An N-terminal truncated carboxypeptidase E splice isoform induces tumor growth and is a biomarker for predicting future metastasis in human cancers
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
Metastasis is a major cause of mortality in cancer patients. However, the mechanisms governing the metastatic process remain elusive, and few accurate biomarkers exist for predicting whether metastasis will occur, something that would be invaluable for guiding therapy. We report here that the carboxypeptidase E gene (CPE) is alternatively spliced in human tumors to yield an N-terminal truncated protein (CPE-ΔN) that drives metastasis. mRNA encoding CPE-ΔN was found to be elevated in human metastatic colon, breast, and hepatocellular carcinoma (HCC) cell lines. In HCC cells, cytosolic CPE-ΔN was translocated to the nucleus and interacted with histone deacetylase 1/2 to upregulate expression of the gene encoding neural precursor cell expressed, developmentally downregulated gene 9 (Nedd9)--which has been shown to promote melanoma metastasis. Nedd9 upregulation resulted in enhanced in vitro proliferation and invasion. Quantification of mRNA encoding CPE-ΔN in HCC patient samples predicted intrahepatic metastasis with high sensitivity and specificity, independent of cancer stage. Similarly, high CPE-ΔN mRNA copy numbers in resected pheochromocytomas/paragangliomas (PHEOs/PGLs), rare neuroendocrine tumors, accurately predicted future metastasis or recurrence. Thus, CPE-ΔN induces tumor metastasis and should be investigated as a potentially powerful biomarker for predicting future metastasis and recurrence in HCC and PHEO/PGL patients.
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 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 | 0.001 |
| 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) | 0.001 | 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".