The Rise of FXR1: Escaping Cellular Senescence in Head and Neck Squamous Cell Carcinoma
Bibliographic record
Abstract
Cellular senescence is a key tumor-suppressing mechanism in response to numerous cellular threats including oxidative stress, telomere loss, and oncogene activation.It is essentially a permanent state of G1 cell cycle arrest in which cells remain viable and metabolically active.Recent studies indicate that senescence plays a pivotal role in suppression of tumorigenesis in vivo [1,2] and is frequently observed in different premalignant tumors such as lung adenomas, neurofibromas, and naevi [3,4].Aside from its critical role in preventing cancer development, the senescence program also enhances the response to cancer therapy [5].In order to become cancerous, cells must find ways to inactivate or bypass the senescence response.In fact, the viral oncoproteins E6 and E7 from human papillomavirus (HPV), which inhibit the tumor suppressors p53 and Rb, respectively, inactivate cellular senescence in response to oncogenic stress [6,7].Thus, infection with HPV, an important risk factor for subsets of head and neck squamous cell carcinoma (HNSCC), could promote tumorigenesis by inhibiting cellular senescence.However, numerous HNSCCs are HPV-independent, thus underscoring the need to identify additional genetic alterations in HNSCC.In the September 2016 issue of PLOS Genetics, Majumber et al. reported that the Fragile X-related protein 1 (FXR1), an RNA-binding protein, suppresses the senescence response in two different HPVnegative HNSCC cell lines [8].This further supports the requirement for bypassing senescence in both HPV-positive and -negative HNSCC and sheds light on the putative role of FXR1 in promoting HNSCC.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.012 | 0.010 |
| 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".