Decoding Oral Carcinogenesis and Tumor Progression in Whole Cigarette Smoke Exposure: A Systematic Review
Bibliographic record
Abstract
This systematic review aims to highlight the molecular mechanisms by which whole cigarette smoke affects oral carcinogenesis and its progression in human oral cells, based on evidence from original research articles published in the literature. A literature search was conducted using three databases: Web of Science, Scopus, and PubMed from May to June 2024. The articles were screened, and the data were extracted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines (2020). The included studies were subsequently evaluated using the Systematic Review Center for Laboratory Animal Experimentation (SYRCLE) tool for bias factors. From the 14 included studies, two types of cell lines were frequently utilized: human oral mucosal epithelial cells or oral squamous cell carcinoma cells. In these cell lines, one of three forms of exposure was applied: cigarette smoke, its extract, or condensate. The mechanism of oral carcinogenesis and tumor progression includes aberrations in the heme metabolic pathway, modulation of miRNA-145, NOD1 and BiP expression, MMP-2, MMP-9, and cathepsin modulation, abnormal TSPO binding, RIP2-mediated NF-κB activation, MZF1-mediated VEGF binding, and activation of the RAGE signaling pathway. In conclusion, cigarette smoke significantly influences the development and progression of oral squamous cell carcinoma, based on the evidence highlighted in human oral cells. While previous studies have focused on specific carcinogens and pathways, this review added to our understanding of the overall impact of whole cigarette smoke on oral carcinogenesis at the molecular and cellular levels.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".