Genome-wide Meta-analysis Identifies Novel Genes Associated with Recurrence and Progression in Non–muscle-invasive Bladder Cancer
Bibliographic record
Abstract
Non–muscle-invasive bladder cancer (NMIBC) is characterized by frequent recurrences and a risk of progression in stage and grade. Increased knowledge of underlying biological mechanisms is needed. To identify single nucleotide polymorphisms (SNPs) associated with recurrence-free (RFS) and progression-free (PFS) survival in NMIBC. We analyzed outcome data from 3400 newly diagnosed NMIBC patients from the Netherlands, the UK, Canada, and Spain. We generated genome-wide germline SNP data using Illumina OmniExpress and Infinium Global Screening Array in combination with genotype imputation. Cohort-specific genome-wide association studies (GWASs) for RFS and PFS were performed using a Cox proportional hazard model. Results were combined in a fixed-effect inverse-variance weighted meta-analysis. Candidate genes for the identified SNP associations were prioritized using functional annotation, gene-based analysis, expression quantitative trait locus analysis, and transcription factor binding site databases. Tumor expression levels of prioritized genes were tested for association with RFS and PFS in an independent NMIBC cohort. This meta-analysis revealed a genome-wide significant locus for RFS on chromosome 14 (lead SNP rs12885353, hazard ratio [HR] C vs T allele 1.55, 95% confidence interval [CI] 1.33–1.82, p = 4.0 × 10–8), containing genes G2E3 and SCFD1. Higher expression of SCFD1 was associated with increased RFS (HR 0.70, 95% CI 0.59–0.84, pFDR = 0.003). Twelve other loci were suggestively associated with RFS (p < 10–5), pointing toward 18 additional candidate genes. For PFS, ten loci showed suggestive evidence of association, indicating 36 candidate genes. Expression levels of ten of these genes were statistically significantly associated with PFS, of which four (IFT140, UBE2I, FAHD1, and NME3) showed directional consistency with our meta-analysis results and published literature. In this first prognostic GWAS in NMIBC, we identified several novel candidate loci and five genes that showed convincing associations with recurrence or progression. In this study, we searched for inherited DNA changes that affect the outcome of non–muscle-invasive bladder cancer (NMIBC). We identified several genes that are associated with disease recurrence and progression. The roles and mechanisms of these genes in NMIBC prognosis should be investigated in future studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| 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 | 0.001 |
| 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 teacher head, 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".