Kyriakidis Ioannis, Papadovasilakis Zacharias, Papoutsoglou Georgios, Pelagiadis Iordanis, Papadaki Helen A, Pontikoglou Charalampos, Stiakaki Eftichia
Department of Pediatric Hematology-Oncology & Autologous Hematopoietic Stem Cell Transplantation Unit, University Hospital of Heraklion & Laboratory of Blood Diseases and Childhood Cancer Biology, School of Medicine, University of Crete, 71003, Heraklion, Greece.
JADBio Gnosis DA S.A., Science and Technology Park of Crete, 70013, Heraklion, Greece.
Noncoding RNA Res. 2025 Aug 20;15:120-131. doi: 10.1016/j.ncrna.2025.08.003. eCollection 2025 Dec.
Despite significant advancements in overall survival rates for childhood acute lymphoblastic leukemia (ALL), relapse continues to pose a major challenge. MicroRNAs have proven valuable for improving diagnosis, treatment, and survival outcomes, establishing themselves as key biomarkers. Using RNA-seq data from 123 ALL patients and employing predictive modeling via automated machine learning (AutoML) alongside causal-inspired biomarker discovery, we identified highly predictive microRNA signatures linked to high-risk strata and clinical features in unfavorable cases. We further identified predictive signatures for each genetic subtype of childhood ALL, highlighting shared miRNAs throughout the study. A thorough literature review of the relationships between miRNA differential expression and key high-risk features in childhood ALL [immunophenotype, elevated white blood cell counts at diagnosis, central nervous system involvement, measurable residual disease (MRD), and chemoresistance] confirmed the signatures generated in this study. Our results revealed a highly predictive signature distinguishing B- and T-ALL, associated with apoptosis, confirming the reported difference between the two immunophenotypes. Additionally, miR-223 emerged as crucial for high-risk stratification and chemoresistant MRD-positive cases. These findings demonstrate the potential of AutoML tools to reveal novel biological insights in pediatric ALL, driving future advancements.
尽管儿童急性淋巴细胞白血病（ALL）的总体生存率有了显著提高，但复发仍然是一个重大挑战。事实证明，微小RNA对于改善诊断、治疗和生存结果具有重要价值，已成为关键的生物标志物。利用123例ALL患者的RNA测序数据，并通过自动化机器学习（AutoML）进行预测建模以及受因果关系启发的生物标志物发现，我们确定了与高危分层以及不良病例临床特征相关的高度预测性微小RNA特征。我们还进一步确定了儿童ALL各基因亚型的预测特征，突出了整个研究中共享的微小RNA。对儿童ALL中微小RNA差异表达与关键高危特征（免疫表型、诊断时白细胞计数升高、中枢神经系统受累、可测量残留病（MRD）和化疗耐药性）之间关系的全面文献综述证实了本研究中生成的特征。我们的结果揭示了一个区分B-ALL和T-ALL的高度预测性特征，与细胞凋亡相关，证实了两种免疫表型之间已报道的差异。此外，miR-223在高危分层和化疗耐药的MRD阳性病例中至关重要。这些发现证明了AutoML工具在揭示小儿ALL新生物学见解方面的潜力，推动未来的进展。
