The Multifaceted Roles of MicroRNA-181 in Stem Cell Differentiation and Cancer Stem Cell Plasticity
Bibliographic record
Abstract
Stem cells are undifferentiated or partially differentiated cells with an extraordinary ability to self-renew and differentiate into various cell types during growth and development. The epithelial-mesenchymal transition (EMT), a critical developmental process, enhances stem cell-like properties in cells, and is associated with both normal stem cell function and the formation of cancer stem cells. Cell stemness and the EMT often coexist and are interconnected in various contexts. Cancer stem cells are a critical tumor cell population that drives tumorigenesis, cancer progression, drug resistance, and metastasis. Stem cell differentiation and the generation of cancer stem cells are regulated by numerous molecules, including microRNAs (miRNAs). These miRNAs, particularly through the modulation of EMT-associated factors, play major roles in controlling the stemness of cancer stem cells. This review presents an up-to-date summary of the regulatory roles of miR-181 in human stem cell differentiation and cancer cell stemness. We outline studies from the current literature and summarize the miR-181-controlled signaling pathways responsible for driving human stem cell differentiation or the emergence of cancer stem cells. Given its critical role in regulating cell stemness, miR-181 is a promising target for influencing human cell fate. Modulation of miR-181 expression has been found to be altered in cancer stem cells' biological behaviors and to significantly improve cancer treatment outcomes. Additionally, we discuss challenges in miRNA-based therapies and targeted delivery with nanotechnology-based systems.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".