Unraveling the function of epithelial-mesenchymal transition (EMT) in colorectal cancer: Metastasis, therapy response, and revisiting molecular pathways
Bibliographic record
Abstract
Colorectal cancer (CRC) is a dangerous form of cancer that affects the gastrointestinal tract. It is a major global health concern, and the aggressive behavior of tumor cells makes it difficult to treat, leading to poor survival rates for patients. One major challenge in treating CRC is the metastasis, or spread, of the cancer, which is a major cause of death. In order to improve the prognosis for patients with CRC, it is necessary to focus on ways to inhibit the cancer's ability to invade and spread. Epithelial-mesenchymal transition (EMT) is a process that is linked to the spread of cancer cells, also known as metastasis. The process transforms epithelial cells into mesenchymal ones, increasing their mobility and ability to invade other tissues. This has been shown to be a key mechanism in the progression of colorectal cancer (CRC), a particularly aggressive form of gastrointestinal cancer. The activation of EMT leads to increases in the spread of CRC cells, and during this process, levels of the protein E-cadherin decrease while levels of N-cadherin and vimentin increase. EMT also contributes to the development of resistance to chemotherapy and radiation therapy in CRC. Non-coding RNAs, such as long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs), play a role in regulating EMT in CRC, often through their ability to "sponge" microRNAs. Anti-cancer agents have been shown to suppress EMT and reduce the progression and spread of CRC cells. These findings suggest that targeting EMT or related mechanisms may be a promising approach for treating CRC patients in the clinic.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".