Bibliographic record
Abstract
Wnt/β-catenin alterations are prominent in human malignancies. In non-small cell lung cancer (NSCLC), β-catenin and APC mutations are uncommon, but Wnt signaling is important in NSCLC cell lines, and Wnt inhibition reduces proliferation. Overexpression of Wnt-1, -2, -3, and -5a and of Wnt-pathway components Frizzled-8, Dishevelled, Porcupine, and TCF-4 is common in resected NSCLC and is associated with poor prognosis. Conversely, noncanonical Wnt-7a suppresses NSCLC development and is often downregulated. Although β-catenin is often expressed in NSCLCs, it was paradoxically associated with improved prognosis in some series, possibly because of E-cadherin interactions. Downregulation of Wnt inhibitors (eg, by hypermethylation) is common in NSCLC tumor cell lines and resected samples; may be associated with high stage, dedifferentiation, and poor prognosis; and has been reported for AXIN, sFRPs 1-5, WIF-1, Dkk-1, Dkk-3, HDPR1, RUNX3, APC, CDX2, DACT2, TMEM88, Chibby, NKD1, EMX2, ING4, and miR-487b. AXIN is also destabilized by tankyrases, and GSK3β may be inactivated through phosphorylation by EGFR. Preclinically, restoration of Wnt inhibitor function is associated with reduced Wnt signaling, decreased cell proliferation, and increased apoptosis. Wnt signaling may also augment resistance to cisplatin, docetaxel, and radiotherapy, and Wnt inhibitors may restore sensitivity. Overall, available data indicate that Wnt signaling substantially impacts NSCLC tumorigenesis, prognosis, and resistance to therapy, with loss of Wnt signaling inhibitors by promoter hypermethylation or other mechanisms appearing to be particularly important. Wnt pathway antagonists warrant exploration clinically in NSCLC. Agents blocking selected specific β-catenin interactions and approaches to increase expression of downregulated Wnt inhibitors may be of particular interest.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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 | 0.002 |
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".