Screening and identification of miRNAs negatively regulating FAM83A/Wnt/β-catenin signaling pathway in non-small cell lung cancer
Bibliographic record
Abstract
The prevalence of non-small cell lung cancer (NSCLC) accounts for 85% of all lung cancers, with the Wnt/β-catenin signaling pathway exhibiting robust activation in this particular subtype. The expression of FAM83A (family with sequence similarity 83, member A) has been found to be significantly upregulated in lung cancer, leading to the stabilization of β-catenin and activation of the Wnt signaling pathway. In this study, we conducted a screening of down-regulated miRNAs in lung cancer with FAM83A as the target. Ultimately, we identified miR-1 as a negative regulator of FAM83A and confirmed that FAM83A is a direct target gene of miR-1 through dual luciferase reporter assays. The overexpression of miR-1 significantly attenuated the expression level of FAM83A and suppressed the Wnt signaling pathway, leading to a reduction in the expression levels of downstream target genes AXIN2, CyclinD1, and C-MYC. Additionally, it decreased the nuclear translocation of β-catenin. In addition, overexpression of miR-1 accelerated the degradation of β-catenin by inhibiting FAM83A, promoted the assembly of β-catenin degradation complex, and inhibited the proliferation, migration and invasion of NSCLC cells. In summary, miR-1 may be a potential candidate miRNA for the treatment of NSCLC.
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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) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".