Bibliographic record
Abstract
Initiation of transcription for ribosomal RNA (rRNA) by RNA polymerase I requires TATA-binding protein (TBP) and TBP-associated factors (TAF1A, TAF1B and TAF1C). p53 tumour suppressor inhibits rRNA transcription by blocking TAF1C-UBF interaction, but alterations of TAF1C itself in tumorigenesis remain unknown. The aim of this study was to explore whether TAF1C gene was mutated in gastric (GC) and colorectal cancers (CRC).In a public database, we found that TAF1C gene had a mononucleotide repeat (C8) in the coding sequences that might be a mutation target in the cancers with microsatellite instability (MSI). We analysed 79 GC and 124 CRC by single-strand conformation polymorphism and DNA sequencing analyses. In this study, we found TAF1C frameshift mutations (8.8% of GC and 10.1% of CRC with MSI-H), which were not found in stable MSI/low MSI (MSS/MSI-L) (0/90). In addition, we analysed intratumoural heterogeneity (ITH) of TAF1C frameshift mutations in 16 CRC and found that three CRC (18.8%) harboured regional ITH of the TAF1C frameshift mutations. Our results indicate that TAF1C gene harboured not only somatic frameshift mutations but also the mutational ITH, which together might play a role in tumourigenesis of GC and CRC. Our data also suggest that multi-regional mutation analysis is needed for a better evaluation of the mutation status in CRC.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".