The essentiality landscape of cell cycle related genes in human pluripotent and cancer cells
Bibliographic record
Abstract
Abstract Background Cell cycle regulation is a complex system consisting of growth-promoting and growth-restricting mechanisms, whose coordinated activity is vital for proper division and propagation. Alterations in this regulation may lead to uncontrolled proliferation and genomic instability, triggering carcinogenesis. Here, we conducted a comprehensive bioinformatic analysis of cell cycle-related genes using data from CRISPR/Cas9 loss-of-function screens performed in four cancer cell lines and in human embryonic stem cells (hESCs). Results Cell cycle genes, and in particular S phase and checkpoint genes, are highly essential for the growth of cancer and pluripotent cells. However, checkpoint genes are also found to underlie the differences between the cell cycle features of these cell types. Interestingly, while growth-promoting cell cycle genes overlap considerably between cancer and stem cells, growth-restricting cell cycle genes are completely distinct. Moreover, growth-restricting genes are consistently less frequent in cancer cells than in hESCs. Here we show that most of these genes are regulated by the tumor suppressor gene TP53, which is mutated in most cancer cells. Therefore, the growth-restriction system in cancer cells lacks important factors and does not function properly. Intriguingly, M phase genes are specifically essential for the growth of hESCs and are highly abundant among hESC-enriched genes. Conclusions Our results highlight the differences in cell cycle regulation between cell types and emphasize the importance of conducting cell cycle studies in cells with intact genomes, in order to obtain an authentic representation of the genetic features of the cell cycle.
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 | 0.000 |
| 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 | 0.000 |
| 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".