A Cell Proteomic Approach for the Detection of Secretable Biomarkers of Invasiveness in Oral Squamous Cell Carcinoma
Bibliographic record
Abstract
OBJECTIVE: To identify potential biomarkers of invasiveness in oral squamous cell carcinoma. DESIGN: A pilot proteomic study for the identification of secreted and cleaved proteins that can serve as potential biomarkers for head and neck carcinoma invasiveness. SUBJECTS: Two primary cell lines and their variants were established from 2 oral squamous cell carcinoma human tissue samples with distinct invasive phenotypes. The cell lines were confirmed to maintain the invasive capacity of the original cancer when implanted into the tongues of immunocompromised RAG-2/gamma(c) mice. INTERVENTIONS: Invasiveness was assessed by the capacity of cells to invade through a matrigel matrix using the Boyden chamber assay and correlated with the invasiveness seen clinically and histologically in patients. In parallel, cell lines were grown in serum-free conditioned medium, which was then used to identify secreted and/or cleaved proteins that emanate from cancer cells, using 2-dimensional gel electrophoresis and matrix-assisted laser desorption-ionization combined with tandem mass spectrometry. RESULTS: The invasion assays revealed a correlation between cell migration capacity through matrigel matrix and the aggressive phenotype seen in the clinical and histopathological assessments. More than 50 proteins were identified as being differentially secreted in media between the least and the more aggressive cell lines (P < .05). These include proteins that regulate cell metabolism, cell structure, cell adhesion, and cell motility, as well as proteins with undefined function. CONCLUSIONS: We report a sensitive and clinically relevant approach to screen for secreted biomarkers of oral squamous cell carcinoma invasiveness using proteomic technology. Both high- and low-abundant secreted proteins were identified and can represent potential biomarkers for oral cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".