Multi‐omics analysis of tumor mutation burden combined with immune infiltrates in bladder urothelial carcinoma
Bibliographic record
Abstract
To explore the prognosis of tumor mutation burden (TMB) and underlying relationships with tumor-infiltrating immune cells in bladder cancer (BLCA). Transcriptome profiles and somatic mutation data from The Cancer Genome Atlas database by the GDC tool. A total of 437 samples were included, consisted of 412 BLCA patients and matched 25 normal samples. Specific mutation information was summarized and illustrated in waterfall plot. Higher TMB levels revealed improved overall survival (OS) and lower tumor recurrence. We found 68 differentially expressed genes in two TMB groups and identified eight independent hub TMB-related signature. Pathway analysis suggested that differential TMB-related signature correlated with multiple cancer-related crosstalk, including cell cycle, DNA replication, cellular senescence, and p53 signaling pathway. Besides, the tumor mutation burden related signature (TMBRS) model based on eight signature possessed well predictive value with area under curve (AUC) = 0.753, and patients with higher TMBRS scores showed worse OS outcomes (p < .001). Moreover, we exhibited the inferred immune cell fractions in box plot and differential abundance of immune cells were shown in the heatmap. The Wilcoxon rank-sum test suggested that CD8+ T cell (p = .001) and memory activated CD4+ T cell (p = .004) showed higher infiltrating levels in high-TMB group, while the density of resting mast cells showed lower infiltrating level in high-TMB group (p = .016). Finally, it is significant to note that CD8+ T cell and memory activated CD4+ T cell subsets not only revealed higher infiltrating abundance in high-TMB group but correlated with prolonged OS and lower risk of tumor recurrence, respectively.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".