Serum Proteomic Approach for the Identification of Serum Biomarkers Contributed by Oral Squamous Cell Carcinoma and Host Tissue Microenvironment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.004 | 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 it