Upregulation of IFNɣ-mediated chemokines dominate the immune transcriptome of muscle-invasive urothelial carcinoma
Bibliographic record
Abstract
Tumor inflammation is prognostically significant in high-grade muscle-invasive bladder cancer (MIBC). However, the underlying mechanisms remain elusive. To identify inflammation-associated immune gene expression patterns, we performed transcriptomic profiling of 40 MIBC archival tumors using the NanoString nCounter Human v.1.1 PanCancer Panel. Findings were validated using the TCGA MIBC dataset. Unsupervised and supervised clustering identified a distinctive immune-related gene expression profile for inflammation, characterized by significant upregulation of 149 genes, particularly chemokines, a subset of which also had potential prognostic utility. Some of the most enriched biological processes were lymphocyte activation and proliferation, leukocyte adhesion and migration, antigen processing and presentation and cellular response to IFN-γ. Upregulation of numerous IFN-γ-inducible chemokines, class II MHC molecules and immune checkpoint genes was detected as part of the complex immune response to MIBC. Further, B-cell markers linked to tertiary lymphoid structures were upregulated, which in turn is predictive of tumor response to immunotherapy and favorable outcome. Our findings of both an overall activated immune profıle and immunosuppressive microenvironment provide novel insights into the complex immune milieu of MIBC with inflammation and supports its clinical significance for predicting prognosis and immunotherapeutic responsiveness, which warrants further investigation. This may open novel opportunities to identify mechanisms for developing new immunotherapeutic strategies.
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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 | 0.000 | 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".