Loss of NFE2L3 protects against inflammation-induced colorectal cancer through modulation of the tumor microenvironment
Bibliographic record
Abstract
Abstract We investigated the role of the NFE2L3 transcription factor in inflammation-induced colorectal cancer. Our studies revealed that Nfe2l3−/− mice exhibit significantly less inflammation in the colon, reduced tumor size and numbers, and skewed localization of tumors with a more pronounced decrease of tumors in the distal colon. CIBERSORT analysis of RNA-seq data from normal and tumor tissue predicted a reduction in mast cells in Nfe2l3−/− animals, which was confirmed by toluidine blue staining. Concomitantly, the transcript levels of Il33 and Rab27a, both important regulators of mast cells, were reduced and increased, respectively, in the colorectal tumors of Nfe2l3−/− mice. Furthermore, we validated NFE2L3 binding to the regulatory sequences of the IL33 and RAB27A loci in human colorectal carcinoma cells. Using digital spatial profiling, we found that Nfe2l3−/− mice presented elevated FOXP3 and immune checkpoint markers CTLA4, TIM3, and LAG3, suggesting an increase in Treg counts. Staining for CD3 and FOXP3 confirmed a significant increase in immunosuppressive Tregs in the colon of Nfe2l3−/− animals. Also, Human Microbiome Project (HMP2) data showed that NFE2L3 transcript levels are higher in the rectum of ulcerative colitis patients. The observed changes in the tumor microenvironment provide new insights into the molecular differences regarding colon cancer sidedness. This may be exploited for the treatment of early-onset colorectal cancer as this emerging subtype primarily displays distal/left-sided tumors.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".