LRP11-AS1 Promotes the Malignant Behavior of Triple Negative Breast Cancer Cells via the MiR-149-3p/NRP2 Axis
Bibliographic record
Abstract
Abstract Background: Breast cancer is the most commonly diagnosed cancer in women. Triple negative breast cancer (TNBC) is the most difficult subtype of breast cancer to treat due to the deficiency in drug-targetable receptors. LRP11-AS1, a newly identified oncogenic long noncoding RNA (lncRNA) was found to be significantly overexpressed in TNBC cells. The aim of this study is to investigate the malignant roles and the oncogenic mechanisms of LRP11-AS1 in TNBC. Methods: CCK-8, colony formation, transwell migration and transwell invasion assays were performed to study the functions of LRP11-AS1. Quantitative PCR and western blot were used to determine the gene expression. Bioinformatics analysis and dual-luciferase reporter assay were conducted to study lncRNA and miRNA interactions. Results: LRP11-AS1 was found to be significantly overexpressed in TNBC cells compared to the non-TNBC cells and normal mammary epithelial cells. Knockdown of LRP11-AS1 could inhibit the growth and metastasis of TNBC cells and regulate cell cycle. Mechanistically, LRP11-AS1 was found to act as a competing endogenous RNA (ceRNA) to sponge miR-149-3p. Silencing of LRP11-AS1 increased the expression of miR-149-3p and overexpression of miR-149-3p suppressed the expression of LRP11-AS1. Inhibition of miR-149-3p could reverse the anticancer effect of LRP11-AS1 deficiency in TNBC cells. Moreover, Neuropilin-2 (NRP2) was found to be the target of miR-149-3p. Rescue experiments revealed that NRP2 overexpression could rescue the anticancer effect of LRP11-AS1 deficiency in TNBC cells. Conclusion: LRP11-AS1 overexpressed in TNBC showed the oncogenic effects possibly by sponging miR-149-3p and regulating the miR-1493p/NRP2 axis, which indicated LRP11-AS1 as a potential diagnostic biomarker and therapeutic target in TNBC.
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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 | 0.001 | 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".