Molecular characterization of immortalized normal and dysplastic oral cell lines
Bibliographic record
Abstract
BACKGROUND: Cell lines have been developed for modeling cancer and cancer progression. The molecular background of these cell lines is often unknown to those using them to model disease behaviors. As molecular alterations are the ultimate drivers of cell phenotypes, having an understanding of the molecular make-up of these systems is critical for understanding the disease biology modeled. METHODS: Six immortalized normal, one immortalized dysplasia, one self-immortalized dysplasia, and two primary normal cell lines derived from oral tissues were analyzed for DNA copy number changes and changes in both mRNA and miRNA expression using SMRT-v.2 genome-wide tiling comparative genomic hybridization arrays, Agilent Whole Genome 4x44k expression arrays, and Exiqon V2.M-RT-PCR microRNA Human panels. RESULTS: DNA copy number alterations were detected in both normal and dysplastic immortalized cell lines-as well as in the single non-immortalized dysplastic cell line. These lines were found to have changes in expression of genes related to cell cycle control as well as alterations in miRNAs that are deregulated in clinical oral squamous cell carcinoma tissues. Immortal lines-whether normal or dysplastic-had increased disruption in expression relative to primary lines. All data are available as a public resource. CONCLUSIONS: Molecular profiling experiments have identified DNA, mRNA, and miRNA alterations for a panel of normal and dysplastic oral tissue cell lines. These data are a valuable resource to those modeling diseases of the oral mucosa, and give insight into the selection of model cell lines and the interpretation of data from those lines.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".