<i>SNHG8</i> promotes cell proliferation, migration, and invasion of nasopharyngeal carcinoma cells as an oncogene through miR-588/HMGA2 axis
Bibliographic record
Abstract
Nasopharyngeal carcinoma (NC) poses a threat to the life of patients. Long non-coding RNA (LncRNA) is a novel kind of non-coding RNA, which plays a pivotal role through sponge microRNA (miRNA). Abnormal expression of small nucleolar RNA host gene 8 (SNHG8) is involved in various tumors; however, the role of SNHG8 in NC remains unknown. Quantitative real-time PCR (qRT-PCR) and Western blotting was employed to detect the expression levels of SNHG8, miR-588, and high mobility group A2 (HMGA2). Cell proliferation, migration, and invasion were analyzed by CCK-8 and transwell assays. miR-588 binding sites in SNHG8 were predicted by LncBase analysis. Luciferase reporter and RNA pull-down assay were used to confirm the interaction of SNHG8 and miR-588. SNHG8 was highly expressed in NC cells. The prognosis of the patients with NC in the high expression levels of SNHG8 was poorer than that in the low expression levels. The expression of SNHG8 was closely related to tumor size, TNM stage, and distal metastasis. Knockdown of SNHG8 inhibited cell proliferation, migration, and invasion of NC. SNHG8 targeted miR-588. Inhibition of miR-588 could partially reverse the knockdown of SNHG8 in NC cells, and miR-588 targeted HMGA2. In conclusion, SNHG8 promotes proliferation, migration, and invasion of NC cells through miR-588/HMGA2 in NC as an oncogene.
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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) | 0.003 | 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".