LncRNAs as nodes for the cross-talk between autophagy and Wnt signaling in pancreatic cancer drug resistance
Bibliographic record
Abstract
Pancreatic cancer is a malignancy with high mortality. In addition to the few symptoms until the disease reaches an advanced stage, the high fatality rate is attributed to its rapid development, drug resistance and lack of appropriate treatment. In the selection and research of therapeutic drugs, gemcitabine is the first-line drug for pancreatic cancer. Solving the problem of gemcitabine resistance in pancreatic cancer will contribute to the progress of pancreatic cancer treatment. Long non coding RNAs (lncRNAs), which are RNA transcripts longer than 200 nucleotides, play vital roles in cellular physiological metabolic activities. Currently, our group and others have found that some lncRNAs are aberrantly expressed in pancreatic cancer cells, which can regulate the process of cancer through autophagy and Wnt/β-catenin pathways simultaneously and affect the sensitivity of cancer cells to therapeutic drugs. This review presents an overview of the recent evidence concerning the node of lncRNA for the cross-talk between autophagy and Wnt/β-catenin signaling in pancreatic cancer, together with the practicability of lncRNAs and the core regulatory factors as targets in therapeutic resistance.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".