Molecular classification of oral cancer by cDNA microarrays identifies overexpressed genes correlated with nodal metastasis
Bibliographic record
Abstract
Our purpose was to classify OSCCs based on their gene expression profiles, to identify differentially expressed genes in these cancers and to correlate genetic deregulation with clinical and histopathologic data and patient outcome. After conducting proof-of-principle experiments utilizing 6 HNSCC cell lines, the gene expression profiles of 20 OSCCs were determined using cDNA microarrays containing 19,200 sequences and the BTSVQ method of data analysis. We identified 2 sample clusters that correlated with the T3-T4 category of disease (p = 0.035) and nodal metastasis (p = 0.035). BTSVQ analysis identified a subset of 23 differentially expressed genes with the lowest QE scores in the cluster containing more advanced-stage tumors. Expression of 6 of these differentially expressed genes was validated by quantitative real-time RT-PCR. Statistical analysis of quantitative real-time RT-PCR data was performed and, after Bonferroni correction, CLDN1 overexpression was significantly correlated with the cluster containing more advanced-stage tumors (p = 0.007). Despite the clinical heterogeneity of OSCC, molecular subtyping by cDNA microarray analysis identified distinct patterns of gene expression associated with relevant clinical parameters. Application of this methodology represents an advance in the classification of oral cavity tumors and may ultimately aid in the development of more tailored therapies for oral carcinoma.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".