Evidence map›Paper›PMID 39885388›Full record

ReviewMolecular medicine (Cambridge, Mass.)2025

Single cell RNA sequencing improves the next generation of approaches to AML treatment: challenges and perspectives.

Zahra Khosroabadi, Samaneh Azaryar, Hassan Dianat-Moghadam, Zohreh Amoozgar, Mohammadreza Sharifi

Abstract readReview
In one paragraph

Review in Molecular medicine (Cambridge, Mass.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

  1. Review
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  9. Multidrug Resistance: Are We Still Afraid of the Big Bad Wolf.Pharmaceuticals (Basel, Switzerland) · 2025
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Zahra Khosroabadi *Department of Genetics and Molecular Biology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, 8174673461, Iran.
Samaneh Azaryar *Department of Genetics and Molecular Biology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, 8174673461, Iran.
Hassan Dianat-MoghadamDepartment of Genetics and Molecular Biology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, 8174673461, Iran. Dianat.h@med.mui.ac.ir.
Zohreh AmoozgarEdwin L. Steele Laboratories for Tumor Biology, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
Mohammadreza SharifiDepartment of Genetics and Molecular Biology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, 8174673461, Iran. mo_sharifi@med.mui.ac.ir.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Acute myeloid leukemia (AML) is caused by altered maturation and differentiation of myeloid blasts, as well as transcriptional/epigenetic alterations, all leading to excessive proliferation of malignant blood cells in the bone marrow. Tumor heterogeneity due to the acquisition of new somatic alterations leads to a high rate of resistance to current therapies or reduces the efficacy of hematopoietic stem cell transplantation (HSCT), thus increasing the risk of relapse and mortality. Single-cell RNA sequencing (scRNA-seq) will enable the classification of AML and guide treatment approaches by profiling patients with different facets of the same disease, stratifying risk, and identifying new potential therapeutic targets at the time of diagnosis or after treatment. ScRNA-seq allows the identification of quiescent stem-like cells, and leukemia stem cells responsible for resistance to therapeutic approaches and relapse after treatment. This method also introduces the factors and mechanisms that enhance the efficacy of the HSCT process. Generated data of the transcriptional profile of the AML could even allow the development of cancer vaccines and CAR T-cell therapies while saving valuable time and alleviating dangerous side effects of chemotherapy and HSCT in vivo. However, scRNA-seq applications face various challenges such as a large amount of data for high-dimensional analysis, technical noise, batch effects, and finding small biological patterns, which could be improved in combination with artificial intelligence models.

Indexed as

Leukemia, Myeloid, AcuteSequence Analysis, RNASingle-Cell AnalysisGene Expression ProfilingHematopoietic Stem Cell TransplantationHumansAI modelAMLDrug resistanceSingle cell RNA sequencingTumor heterogeneity

Identifiers

PMID39885388
PMCPMC11783831

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.