Serum Proteomic Approach for the Identification of Serum Biomarkers Contributed by Oral Squamous Cell Carcinoma and Host Tissue Microenvironment
Bibliographic record
Abstract
The lack of serum biomarkers for head and neck carcinoma limits early diagnosis, monitoring of advanced disease, and prediction of relapses in patients. We conducted a comprehensive proteomics study on serum from mice bearing orthotopic human oral squamous cell carcinomas (OSCC) with distinct invasive phenotypes. Matched established cell lines were transplanted orthotopically into tongues of RAG-2/gamma(c) mice and mouse serum was analyzed by 2-dimensional-differential gel electrophoresis(2D-DIGE)/liquid chromatography (LC)-MS/MS and by online 2D-LC-MS/MS of iTRAQ labeled samples. We identified several serum proteins as being differentially expressed between control and cancer-bearing mice and between noninvasive and invasive cancer (p<0.05). Differentially expressed proteins of human origin included the epidermal growth factor receptor (EGFR), cytokeratins, G-protein coupled receptor 87, Rab11 GTPase, PDZ-domain containing proteins, and PEST-containing nuclear proteins. Identified proteins of mouse origin included clusterin, titin, vitronectin, vitamin D-binding protein, hemopexin, and kininogen I. The levels of serum and cell secreted EGFR were further validated to match proteomic data regarding the inverse correlation with the invasive phenotype. In summary, we report a comprehensive patient-based proteomics approach for the identification of potential serum biomarkers for OSCC using an orthotopic xenograft mouse model.
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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 | 0.000 |
| 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 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".