ArticleFrontiers in immunology2023
A novel stemness classification in acute myeloid leukemia by the stemness index and the identification of cancer stem cell-related biomarkers.
Article in Frontiers in immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 12 citations in OpenAlex.
- Predicting immunotherapeutic response and therapeutic targets by stemness classification of acute myeloid leukemia by stemness score.Discover oncology · 2025Article
- Development of a cancer metastasis-associated risk model via multi-machine-learning algorithms for prognostic risk evaluation and clinical application in oral squamous cell carcinoma.Journal of translational medicine · 2025Article
- Systematic pan-cancer analysis identified NCOA4 as an immunological and prognostic biomarker and validated in lung adenocarcinoma.Discover oncology · 2025Article
- Shared genetic association between inflammatory bowel disease and acute myeloid leukemia: insights from mendelian randomization and transcriptomic analyses.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2025Article
- Integrin factor (FAM27E3), as a metastatic marker of papillary thyroid carcinoma, through the p53 signaling pathway promoting lymph node metastasis.Frontiers in genetics · 2025Article
- Unlocking reproducible transcriptomic signatures for acute myeloid leukaemia: Integration, classification and drug repurposing.Journal of cellular and molecular medicine · 2024Article
- Pharmacological p38 MAPK inhibitor SB203580 enhances AML stem cell line KG1a chemosensitivity to daunorubicin by promoting late apoptosis, cell growth arrest in S-phase, and miR-328-3p upregulation.Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society · 2024Article
- Multiple machine-learning tools identifying prognostic biomarkers for acute Myeloid Leukemia.BMC medical informatics and decision making · 2024Article
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7 authors at 3 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Background: Stem cells play an important role in acute myeloid leukemia (AML). However, their precise effect on AML tumorigenesis and progression remains unclear. Methods: The present study aimed to characterize stem cell-related gene expression and identify stemness biomarker genes in AML. We calculated the stemness index (mRNAsi) based on transcription data using the one-class logistic regression (OCLR) algorithm for patients in the training set. According to the mRNAsi score, we performed consensus clustering and identified two stemness subgroups. Eight stemness-related genes were identified as stemness biomarkers through gene selection by three machine learning methods. Results: We found that patients in stemness subgroup I had a poor prognosis and benefited from nilotinib, MK-2206 and axitinib treatment. In addition, the mutation profiles of these two stemness subgroups were different, which suggested that patients in different subgroups had different biological processes. There was a strong significant negative correlation between mRNAsi and the immune score (r= -0.43, p<0.001). Furthermore, we identified eight stemness-related genes that have potential to be biomarkers, including SLC43A2, CYBB, CFP, GRN, CST3, TIMP1, CFD and IGLL1. These genes, except IGLL1, had a negative correlation with mRNAsi. SLC43A2 is expected to be a potential stemness-related biomarker in AML. Conclusion: Overall, we established a novel stemness classification using the mRNAsi score and eight stemness-related genes that may be biomarkers. Clinical decision-making should be guided by this new signature in prospective studies.
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