Long non-coding RNA DSCAM-AS1 upregulates <i>USP47</i> expression through sponging miR-101-3p to accelerate osteosarcoma progression
Bibliographic record
Abstract
Osteosarcoma (OS) originating from mesenchyme is one of the most common invasive tumors of bone, and has an extremely high mortality rate. Previous studies have reported that long non-coding RNAs (lncRNAs) play essential roles in the tumorigenesis and progression of a multitude of human cancers. The lncRNA DSCAM-AS1 has been reported to be an oncogenic gene in many cancers. However, the roles and regulatory mechanisms of DSCAM-AS1 in OS have not been deeply investigated. In this study, our findings prove that DSCAM-AS1 is highly expressed in OS cells. Knockdown of DSCAM-AS1 suppressed cell proliferation, migration, and invasiveness, and induced cell apoptosis in OS. Additionally, knockdown of DSCAM-AS1 inactivated the Wnt–β-catenin signaling pathway. Moreover, research into its molecular mechanisms confirmed that DSCAM-AS1 functions as a sponge for miR-101-3p, and that ubiquitin-specific peptidase 47 (USP47) is a target gene of miR-101-3p. Furthermore, a negative relationship between miR-101-3p and DSCAM-AS1 or USP47 was discovered. The results from our rescue assays suggest that DSCAM-AS1 regulates the progression of OS through binding with miR-101-3p to control the expression of USP47. Finally, we discovered that AKT–mTOR signaling pathway mediates the activity of DSCAM-AS1 in OS. Taken together, our results show that DSCAM-AS1 accelerates the progression of OS via the miR-101-3p–USP47 axis, which could present a new potential therapeutic treatment for OS.
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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".