Novel Blood Biomarkers of Human Urinary Bladder Cancer
Bibliographic record
Abstract
PURPOSE: Recent data indicate that cDNA microarray gene expression profile of blood cells can reflect disease states and thus have diagnostic value. We tested the hypothesis that blood cell gene expression can differentiate between bladder cancer and other genitourinary cancers as well as between bladder cancer and healthy controls. EXPERIMENTAL DESIGN: We used Affymetrix U133 Plus 2.0 GeneChip (Affymetrix, Santa Clara, CA) to profile circulating blood total RNA from 35 patients diagnosed with one of three types of genitourinary cancer [bladder cancer (n = 16), testicular cancer (n = 10), and renal cell carcinoma (n = 9)] and compared their cDNA profiles with those of 10 healthy subjects. We then verified the expression levels of selected genes from the Affymetrix results in a larger number of bladder cancer patients (n = 40) and healthy controls (n = 27). RESULTS: Blood gene expression profiles distinguished bladder cancer patients from healthy controls and from testicular and renal cancer patients. Differential expression of a combined set of seven gene transcripts (insulin-like growth factor-binding protein 7, sorting nexin 16, chondroitin sulfate proteoglycan 6, and cathepsin D, chromodomain helicase DNA-binding protein 2, nell-like 2, and tumor necrosis factor receptor superfamily member 7) was able to discriminate bladder cancer from control samples with a sensitivity of 83% (95% confidence interval, 67-93%) and a specificity of 93% (95% confidence interval, 76-99%). CONCLUSION: We have shown that the gene expression profile of circulating blood cells can distinguish bladder cancer from other types of genitourinary cancer and healthy controls and can be used to identify novel blood markers for bladder cancer.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".