CD9, a potential leukemia stem cell marker, regulates drug resistance and leukemia development in acute myeloid leukemia
Bibliographic record
Abstract
Abstract Background: Leukemia stem cells (LSCs) are responsible for the initiation, progression and relapse of acute myeloid leukemia (AML). Therefore, a therapeutic strategy targeting LSCs is a potential approach to eradicate AML. In this study, we aimed to identify LSC-specific surface markers and uncover the underlying mechanism of AML LSCs.Methods: Microarray gene expression data were used to investigate candidate AML-LSC-specific markers. CD9 expression in AML cell lines, patients with AML and normal donors was evaluated by flow cytometry (FC). The biological characteristics of CD9-positive (CD9+) cells were analyzed by in vitro proliferation, chemotherapeutic drug resistance, migration and in vivo xenotransplantation assays. The molecular mechanism involved in CD9+ cell function was investigated by gene expression profiling. The effects of alpha-2-macroglobulin (A2M) on CD9+ cells were analyzed with regard to proliferation, drug resistance and migration.Results: CD9, a cell surface protein, was specifically expressed on AML LSCs but barely detected on normal hematopoietic stem cells (HSCs). CD9+ cells exhibit more resistance to chemotherapy drugs and higher migration potential than do CD9-negative (CD9-) cells. More importantly, CD9+ cells possess the ability to reconstitute human AML in immunocompromised mice and promote leukemia growth, suggesting that CD9+ cells define the LSC population. Furthermore, we identified that A2M plays a crucial role in maintaining CD9+ LSC stemness. Knockdown of A2M impairs drug resistance and migration of CD9+ cells.Conclusion: Our findings suggest that CD9 is a new biomarker of AML LSCs and is a promising therapeutic target.
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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".