Proteogenomic and observational evidence implicate ANGPTL4 as a potential therapeutic target for colorectal cancer prevention
Bibliographic record
Abstract
BACKGROUND: The role of lipid-perturbing medications in cancer risk is unclear. METHODS: We employed cis-Mendelian randomization and colocalization to evaluate the role of 5 lipid-perturbing drug targets (ANGPTL3, ANGPTL4, APOC3, CETP, and PCSK9) in risk of 5 cancers (breast, colorectal, head and neck, ovarian, and prostate). We triangulated findings using pre-diagnostic protein measures in prospective analyses in EPIC (977 colorectal cancer cases, 4080 sub-cohort members) and the UK Biobank (860 colorectal cancer cases, 50 177 controls). To gain mechanistic insight into the role of ANGPTL4 in carcinogenesis, we examined the impact of the ANGPTL4 p. E40K loss-of-function variant on differential gene expression in normal colon tissue in BarcUVa-Seq. Finally, we evaluated the association of colon tumor ANGPTL4 expression with cancer-specific mortality in TCGA. RESULTS: In analysis of 78 473 cases and 107 143 controls, genetically proxied circulating ANGPTL4 inhibition was associated with reduced colorectal cancer risk (ORSD decrease = 0.76, 95% confidence interval [CI] = 0.66 to 0.89, P = 5.52 × 10-4, PPcolocalization = 0.83). This association was replicated using pre-diagnostic circulating ANGPTL4 concentrations in EPIC (hazard ratio [HR]log10 decrease = 0.91, 95% CI = 0.84 to 0.98, P = .01) and the UK Biobank (HRSD decrease = 0.93, 95% CI = 0.86 to 0.99, P = .03). In gene-set enrichment analysis of differential gene expression in 445 colon tissue samples, ANGPTL4 loss-of-function down-regulated several cancer-related biological pathways (PFDR < .05), including those involved in cellular proliferation, epithelial-to-mesenchymal transition, and bile acid metabolism. In analysis of 465 colon cancer patients, lower ANGPTL4 tumor expression was associated with reduced colorectal cancer-specific mortality risk (HRlog2 decrease = 0.66, 95% CI = 0.50 to 0.87, P = 2.92 × 10-3). CONCLUSIONS: Our integrative proteogenomic and observational analyses suggest a potential protective role of lower circulating ANGPTL4 concentrations in colorectal cancer risk. These findings support further evaluation of ANGPTL4 as a therapeutic target for colorectal cancer prevention.
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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 | 0.003 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".