Delineating miRNA profile induced by chewing tobacco in oral keratinocytes
Bibliographic record
Abstract
The major established etiologic risk factor for oral cancer is tobacco (chewed, smoked and snuffed forms). Chewing form of tobacco is predominantly used in India making it the leading cause of oral cancer. Despite being one of the leading causes of oral cancer, the molecular alterations induced by chewing tobacco remains largely unclear. Carcinogenic effect of chewing tobacco is through chronic and not acute exposure. To understand the molecular alterations induced by chewing tobacco, we developed a cell line model where non-neoplastic oral keratinocytes were chronically exposed to chewing tobacco for a period of 6 months. This resulted in increased cellular proliferation and invasive ability of normal oral keratinocytes. Using this cellular model we studied the differential expression of miRNAs associated with chewing tobacco and the altered signaling pathways through which the aberrantly expressed miRNAs affect tumorigenesis. miRNA sequencing was carried out using Illumina HiSeq 2500 platform which resulted in the identification of 427 annotated miRNAs of which 10 were significantly dysregulated ( 4 fold; p-value 0.05) in tobacco exposed cells compared to untreated parental cells. To study the altered signaling in oral keratinocytes chronically exposed to chewing tobacco, we employed quantitative proteomics to characterize the dysregulated proteins. Integration of miRNA sequencing data with proteomic data resulted in identification of 36 proven protein targets which (1.5 fold; p-value 0.05) showed expression correlation with the 10 significantly dysregulated miRNAs. Pathway analysis of the dysregulated targets revealed enrichment of interferon signaling and mRNA processing related pathways in the chewing tobacco exposed cells. In addition, we also identified 6 novel miRNA in oral keratinocytes chronically exposed to chewing tobacco extract. Our study provides a framework to understand the oncogenic transformation induced by chromic tobacco exposure in normal oral keratinocytes.
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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".