Abstract 3337: Unveiling m6A epitranscriptome-related genomic variations in bladder cancer: predictive biomarkers for disease progression and treatment outcome towards tailored therapeutics
Bibliographic record
Abstract
Abstract Due to its highly heterogenous molecular landscape, bladder cancer (BlCa) is still characterized by non-personalized prognosis and treatment decisions. N6-methyladenosine (m6A) has emerged as the most common and conserved internal mRNA modification, regulating RNA metabolism and translation. Herein, we have profiled mutations and copy number variations (CNVs) within m6A RNA machinery genes and assessed their clinical relevance in BlCa patients’ prognosis and treatment outcome. DNA-seq libraries were prepared from 96 bladder specimens (tumors: n=87; normal urothelium: n=9) using a custom-designed panel of enrichment probes for m6A writers (METTL3, METTL14, METTL16, VIRMA, WTAP, RBM15, RBM15B, ZC3H13), erasers (FTO, ALKBH5) and readers (YTHDF1, YTHDF2, YTHDF3, YTHDC1, YTHDC2, IGF2BP1, IGF2BP2, IGF2BP3). METTL3 methyltransferase expression was quantified by RT-qPCR. Mutational and CNV analyses of DNA-seq data were performed by SNPNexus and CNVkit, respectively. Kaplan-Meier curves and Cox regression analysis were implemented for patient’s survival analysis. Internal validation was performed by bootstrap analysis. DNA-seq on DNBSEQ-G400 revealed that ∼30% of bladder tumors (25/87) harbored deleterious mutations in m6A writers, while m6A erasers (3,2%) and readers (5,3%) were less mutated. CNVs were found in ∼60% of tumors (52/87), with VIRMA frequently amplified (22%) in superficial (TaT1) tumors. Reduced METLL3 expression was detected in muscle-invasive tumors (p=0.009) and associated with increased risk for NMIBC relapse (p=0.002) and progression (p=0.028) to MIBC. Moreover, MIBC patients with deleterious mutations in m6A writer’s complex presented significantly higher progression risk (p=0.036) and worse survival (p=0.010), while VIRMA copy-number gain/amplification was associated with significantly increased risk of short-term relapse (p=0.019) in NMIBC. Finally, multivariate Cox regression confirmed m6A writer complex mutations as independent marker for patients’ progression (HR=3.242, p=0.021) and death (HR=5.388, p=0.006) in MIBC, and of VIRMA gain/amplification as an independent indicator of NMIBC relapse risk (HR=3.564, p=0.010).Genomic variations in m6A machinery emerge as modern molecular markers to address BlCa clinical heterogeneity and guide personalized prognosis and treatment/monitoring decisions. Acknowledgements: The research project was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the “2nd Call for H.F.R.I. Research Projects to support Faculty Members & Researchers” (Project Number: HFRI-FM20-3765) Citation Format: Andreas Scorilas, Katerina-Marina Pilala, Panagiotis Tsiakanikas, Konstantina Panoutsopoulou, Maria-Alexandra Papadimitriou, Konstantinos Soureas, Georgios-Christos Giagkos, Panagiotis Levis, Georgios Kotronopoulos, Zoi Kanaki, Ioannis Prassas, Lampros Dimitrakopoulos, George Yousef, Konstantinos Stravodimos, Stiliani Koroneou, Margaritis Avgeris. Unveiling m6A epitranscriptome-related genomic variations in bladder cancer: predictive biomarkers for disease progression and treatment outcome towards tailored therapeutics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3337.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".