Analysis of Pancreatic Cancer Genetic Risk Factors in a Multi-Ethnic Population Sample
Bibliographic record
Abstract
Background: Pancreatic cancer (PC) has one of the highest mortality to incidence ratio of all cancers. Early identification of at-risk individuals should permit early diagnosis. Genome-wide association studies showed the association of several genetic variants with PC risk in multi-ethnic populations. Our objective was to examine the association of these genetic variants with PC in a population sample from Kuwait. Methods: DNA samples from 103 pancreatic ductal adenocarcinoma (PDAC) specimens and 132 healthy controls were used for genotyping ABO rs505922, BCAR1 rs7190458, LINC-PINT rs6971499, HNF1B rs4795218, VDR rs2228570 rs731236, and PRSS1 rs111033565 rs111033568 rs387906698 and rs267606982 using TaqMan genotyping assays, and VDR expression was performed by immunocytochemistry. Results: ABO rs505922C and VDR rs2228570A were associated with PDAC risk (odds ratio (OR): 1.55, 95% confidence interval (CI): 1.07 - 2.24, P = 0.027; OR: 1.64, 95% CI: 1.09 - 2.48, P = 0.024; respectively). An unweighted polygenic risk score ( ABO rs505922, BCAR1 rs7190458, LINC-PINT rs6971499, and HNF1B rs4795218) was significantly associated with PDAC risk (¦A: -0.11, 95% CI: -0.15 to -0.05, P < 0.001). VDR expression was downregulated or absent in most PDAC specimens regardless of VDR haplotype. Conclusion: ABO rs505922C and VDR rs2228570A are PDAC genetic risk factors in our population. Ethnicity influences the association of reported genetic PDAC risk factors and should be adjusted for when performing PDAC genetic risk estimations. Investigation of these genetic risk factors in other ethnic populations is a necessity to evaluate their PDAC risk prediction potential. World J Oncol. 2024;15(5):792-800 doi: https://doi.org/10.14740/wjon1911
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".