Whole-Exome Sequencing Identified Mutational Profiles of Urothelial Carcinoma post Kidney Transplantation
Bibliographic record
Abstract
Abstract Background: Kidney transplantation is a lifesaving option of selected patients with end-stage kidney disease (ESKD). Urothelial carcinoma (UC) is the most common de novo cancer after kidney transplantation in Taiwan. UC has the characteristics of high mutational burden and elevated degree of molecular heterogeneity compared with other solid tumors. However, there are limited research exploring the genomic alterations in UC after kidney transplantation. Methods: In this study, we performed whole exome sequencing (WES) to compare the genetic alterations between UC developed after kidney transplantation (UCKT) and UC from hemodialysis patients (UCHD). After mapping and variant calling, a total of 18,733 and 11,093 mutations were identified from UCKT and UCHD patients, respectively. We first excluded known SNPs and then retained genes that were annotated in Cosmic, Intogen, and TCGA bladder databases for onco-driver genes. Seventeen UCKT unique genes with recurrent SNPs in more than two patients were subjected for further analysis. IPA pathway analysis was used to explore the interconnections among these genes. Results: 17 novel mutations of BTK, CARD11, ELL, FNBP1, GNAQ, HOXD13, IKZF1, MAX, MLLT10, NTRK3, PAX5, SEPTIN6, SEPTIN9, SH3GL1, SLC34A2, TAL1 , and TRAF7 were identified UCKT groups, and none of the genes had been reported in the genomes of patients with UC. Among the affected genes, GNAQ, IKZF1, and NTRK3 were potentially involved in the signaling network of UCKT. Conclusion : Our findings provide information for understanding the mutational landscape of UC developed after kidney transplantation, and a gene list that may be of great importance in related molecular mechanisms. Genetic analysis in malignancy after transplantation may help to identify high risk patients in post-transplant care and also illuminate a fundamental aspect of the molecular pathogenesis of post-transplantation UC.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".