Transcriptome-wide analysis of alternative splicing events in bladder cancer: Novel biomarkers discovery for early diagnosis.
Bibliographic record
Abstract
483 Background: Cystoscopy, an invasive, painful and expensive method, is currently the main clinical tool for new diagnosis and disease recurrence detection of bladder cancer (BCa). The need for new biomarkers discovery that is costless, sensitive and specific is urgent. This project aims to study and compare alternative splicing events (ASE) in BCa tissues and normal bladder tissues and ultimately, identify specific spliced events coding for proteins detectable in urine by liquid chromatography–mass spectrometry. Methods: In this study, alterations to the global RNA splicing landscape of cellular genes were investigated in a large-scale screen from 408 BCa tissues and 19 normal tissues provided by The Cancer Genome Atlas (TCGA). Three statistical thresholds were used to determine substantial modifications. All events showing a p-value<0.05 and a level of expression ≥ 50 transcripts per million; -10 ≥ Δ percent splice index ≤10; and a q-value<0.05 were conserved. Next, mRNA expression levels between cancer and normal tissues were compared for all splicing factors and the spliceosome to determine the impact of gene dysregulation on alternative splicing events. Using multiple bioinformatic platforms such as EASANA, MultAlin, ExPasy, NLS Mapper and Pfam, splicing events responsible for significant protein structural changes between cancer and healthy tissue were selected. From this sample chosen, ASEs coding for proteins that could be detected in urine were conserved. Results: Our study identifies modifications in the alternative splicing patterns of 107 transcripts encoded by 97 genes. STRING analysis revealed that many of the gene products interact either directly or indirectly with each other (enrichment p-value = 1x10-10). 61 ASEs are causing important protein changes from which 27 can be detected in urine. Finally, 16 ASEs coding for easily recognizable peptide sequences in urine represented significant targets for potential BCa biomarkers. Conclusions: The TCGA data show the relevance to investigate alternative splicing events in bladder cancer. 16 significant events were detectable in urine and may potentially discriminate between presence or absence of bladder cancer.
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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 | 0.000 |
| 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 | 0.000 | 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".