Diversity of oxidative stress and senescence phenotypes induced by chemotherapeutic agents in HUVECs
Bibliographic record
Abstract
Chemotherapy-induced endothelial cell senescence impacts tumor angiogenesis and therapy outcomes. While senescence can suppress tumorigenesis, it may also promote chronic inflammation and vascular toxicity. This study investigates how different chemotherapeutic agents induce senescence in human umbilical vein endothelial cells (HUVECs) and explores the roles of reactive oxygen species (ROS) and ataxia telangiectasia mutated (ATM) kinase. HUVECs were treated with DNA crosslinkers (doxorubicin, mitomycin C), topoisomerase inhibitors (etoposide, camptothecin), and methotrexate (MTX). Senescence was assessed via senescence-associated β-galactosidase (SA-β-gal) staining, senescence-associated secretory phenotype related factors, Phalloidin staining, immunofluorescence (p53, Ki67, 53BP1, γH2AX), mitochondrial morphology, ROS measurement, and transcriptomic sequencing. ROS scavenger Mito-Q and ATM inhibitor KU55933 were co-administered to evaluate their modulatory effects. SA-β-gal staining demonstrated that all agents induced senescence, with etoposide being the most potent (80% SA-β-gal-positive cells) and methotrexate the weakest (40%). DNA damage markers (53BP1, γH2AX) and ROS levels increased significantly, accompanied by mitochondrial fragmentation, reduced Ki67, and increased cell morphology. Furthermore, Mito-Q alleviated methotrexate-induced senescence but had no effect on other agents. ATM inhibition did not demonstrate an effect across all treatments in this study. Transcriptomic analysis revealed the p53 signaling pathway as a pivotal molecular determinant in chemotherapy-induced cellular senescence. MTX appeared mechanistically linked to TNF-mediated signaling cascades. Chemotherapeutic agents triggered endothelial senescence via DNA damage and ROS accumulation, with heterogeneity in potency and mechanisms. ROS scavengers may mitigate methotrexate-associated vascular toxicity, and targeting ROS could enhance chemotherapy safety by preserving endothelial function.
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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".