Tumor expression of calcium sensing receptor and colorectal cancer survival: Results from the nurses’ health study and health professionals follow‐up study
Bibliographic record
Abstract
Although experimental evidence suggests calcium-sensing receptor (CASR) as a tumor-suppressor, the prognostic role of tumor CASR expression in colorectal carcinoma remains unclear. We hypothesized that higher tumor CASR expression might be associated with improved survival among colorectal cancer patients. We evaluated tumor expression levels of CASR by immunohistochemistry in 809 incident colorectal cancer patients within the Nurses' Health Study and the Health Professionals Follow-up Study. We used Cox proportional hazards regression models to estimate multivariable hazard ratio (HR) for the association of tumor CASR expression with colorectal cancer-specific and all-cause mortality. We adjusted for potential confounders including tumor biomarkers such as microsatellite instability, CpG island methylator phenotype, LINE-1 methylation level, expressions of PTGS2, VDR and CTNNB1 and mutations of KRAS, BRAF and PIK3CA. There were 240 colorectal cancer-specific deaths and 427 all-cause deaths. The median follow-up of censored patients was 10.8 years (interquartile range: 7.2, 15.1). Compared with patients with no or weak expression of CASR, the multivariable HRs for colorectal cancer-specific mortality were 0.80 [95% confidence interval (CI): 0.55-1.16] in patients with moderate CASR expression and 0.50 (95% CI: 0.32-0.79) in patients with intense CASR expression (p-trend = 0.003). The corresponding HRs for overall mortality were 0.85 (0.64-1.13) and 0.81 (0.58-1.12), respectively. Higher tumor CASR expression was associated with a lower risk of colorectal cancer-specific mortality. This finding needs further confirmation and if confirmed, may lead to better understanding of the role of CASR in colorectal cancer progression.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".