Multiple pathways in the FGF signaling network are frequently deregulated by gene amplification in oral dysplasias
Bibliographic record
Abstract
Genetic alteration in oral premalignant lesions (OPLs), the precursors of oral squamous cell carcinomas (OSCCs), may represent key changes in disease initiation and development. We ask if DNA amplification occurs at this early stage of cancer development and which oncogenic pathways are disrupted in OPLs. Here, we evaluated 50 high-grade dysplasias and low-grade dysplasias that later progressed to cancer for gene dosage aberrations using tiling-path DNA microarrays. Early occurrences of DNA amplification and homozygous deletion were frequently detected, with 40% (20/50) of these early lesions exhibiting such features. Expression for 88 genes in 7 recurrent amplicons were evaluated in 5 independent head and neck cancer datasets, with 40 candidates found to be overexpressed relative to normal tissues. These genes were significantly enriched in the canonical ERK/MAPK, FGF, p53, PTEN and PI3K/AKT signaling pathways (p = 8.95 x 10(-3) to 3.18 x 10(-2)). These identified pathways share interactions in one signaling network, and amplification-mediated deregulation of this network was found in 30.0% of these preinvasive lesions. No such alterations were found in 14 low-grade dysplasias that did not progress, whereas 43.5% (10/23) of OSCCs were found to have altered genes within the pathways with DNA amplification. Multitarget FISH showed that amplification of EGFR and CCND1 can coexist in single cells of an oral dysplasia, suggesting the dependence on multiple oncogenes for OPL progression. Taken together, these findings identify a critical biological network that is frequently disrupted in high-risk OPLs, with different specific genes disrupted in different individuals.
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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 | 0.001 | 0.001 |
| 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".