NanoVelcro-captured CTC number concomitant with enhanced serum levels of MMP7 and MMP9 enables accurate prediction of metastasis and poor prognosis in patients with lung adenocarcinoma
Bibliographic record
Abstract
Lung adenocarcinoma (LADC) is among the most malignant cancers that frequently develops micrometastases even in early stages of the disease. Circulating tumor cell (CTC) number, matrix metalloproteinase (MMP) 7, and MMP9 show great prospects as predictive biomarkers in many tumors. However, the interactions between these biomarkers and the molecular basis of their roles in the metastasis and prognosis of LADC remain unclear. The present study revealed that an elevated CTC count and overexpression of MMP7 and MMP9 correlate with metastasis and clinical progression in LADC patients (n=143). Furthermore, MMP7 and MMP9 upregulation facilitates LADC cell migration in vitro and enhances serum CTC levels in a xenograft mouse model. More importantly, receiver operating characteristic (ROC) curves and Kaplan-Meier analysis confirmed more accurate prediction of metastasis and overall survival (OS) with a combination panel of CTC, MMP7, and MMP9. Taken together, our data show, for the first time, the involvement of MMP7 and MMP9 in the release of CTCs into the peripheral blood, and our data reveal that CTC count and expression of MMP7 and MMP9 can be used together as an effective clinical prediction panel for LADC metastasis and prognosis.
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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) | 0.001 | 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".