Overexpression of FRA1 (FOSL1) Leads to Global Transcriptional Perturbations, Reduced Cellular Adhesion and Altered Cell Cycle Progression
Bibliographic record
Abstract
superfamily. FRA1 is expressed in most tissues at low levels, and its expression is robustly induced in response to extracellular signals, leading to downstream cellular processes. However, abnormal FRA1 overexpression has been reported in various pathological states, including tumor progression and inflammation. To date, the molecular effects of FRA1 overexpression are still not understood. Therefore, the aim of this study was to investigate the transcriptional and functional effects of FRA1 overexpression using the CGL1 human hybrid cell line. FRA1-overexpressing CGL1 cells were generated using stably integrated CRISPR-mediated transcriptional activation, resulting in a 2-3 fold increase in FRA1 mRNA and protein levels. RNA-sequencing identified 298 differentially expressed genes with FRA1 overexpression. Gene ontology analysis showed numerous molecular networks enriched with FRA1 overexpression, including transcription-factor binding, regulation of the extracellular matrix and adhesion, and a variety of signaling processes, including protein kinase activity and chemokine signaling. In addition, cell functional assays demonstrated reduced cell adherence to fibronectin and collagen with FRA1 overexpression and altered cell cycle progression. Taken together, this study unravels the transcriptional response mediated by FRA1 overexpression and establishes the role of FRA1 in adhesion and cell cycle progression.
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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".