Acquiring Metastatic Competence by Oral Squamous Cell Carcinoma Cells Is Associated with Differential Expression of<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi mathvariant="bold">α</mml:mi></mml:math>-Tubulin Isoforms
Bibliographic record
Abstract
We performed comparative global proteomics analyses of patient-matched primary (686Tu) and metastatic (686Ln) OSCC cells. The metastatic OSCC 686Ln cells showed greater in vitro migratory/invasive potential and distinct cell shape from their parental primary 686Tu cells. Ettan DIGE analysis revealed 1316 proteins spots in both cell lines with >85% to be quantitatively similar (<2 folds) between the two cell lines. However, two protein spots among four serial spots were highly dominant in 686Ln cells. Mass spectrometry sequencing demonstrated all four spots to be α-tubulin isotypes. Further analysis showed no significant quantitative difference in the α-tubulin between the two cell lines either at mRNA or protein levels. Thus, two distinct isoforms of α-tubulin, probably due to posttranslational modification, were associated with metastatic 686Ln cells. Immunofluorescence demonstrated remarkable differences in the cytosolic α-tubulin distribution patterns between the two cells. In 686Tu cells, α-tubulin proteins formed a normal network composed of filaments. In contrast, α-tubulin in 686Ln cells exhibited only partial cytoskeletal distribution with the majority of the protein diffusely distributed within the cytosol. Since α-tubulin is critical for cell shape and mobility, our finding suggests a role of α-tubulin isoforms in acquisition of metastatic phenotype and represents potential target for therapeutic intervention.
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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) | 0.002 | 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".