The Astragalus Membranaceus Herb Attenuates Leukemia by Inhibiting the FLI1 Oncogene and Enhancing Anti-Tumor Immunity
Bibliographic record
Abstract
Astragalus membranaceus (AM) herb is a component of traditional Chinese medicine used to treat various cancers. Herein, we demonstrate a strong anti-leukemic effect of AM injected (Ai) into the mouse model of erythroleukemia induced by Friend virus. Chemical analysis combined with mass spectrometry of AM/Ai identified the compounds Betulinic acid, Kaempferol, Hederagenin, and formononetin, all major mediators of leukemia inhibition in culture and in vivo. Docking analysis demonstrated binding of these four compounds to FLI1, resulting in downregulation of its targets, induction of apoptosis, differentiation, and suppression of cell proliferation. Chemical composition analysis identified other compounds previously known having anti-tumor activity independent of the FLI1 blockade. Among these, Astragaloside-A (As-A) has marginal effect on cells in culture, but strongly inhibits leukemogenesis in vivo, likely through improvement of anti-tumor immunity. Indeed, both IDO1 and TDO2 were identified as targets of As-A, leading to suppression of tryptophane-mediated Kyn production and leukemia suppression. Moreover, As-A interacts with histamine decarboxylase (HDC), leading to suppression of anti-inflammatory genes TNF, IL1B/IL1A, TNFAIP3, and CXCR2, but not IL6. These results implicate HDC as a novel immune checkpoint mediator, induced in the tumor microenvironment to promote leukemia. Functional analysis of AM components may allow development of combination therapy with optimal anti-leukemia effect.
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 | 0.000 | 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".