LONG NON-CODING RNA TUSC7 SUPPRESSED COLORECTAL CANCER PROGRESSION VIA REGULATING MIR-23B/PDE7A AXIS
Bibliographic record
Abstract
PURPOSE: Despite advances in our understanding of the roles of the long noncoding RNA (lncRNA) tumor suppressor candidate 7 (TUSC7) in cancer biology, which has been identified to act as a tumor suppressor by regulating cell proliferation, apoptosis, migration, invasion, cell cycle and tumor growth, its function in colorectal cancer remains unknown. METHODS: The expression levels of TUSC7 in colorectal cancer tissues and cell lines were determined, and the biological functions of TUSC7 to cancer progression in colorectal cancer were investigated via correlation analysis of clinical samples, cell viability assay, transwell assay and apoptosis analysis. Further, the molecular regulatory mechanisms of TUSC7 were demonstrated by luciferase reporter assay and western blotting. RESULTS: We observed that the expression of TUSC7 was markedly decreased in colorectal cancer cell lines. Moreover, the lower expression of TUSC7 was correlated with advanced clinical grades and poorer survival and may be an independent risk factor for colorectal cancer. Moreover, the expression of TUSC7 inhibited cell proliferation, invasion and epithelial-to-mesenchymal transition (EMT), while it facilitated apoptosis through competitively binding miR-23b. We also found that TUSC7 decreased the expression of phosphodiesterase 7A (PDE7A), a downstream target of miR-23b, through the TUSC7/miR-23b/PDE7A axis. CONCLUSION: We demonstrated the expression of TUSC7 suppressed colorectal cancer progression through the TUSC7/miR-23b/PDE7A axis, suggesting that TUSC is a potential target for therapeutic intervention in colorectal cancer.
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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".