Long non-coding RNA VCAN-AS1 promotes the malignant behaviors of breast cancer by regulating the miR-106a-5p-mediated STAT3/HIF-1α pathway
Bibliographic record
Abstract
An accumulating number of studies have found that long noncoding RNAs (lncRNAs) participate in breast cancer (BC) development. LncRNA VCAN-AS1, a novel lncRNA, has been confirmed to regulate the progression of gastric cancer, while its role in BC is elusive. Here, our results illustrate that VCAN-AS1 is overexpressed in BC tissues and cells, while miR-106a-5p was downregulated and negatively correlated with VCAN-AS1. In addition, high VCAN-AS1 expression and low miR-106a-5p expression were closely correlated with poor overall survival in BC patients. Functional experiments confirmed that VCAN-AS1 overexpression notably accelerated BC cell proliferation, migration, invasion, and epithelial–mesenchymal transition (EMT) and enhanced tumor cell growth while also suppressing cell apoptosis. However, overexpression of miR-106a-5p had the opposite effects. In addition, rescue experiments confirmed that overexpression of VCAN-AS1 inhibited the tumor-suppressive effects mediated by miR-106a-5p. Mechanistically, through bioinformatics analysis, we found that VCAN-AS1 functions as a competitive endogenous RNA (ceRNA) of miR-106a-5p, which targets the 3ʹ untranslated region (UTR) of signal transducer and activator of transcription 3 (STAT3). Further experiments indicated that miR-106a-5p downregulated the STAT3/hypoxia-inducible factor-1alpha (HIF-1α) pathway, while activating the STAT3 pathway reversed miR-106a-5p-mediated antitumor effects. Collectively, our data suggest that VCAN-AS1 is upregulated in breast cancer and promotes its progression by regulating the miR-106a-5p-mediated STAT3/HIF-1α pathway. This study provides a new target for BC 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 | 0.000 |
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".