Associations between Calcium Intake and T-cell Infiltration in Colorectal Tumors
Bibliographic record
Abstract
Higher T-cell infiltration in colorectal tumors has been associated with better prognosis. Evidence indicates that calcium signaling is essential for T-cell functioning. However, as it is unknown whether calcium intake influences T-cell infiltration, we investigated the association of calcium intake with T-cell subsets in the tumor microenvironment of colorectal cancer. In total, 943 participants from three cohort studies, for which data on tumor-infiltrating T cells and calcium intake were available, were included for these analyses. Immune cell infiltration was quantified by digital image analyses with machine learning algorithms using a customized 9-plex multispectral immunofluorescence assay (CD3, CD4, CD8, CD45RA, CD45RO, FOXP3, KRT, MKI67, and di-(4-amidinophenyl)-1H-indole-6-carboxamidine). Associations between prediagnostic calcium intake and densities of nonoverlapping subsets of epithelial and stromal tissue area T cells were assessed using multivariable binary or ordinal logistic regression analyses. A higher dietary calcium intake was positively associated with CD3+CD4-CD8- double-negative T-cell density in the epithelial (OR, 1.57; 95% confidence interval, 1.13-2.24) and stromal (OR, 1.24; 95% confidence interval, 1.06-1.45) tumor tissue areas. No other statistically significant associations were observed after correcting for multiple testing. In conclusion, dietary calcium intake was associated with a higher density of CD4-CD8- double-negative T cells in the epithelial and stromal tumor tissue areas but not with the infiltration of CD4+ or CD8+ T cells. More research is needed to further unravel the role of calcium in tumor-immune profiles and associations with clinical outcomes. Our findings offer a promising basis for further research. PREVENTION RELEVANCE: Our research contributes to the understanding of how diet could influence immune cell infiltration in and around the tumor. Understanding which factors influence antitumor immune responses is of importance in the prevention of cancer recurrence and/or progression.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".