and inducing lung fibrosis.
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".