Dissecting the expression landscape of genitourinary cancers.
Bibliographic record
Abstract
573 Background: Genitourinary (GU) cancers include bladder urothelial carcinoma (BCa), prostate cancer, kidney clear cell or papillary carcinoma, uterus endometrial carcinoma, ovarian cystadenocarcinoma and testicular germ cell tumors. Clinical data show a higher prevalence of GU cancers for men than women, suggesting significant molecular differences depending on sex regarding cancer development. This project aims at dissecting the expression landscape for all GU cancers and identify significant dysregulated genes between cancer and normal tissues specific either for men and women. The resulting data will serve a clinical application consisting in targeting novel molecular biomarkers detected in urine for GU cancers early diagnosis. Methods: Based on RNAseq data provided by The Cancer Genome Atlas, gene expression fold-change for 19,123 genes from cancer tissues and normal tissues was initially compared for each GU cancer. To ensure significant gene dysregulation, only genes showing a fold-change ≤ -log2(2) or ≥ log2(2) and a p-value < 0.05 were retained. Next, a comparison in gene expression modification between men and women for each cancer was made to identify the most significant molecular differences between both sexes. Unique dysregulated genes for each cancer were then identified and their presence or absence in urine was confirmed. From the pool of genes expressing proteins in urine, non-parametric tests for paired and unpaired values were used to determine the statistical difference between cancer and normal tissues in terms of gene expression. Results: Among 2880 genes significantly dysregulated for all five GU cancers, more than half of them were unique to BCa. Among these, 129 gene products (proteins) were ultimately detected in urine of patients. Seven up-regulated and ten down-regulated genes have shown high statistical differences between cancer and normal tissues in the Mann-Whitney U test for unpaired values. CEACAM6, S100A8 and S100A9 have shown significant differences in the Wilcoxon test for paired values which make them potential biomarkers ready for validation by LC-MS/MS. Conclusions: This present study shows the relevance to pursue research in gene expression modification in cancer to improve early diagnosis.
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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".