Selecting the Appropriate Oral Cancer Cell Line: Characteristic-Based Recommendations from a Systematic Review
Bibliographic record
Abstract
Introduction: Oral squamous cell carcinoma (OSCC) research often relies on in vitro models to study tumor behaviour and evaluate therapeutic agents. However, variability in cell line characteristics and limited guidance on their selection pose challenges to research consistency and translational accuracy. Objective: To provide a comprehensive evaluation of OSCC cell lines based on anatomical origin/site, biological features and associated risk factors, offering evidence-based recommendations for their appropriate use. Method: A systematic review was conducted in PubMed database covering the period 1972 to 2024 using the keywords “human,” “oral squamous cell carcinoma,” and “cell line.” Inclusion criteria were English full-text publications describing human-derived OSCC cell lines. Cell lines of animal origin or with known contamination were excluded from this study. Results: Out of 524 records, 106 publications were analyzed. Japan and the USA led in cell line development. Most cell lines originated from male patients and the tongue was the predominant anatomical site for OSCC. Highly cited lines such as HSC-3, CAL-27, and SAS were favored for studies on metastasis, immune markers, and drug testing. Cell lines were categorized based on single or multiple risk exposures, including tobacco, alcohol, betel quid, and HPV infection. Conclusion: This review provides an evidence-based framework for selecting OSCC cell lines by anatomical origin/site, molecular features, and documented etiologic exposures. Therefore, researchers should align cell-line selection with the relevant characteristic background and the specific experimental goal (e.g., metastasis, immune-marker, or drug testing), prioritizing well-characterized models when reproducibility and translational relevance are key.
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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 | 0.032 | 0.164 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.022 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".