A genome-wide shRNA screen identifies <i>GAS1</i> as a novel melanoma metastasis suppressor gene
Bibliographic record
Abstract
Metastasis suppressor genes inhibit one or more steps required for metastasis without affecting primary tumor formation. Due to the complexity of the metastatic process, the development of experimental approaches for identifying genes involved in metastasis prevention has been challenging. Here we describe a genome-wide RNAi screening strategy to identify candidate metastasis suppressor genes. Following expression in weakly metastatic B16-F0 mouse melanoma cells, shRNAs were selected based upon enhanced satellite colony formation in a three-dimensional cell culture system and confirmed in a mouse experimental metastasis assay. Using this approach we discovered 22 genes whose knockdown increased metastasis without affecting primary tumor growth. We focused on one of these genes, Gas1 (Growth arrest-specific 1), because we found that it was substantially down-regulated in highly metastatic B16-F10 melanoma cells, which contributed to the high metastatic potential of this mouse cell line. We further demonstrated that Gas1 has all the expected properties of a melanoma tumor suppressor including: suppression of metastasis in a spontaneous metastasis assay, promotion of apoptosis following dissemination of cells to secondary sites, and frequent down-regulation in human melanoma metastasis-derived cell lines and metastatic tumor samples. Thus, we developed a genome-wide shRNA screening strategy that enables the discovery of new metastasis suppressor genes.
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".