Targeting <i>LINC00673</i> expression triggers cellular senescence in lung cancer
Bibliographic record
Abstract
Aberrant expression of noncoding RNAs plays a critical role during tumorigenesis. To uncover novel functions of long non-coding RNA (lncRNA) in lung adenocarcinoma, we used a microarray-based screen identifying LINC00673 with elevated expression in matched tumor versus normal tissue. We report that loss of LINC00673 is sufficient to trigger cellular senescence, a tumor suppressive mechanism associated with permanent cell cycle arrest, both in lung cancer and normal cells in a p53-dependent manner. LINC00673-depleted cells fail to efficiently transit from G1- to S-phase. Using a quantitative proteomics approach, we confirm the modulation of senescence-associated genes as a result of LINC00673 knockdown. In addition, we uncover that depletion of p53 in normal and tumor cells is sufficient to overcome LINC00673-mediated cell cycle arrest and cellular senescence. Furthermore, we report that overexpression of LINC00673 reduces p53 translation and contributes to the bypass of Ras-induced senescence. In summary, our findings highlight LINC00673 as a crucial regulator of proliferation and cellular senescence in lung cancer.
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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 | 0.000 |
| 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".