Matrine Inhibitory Effect on Self-renewal and Re-sensitization of 5-FU Resistant NSCLC Stem Cells were through Let-7b dependent Downregulation of CCND1
Bibliographic record
Abstract
Matrine is one of the major alkaloids extracted from Sophora flavescens Ait of the traditional Chinese medicine, was the main chemical ingredient of compounds of Kushen injection. The Matrine is considered as a promising therapeutic agent for curing nonsmall cell lung cancer (NSCLC), used either alone or combined with chemotherapeutic agents. In the present study, we focused on the possible roles of Matrine exerted on the self-renewal ability of stem-like cells of the NSCLC group, as well as the cytotoxicity of chemotherapeutic agents, in vitro and in vivo. Here we reported that Matrine inhibits cancer stem-like cell (CSC) properties through upregulation of Let-7b and suppression of the Wnt pathway. Overexpression of Let-7b suppressed the ability of tumorsphere formation, decreased Wnt pathway activation through inhibiting its transcriptional activity in lung CSCs. Further studies revealed that Let-7b directly targeted CCND1 and decreased its expression, whereas Matrine increased Let-7b levels and followed by inactivation of the CCND1/Wnt signaling pathway and inhibition of EMT, which was characterized by loss of epithelial markers and acquisition of a mesenchymal phenotype in lung CSCs. What is more, we found that Matrine increased Let-7b level in an endoribonuclease DICER1-dependent manner. And xenografts in nude mice evidenced that Matrine increased the sensitivity of lung CSCs to 5-FU and inhibited the accumulation of CCND1 in tumor tissues induced by 5-FU. Taken together, these data illustrate the role of Let-7b in regulating lung CSCs traits and DICER1/let-7/CCND1 axis in Matrine or in combination with 5-FU intervention of lung CSCs’ expansion, helping to fulfill the anti-cancer action of Matrine.
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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 | 0.000 | 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 | 0.000 |
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".