Identification of DEK and E2F3 as candidate 6p22 oncogenes in retinoblastoma
Bibliographic record
Abstract
The study of retinoblastoma, the malignant tumor of the retina, has set the fundamentals of cancer genetics and the genetics of familial cancer syndromes through discovery of the first tumor suppressor gene RB1. Retinoblastoma is an excellent disease model of the multistep nature of cancer. The initiation events, leading to the loss of function of both alleles of the RBI gene, hence named mutations 1 and 2 (Ml and M2) are well characterized, as well as the number of recurrent chromosomal aberrations that are positively selected for during tumor progression. Recurrent regions of chromosomal gain and loss are hypothesized to carry oncogenes and tumor suppressor genes, respectively, which are targeted by mutational events M3-Mn. One of the most frequently gained chromosomal regions is the short arm of chromosome 6, 6p, with the minimal region of gain mapping to the chromosomal band 6p22. In this thesis, through comparative expression analysis of retinoblastoma and healthy retina, the number of candidate 6p22 oncogenes is narrowed to two genes, DEK and E2F3. The functional analysis of the oncogenic potential of DEK and E2F3 in retinoblastoma cell lines through RNA interference shows that both genes display oncogenic properties, since the knockdown of any of the two adversely affects the growth of retinoblastoma when 6p22 genomic gain is present. In addition, it is shown that, besides increase in the genomic copy number of 6p22, some retinoblastoma cell lines exhibit translocations between chromosomal arms 6p and 6q, with a recurrent translocation breakpoint at 6p22, in the vicinity of DEK and E2F3 loci, emphasizing the involvement of chromosomal band 6p22 in the etiology of retinoblastoma. Based on the work presented. DEK and E2F3 are the promising new targets for treatment and prevention of retinoblastoma.
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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 | 0.000 |
| 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".