Associations of genetically determined circulating proteins with breast cancer risk or survival
Bibliographic record
Abstract
Abstract Background There are few large-scale studies that focus on the associations between circulating proteins and breast cancer (BC) risk or survival. This study aimed to evaluate the potential circulating proteins associated with BC risk or survival using the Mendelian randomization (MR) method. Methods We collected the protein quantitative trait locus (pQTL) data of 4,907 circulating proteins from the DeCODE study (n = 35,559) as exposures. We gathered the genome wide association study (GWAS) data of BC from BCAC (OncoArray, n = 138,508) and BCAC (iCOGS, n = 76,167). The FinnGen study (n = 224,737) as the outcomes. The BC survival data was obtained from BCAC (OncoArray, n = 91,686). We used two sample MR framework to assess the associations between genetically predictive proteins and BC risk. Besides strict quality control, sensitivity tests and false discovery rate (FDR) or bonferroni correction, we further performed meta-analysis to ensure the robustness of the results. Results Four proteins—SIA4B (OR = 0.58, 95% CI (confidence interval): 0.51–0.64), CDH1 (OR = 0.83, 95% CI: 0.77–0.89), ALPI (OR = 0.91, 95% CI: 0.90–0.93) and CCDC134 (OR = 0.84, 95% CI: 0.80–0.88) are associated with reduced BC risk. 57 circulating proteins passed the sensitivity test and causally associated with BC survival. Conclusions Genetically predicted four circulating proteins (SIA4B, CDH1, ALPI and, CCDC134) are associated with reduced BC risk. 57 proteins are associated with BC survival. Our analyses from genetics and MR provide insights into the causes of BC and add evidence for reducing the risk of BC.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".