Long noncoding RNA POU6F2-AS1 regulates lung cancer aggressiveness through sponging miR-34c-5p to modulate KCNJ4 expression
Bibliographic record
Abstract
Abstract It has been extensively reported that long noncoding RNAs (lncRNAs) were closely associated with multiple malignancies. The aim of our study was to investigate the effects and mechanism of lncRNA POU6F2-AS1 in lung adenocarcinoma (LADC).The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets provided us the information of LADC clinical samples. High-regulation of POU6F2-AS1 was presented in LADC tissues compared with adjacent normal tissues, which was correlated with poor outcome of LADC patients. Functional experiments in Calu-3 and NCI-H460 cells showed that POU6F2-AS1 significantly promoted LADC cell proliferation, colony formation, invasion and migration. Moreover, through online prediction, luciferase reporter assay and Pearson’s correlation analysis, we found that POU6F2-AS1 may act as a competing endogenous RNA (ceRNA) of miR-34c-5p and facilitated the expression of potassium voltage-gated channel subfamily J member 4 (KCNJ4). The promoting effect of cell aggressiveness induced by POU6F2-AS1 was enhanced by KCNJ4, whilst was abrogated due to the overexpression of miR-34c-5p. Collectively, POU6F2-AS1 might function as a ceRNA through sponging miR-34c-5p to high-regulate KCNJ4 in LADC, which indicates that POU6F2-AS1 might be a promising therapeutic target with significant prognostic value for LADC treatment.
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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) | 0.001 | 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".