<i>LncRNA SNHG4</i> promotes the proliferation, migration, invasiveness, and epithelial–mesenchymal transition of lung cancer cells by regulating <i>miR-98-5p</i>
Bibliographic record
Abstract
Long noncoding RNA small nucleolar RNA host gene 4 (SNHG4) is usually up-regulated in cancer and regulates the malignant behavior of cancer cells. However, its role in lung cancer remains elusive. In this study, we silenced the expression of SNHG4 in NCI-H1437 and SK-MES-1, two representative non-small-cell lung cancer cell lines, by transfecting them with siRNA (small interfering RNA) that specifically targets SNHG4. We observed significantly inhibited cell proliferation in vitro and reduced tumor growth in vivo after SNHG4 silencing. SNHG4 knockdown also led to cell cycle arrest at the G1 phase, accompanied with down-regulation of cyclin-dependent kinases CDK4 and CDK6. The migration and invasiveness of these two cell lines were remarkably inhibited after SNHG4 silencing. Moreover, our study revealed that the epithelial–mesenchymal transition (EMT) of lung cancer cells was suppressed by SNHG4 silencing, as evidenced by up-regulated E-cadherin and down-regulated SALL4, Twist, and vimentin. In addition, we found that SNHG4 silencing induced up-regulation of miR-98-5p. MiR-98-5p inhibition abrogated the effect of SNHG4 silencing on proliferation and invasion of lung cancer cells. In conclusion, our findings demonstrate that SNHG4 is required by lung cancer cells to maintain malignant phenotype. SNHG4 probably exerts its pro-survival and pro-metastatic effects by sponging anti-tumor miR-98-5p.
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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".