Screening and identification of specific markers for bladder transitional cell carcinoma from urine urothelial cells with suppressive subtractive hybridization and cDNA microarray
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to screen and identify differentially expressed genes in invasive bladder transitional cell carcinoma (BTCC). METHODS: Voided urine samples were collected from consecutive patients with BTCC and patients under surveillance for bladder cancer recurrence; voided urine samples from patients with non-malignant diseases served as control. We identified the differentially expressed genes by comparing urine samples of bladder carcinoma to that of the control group with suppressive subtractive hybridization (SSH) and cDNA microarray. The differentially expressed genes were verified by quantitative real-time polymerase chain reaction (QPCR). RESULTS: From the 762 white colonies, a total of 449 positive clones were obtained in which 112 were found to be upregulated in BTCC. Sequencing and homology analysis were performed for these 112 clonies. The detection rates of some known genes (including IGF-1, human telomerase reverse transcriptase [hTERT], bladder cancer specific nuclear matrix protein 4 [BLCA-4] and homeobox A13 [HOXA13]) for BTCC at the Ta, T1 and >T1 stages were 48%, 90% and 100%, respectively, with a specificity of 85%. The test specificity was 80% for the 30 control patients with urinary tract infections. The combination of BLCA-4 and HOXA13 could distinguish between low- and high-grade tumours, with specificity and sensitivity of 80%. CONCLUSION: We successfully constructed a reliable SSH library of BTCC and found that combination detection insulin-like growth factor 1 (IGF-1), hTERT, BLCA-4 and HOXA13 genes could help to evaluate BTCC at different stages.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".