<scp>S</scp>erum‐derived carcinoembryonic antigen (<scp>CEA</scp>) activates fibroblasts to induce a local re‐modeling of the extracellular matrix that favors the engraftment of <scp>CEA</scp>‐expressing tumor cells
Bibliographic record
Abstract
Elevated levels of the carcinoembryonic antigen (CEA; CEACAM5) in the serum of colorectal cancer (CRC) patients represent a clinical biomarker that correlates with disease recurrence. However, a mechanistic role for soluble CEA (sCEA) in tumor progression and metastasis remains to be established. In our study, we report that sCEA acts as a paracrine factor, activating human fibroblasts by signaling through both the STAT3 and AKT1‐mTORC1 pathways, promoting their transition to a cancer‐associated fibroblast (CaF) phenotype. sCEA‐activated fibroblasts express and secrete higher levels of fibronectin, including cellular EDA+‐fibronectin (Fn‐EDA) that selectively promote the implantation and adherence of CEA‐expressing cancer cells. Immunohistochemical analyses of liver tissues derived from CRC patients with elevated levels of sCEA reveal that the expression of cellular Fn‐EDA co‐registers with CEA‐expressing liver metastases. Taken together, these findings indicate a direct role for sCEA as a human fibroblast activation factor, in priming target tissues for the engraftment of CEA‐expressing cancer cells, through the differentiation of tissue‐resident fibroblasts, resulting in a local change in composition of the extracellular matrix.
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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".