Acquisition of cancer stem cell-like properties in human small airway epithelial cells after a long-term exposure to carbon nanomaterials
Bibliographic record
Abstract
Cancer stem cells (CSCs) are a key driver of tumor formation and metastasis, but how they are affected by nanomaterials is largely unknown. The present study investigated the effects of different carbon-based nanomaterials (CNMs) on neoplastic and CSC-like transformation of human small airway epithelial cells and determined the underlying mechanisms. Using a physiologically relevant exposure model (long-term/low-dose) with system validation using a human carcinogen, asbestos, we demonstrated that single-walled carbon nanotubes, multi-walled carbon nanotubes, ultrafine carbon black, and crocidolite asbestos induced particle-specific anchorage-independent colony formation, DNA-strand break, and p53 downregulation, indicating genotoxicity and carcinogenic potential of CNMs. The chronic CNM-exposed cells exhibited CSC-like properties as indicated by 3D spheroid formation, anoikis resistance, and CSC markers expression. Mechanistic studies revealed specific self-renewal and epithelial-mesenchymal transition (EMT)-related transcription factors that are involved in the cellular transformation process. Pathway analysis of gene signaling networks supports the role of SOX2 and SNAI1 signaling in CNM-mediated transformation. These findings support the potential carcinogenicity of high aspect ratio CNMs and identified molecular targets and signaling pathways that may contribute to the disease development.
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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".