The serine protease inhibitor serpinE2 is a novel target of ERK signaling involved in human colorectal tumorigenesis
Bibliographic record
Abstract
BACKGROUND: Among the most harmful of all genetic abnormalities that appear in colorectal cancer (CRC) development are mutations of KRAS and its downstream effector BRAF as they result in abnormal extracellular signal-related kinase (ERK) signaling. In a previous report, we had shown that expression of a constitutive active mutant of MEK1 (caMEK) in normal rat intestinal epithelial cells (IECs) induced morphological transformation associated with epithelial to mesenchymal transition, growth in soft agar, invasion and metastases in nude mice. Results from microarrays comparing control to caMEK-expressing IECs identified the gene encoding for serpinE2, a serine protease inhibitor, as a potential target of activated MEK1. RESULTS: 1- RT-PCR and western blot analyses confirmed the strong up-regulation of serpinE2 expression and secretion by IECs expressing oncogenic MEK, Ras or BRAF. 2- Interestingly, serpinE2 mRNA and protein were also markedly enhanced in human CRC cells exhibiting mutation in KRAS and BRAF. 3- RNAi directed against serpinE2 in caMEK-transformed rat IECs or in human CRC cell lines HCT116 and LoVo markedly decreased foci formation, anchorage-independent growth in soft agarose, cell migration and tumor formation in nude mice. 4- Treatment of CRC cell lines with U0126 markedly reduced serpinE2 mRNA levels, indicating that expression of serpinE2 is likely dependent of ERK activity. 5- Finally, Q-PCR analyses demonstrated that mRNA levels of serpinE2 were markedly increased in human adenomas in comparison to healthy adjacent tissues and in colorectal tumors, regardless of tumor stage and grade. CONCLUSIONS: Our data indicate that serpinE2 is up-regulated by oncogenic activation of Ras, BRAF and MEK1 and contributes to pro-neoplastic actions of ERK signaling in intestinal epithelial cells. Hence, serpinE2 may be a potential therapeutic target for colorectal cancer treatment.
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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) | 0.001 | 0.001 |
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".