A comprehensive analysis of SNPs and CNVs identifies novel markers associated with disease outcomes in colorectal cancer
Bibliographic record
Abstract
We aimed to examine the associations of a genome‐wide set of single nucleotide polymorphisms (SNPs) and 254 copy number variations (CNVs) and/or insertion/deletions (INDELs) with clinical outcomes in colorectal cancer patients ( n = 505). We also aimed to investigate whether their associations changed (e.g., appeared, diminished) over time. Multivariable Cox proportional hazards and piece‐wise Cox regression models were used to examine the associations. The Cancer Genome Atlas (TCGA) datasets were used for replication purposes and to examine the gene expression differences between tumor and nontumor tissue samples. A common SNP ( WBP11‐ rs7314075) was associated with disease‐specific survival with P ‐value of 3.2 × 10 −8 . Association of this region with disease‐specific survival was also detected in the TCGA patient cohort. Two expression quantitative trait loci (eQTLs) were identified in this locus that were implicated in the regulation of ERP27 expression. Interestingly, expression levels of ERP27 and WBP11 were significantly different between colorectal tumors and nontumor tissues. Three SNPs predicted the risk of recurrent disease only after 5 years postdiagnosis. Overall, our study identified novel variants, one of which also showed an association in the TCGA dataset, but no CNVs/INDELs, that associated with outcomes in colorectal cancer. Three SNPs were candidate predictors of long‐term recurrence/metastasis risk.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".