Primary renal well‐differentiated neuroendocrine tumour (carcinoid): next‐generation sequencing study of 11 cases
Bibliographic record
Abstract
AIMS: Primary renal well-differentiated neuroendocrine tumour (NET) (hereafter referred to as renal NET) is rare, with ~100 cases having been reported in the literature. There are also limited data on the molecular-genetic background of primary renal NETs. METHODS AND RESULTS: We analysed 11 renal NETs by using next-generation sequencing (NGS) to identify characteristic genetic aberrations. All tumours were positive for synaptophysin, and also expressed insulinoma-associated protein 1 (10/11), chromogranin-A (8/11), and CD56 (3/11). Cytoplasmic positivity of CD99 was present in eight of 11 cases, and strong nuclear expression of α-thalassaemia/mental retardation syndrome X-linked (ATRX) was retained in all 11 cases. Molecular-genetic analysis of aberration of VHL gave negative results in all cases. Loss of heterozygosity on chromosome 3p21 was found in three of nine analysable cases. NGS was successful in nine cases, showing a total of 56 variants being left after the updated filtering process, representing an average of five variants per sample. All analysable cases were negative for ATRX and DAXX (death-domain associated protein X) mutations. The most frequently mutated genes were CDH1 and TET2, with three mutations in two cases. Mutations in AKT3, ROS1, PIK3R2, BCR and MYC were found in two cases. The remaining 41 genes were found to be mutated only in individual cases. In four cases, the mutations affected a subset of genes related to angiogenesis. CONCLUSIONS: Overall, the mutation profile of primary renal NETs is variable, and none of the studied genes or affected pathways seems to be specific for renal NET.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".