Long non-coding RNAs: Emerging regulators of invasion and metastasis in pancreatic cancer
Bibliographic record
Abstract
• Elucidates how lncRNAs regulate EMT in pancreatic cancer via TGF-β, Wnt, and Notch pathways. • Summarizes the effects of lncRNAs on autophagy during pancreatic cancer invasion. • Reviews the impact of lncRNAs on ferroptosis in the invasive progression of pancreatic cancer. • Explores the role of exosomal lncRNAs in modulating the tumor microenvironment to promote PC spread. • Discusses RNA modifications (m 6 A, m 5 C) that enhance lncRNA stability and function in PC. The invasion and metastasis of pancreatic cancer (PC) are key factors contributing to disease progression and poor prognosis. This process is primarily driven by EMT, which has been the focus of recent studies highlighting the role of long non-coding RNAs (lncRNAs) as crucial regulators of EMT. However, the mechanisms by which lncRNAs influence invasive metastasis are multifaceted, extending beyond EMT regulation alone. This review primarily aims to characterize lncRNAs affecting invasion and metastasis in pancreatic cancer. We summarize the regulatory roles of lncRNAs across multiple molecular pathways and highlight their translational potential, considering the implications for clinical applications in diagnostics and therapeutics. The review focuses on three principal scientific themes. First, we primarily summarize lncRNAs orchestrate various signaling pathways, such as TGF-β/Smad, Wnt/β-catenin, and Notch, to regulate molecular changes associated with EMT, thereby enhancing cellular motility and invasivenes. Second, we summarize the effects of lncRNAs on autophagy and ferroptosis and discuss the role of exosomal lncRNAs in the tumor microenvironment to regulate the behavior of neighboring cells and promote cancer cell invasion. Third, we emphasize the effects of RNA modifications (such as m 6 A and m 5 C methylation) on stabilizing lncRNAs and enhancing their capacity to mediate invasive metastasis in PC. Lastly, we discuss the translational potential of these findings, emphasizing the inherent challenges in using lncRNAs as clinical biomarkers and therapeutic targets, while proposing prospective research strategies.
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 | 0.001 |
| 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".