Investigating the Role of E-cigarettes in Epigenetic Changes and Cancer Risk
Bibliographic record
Abstract
Background: E-cigarettes have become popular as an alternative to traditional smoking but their long-term health effects, especially regarding cancer risk, are concerning. This review evaluates the potential carcinogenic effects of e-cigarettes, focusing on DNA damage, epigenetic changes, and tumor-promoting pathways that may promote tumor development. Materials and Methods: A literature search on PubMed, Scopus, and Web of Science databases yielded studies on e-cigarettes and cancer risk from 2010 to 2024. The employed keywords included "e-cigarettes", "vaping", "cancer risk", and "toxic chemicals". Studies included data on e-cigarette vapor composition and health effects, excluding those on smoking cessation. Data extraction covered study design, population, e-cigarette type, usage, health outcomes, and vapor analysis. Quality was assessed using the Cochrane risk of bias tool and the Newcastle-Ottawa scale. Results: Research indicates that e-cigarettes can cause DNA damage and epigenetic changes that potentially lead to cancer. DNA methylation can alter gene expression and cause mutations, particularly in the respiratory system, increasing cancer risk. Short-term use of e-cigarettes induces lung cancer-related tumor-promoting factors and metastasis in small bronchial tubes. Nicotine inhalation from e-cigarettes can promote tumor growth by stimulating angiogenesis and inhibiting apoptosis, despite nicotine not being a carcinogen. E-cigarette vapor contains known carcinogens like formaldehyde and acetaldehyde, further contributing to cancer risk. Conclusion: Exposure to e-cigarette vapor causes gene expression changes and epigenetic damage similar to those from combustible cigarette smoke, potentially leading to cancer. DNA methylation can change gene expression and cause mutations, especially in respiratory cells.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".