LINP1 promotes the progression of cervical cancer by scaffolding EZH2, LSD1, and DNMT1 to inhibit the expression of KLF2 and PRSS8
Bibliographic record
Abstract
There is a growing body of evidence indicating that long non-coding RNAs (lncRNAs) are associated with a variety of cancers. LncRNA LINP1 has been shown to be a key factor in tumor malignancy. However, the role of LINP1 in cervical cancer (CC) it is unclear. In our research, we found that the levels of LINP1 were significantly elevated in CC tissues by comparison with adjacent normal tissue. Further, the expression level of LINP1 was upregulated in CC cells compared with healthy human cervical epithelial cell lines (HUCEC). Surprisingly, we found that downregulation of LINP1 significantly reduced the proliferation of CC cells and promoted apoptosis. Additionally, downregulation of LINP1 significantly decreased CC tumor growth in vivo. Further, we observed that LINP1 recruits EZH2, LSD1, and DNMT1, thereby reducing the expression of KLF2 and PRSS8. The results from our qRT-PCR analyses showed that silencing LINP1 uprgulated the expression of KLF2 and PRSS8 in CC cells. The results from our loss-of-function assays showed that upregulation of KLF2 and PRSS8 inhibits cell proliferation and boosts cell apoptosis in CC. We also found that inhibition of KLF2 and PRSS8 reversed the inhibitory effect on cell proliferation associated with silencing LINP1. In short, LINP1 facilitates the progression of CC by suppressing KLF2 and PRSS8, and thus could provide a promising target for CC therapy.
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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 | 0.001 |
| 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".