Water-Pipe Smoking Exposure Deregulates a Set of Genes Associated with Human Head and Neck Cancer Development and Prognosis
Bibliographic record
Abstract
Water-pipe smoking (WPS) is becoming the most popular form of tobacco use among the youth, especially in the Middle East, replacing cigarettes rapidly and becoming a major risk of tobacco addiction worldwide. Smoke from WPS contains similar toxins as those present in cigarette smoke and is linked directly with different types of cancers including lung and head and neck (HN) carcinomas. However, the underlying molecular pathways and/or target genes responsible for the carcinogenic process are still unknown. In this study, human normal oral epithelial (HNOE) cells, NanoString PanCancer Pathways panel of 770 gene transcripts and quantitative real-time polymerase chain reaction (qRT-PCR) analysis were applied to discover differentially expressed genes (DEG) modulated by WPS. In silico analysis was performed to analyze the impact of these genes in HN cancer patient's biology and outcome. We found that WPS can induce the epithelial-mesenchymal transition (EMT: hallmark of cancer progression) of HNOE cells. More significantly, our analysis of NanoString revealed 23 genes deregulated under the effect of WPS, responsible for the modulation of cell cycle, proliferation, migration/invasion, apoptosis, signal transduction, and inflammatory response. Further analysis was performed using qRT-PCR of HNOE WPS-exposed and unexposed cells supported the reliability of our NanoString data. Moreover, we demonstrate those DEG to be upregulated in cancer compared with normal tissue. Using the Kaplan-Meier analysis, we observed a significant association between WPS-deregulated genes and relapse-free survival/overall survival in HN cancer patients. Our findings imply that WPS can modulate EMT as well as a set of genes that are directly involved in human HN carcinogenesis, thereby affecting HN cancer patients' survival.
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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".