A Comprehensive Assessment of Genetic and Epigenetic Alterations Identifies Frequent Variations Impacting Six Prototypic SCF Complex Members
Bibliographic record
Abstract
The SKP1, CUL1, F-box protein (SCF) complex represents a family of 69 E3 ubiquitin ligases that poly-ubiquitinate protein substrates marking them for proteolytic degradation via the 26S proteasome. Established SCF complex targets include transcription factors, oncoproteins and tumor suppressors that modulate cell cycle activity and mitotic fidelity. Accordingly, genetic and epigenetic alterations involving SCF complex member genes are expected to adversely impact target regulation and contribute to disease etiology. To gain novel insight into cancer pathogenesis, we determined the prevalence of genetic and epigenetic alterations in six prototypic SCF complex member genes (SKP1, CUL1, RBX1, SKP2, FBXW7 and FBXO5) from patient datasets extracted from The Cancer Genome Atlas (TCGA). Collectively, ~45% of observed SCF complex member mutations are predicted to impact complex structure and/or function in 10 solid tumor types. In addition, the distribution of encoded alterations suggest SCF complex members may exhibit either tumor suppressor or oncogenic mutational profiles in a cancer type dependent manner. Further bioinformatic analyses reveal the potential functional implications of encoded alterations arising from missense mutations by examining predicted deleterious mutations with available crystal structures. The SCF complex also exhibits frequent copy number alterations in a variety of cancer types that generally correspond with mRNA expression levels. Finally, we note that SCF complex member genes are differentially methylated across cancer types, which may effectively phenocopy gene copy number alterations. Collectively, these data show that SCF complex member genes are frequently altered at the genetic and epigenetic levels in many cancer types, which will adversely impact the normal targeting and timely destruction of protein substrates, which may contribute to the development and progression of an extensive array of cancer types.
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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 | 0.002 | 0.002 |
| 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) | 0.002 | 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".