Vitamin D supplementation improves the prognosis of patients with colorectal cancer liver metastases
Bibliographic record
Abstract
Abstract Colorectal cancer liver metastasis (CRCLM) is one of the deadliest cancers. CRCLM tumours have two distinct histopathological growth patterns (HGPs) including desmoplastic HGP (DHGP) and replacement HGP (RHGP). The DHGP tumours are angiogenic, while their RHGP counterparts are vessel co-opting. The patients with DHGP tumours have a better response to anti-angiogenic agents and chemotherapy, as well as the prognosis. To determine the influence of vitamin D supplementation in CRCLM, we analyzed the HGPs and the 5-year OS of CRCLM patients (n=106). Interestingly, we found an inverse correlation between vitamin D supplementation and the presence of RHGP tumours in CRCLM patients. Additionally, the 5-year OS of the patients that administered vitamin D was significantly higher. The cancer cells in RHGP lesions are characterized by direct contact with the hepatocytes, and this phenomenon enhances the motility of the cancer cells and facilitates their infiltration through liver parenchyma to co-opt the pre-existing vessels. Significantly, our in vitro data demonstrated the downregulation of motility markers in the co-cultured cancer cells with hepatocytes upon exposure to vitamin D. Altogether, this study highlights the role of vitamin D in CRCLM and provides a rationale to investigate the contribution of vitamin D supplementation to the prognosis of CRCLM patients.
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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 | 0.001 |
| 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) | 0.003 | 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".