{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T11:15:19Z","timestamp":1787224519189,"version":"build-2736575974"},"reference-count":41,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T00:00:00Z","timestamp":1757462400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Increasing evidence highlights the important role of the tumor microenvironment (TME) in B-cell acute lymphocytic leukemia (B-ALL). Our study aimed to stratify B-ALL based on immune signatures, thus helping to clinically predict prognosis and guide treatment.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Two cohorts of pediatric B-ALLs were included in this study, one from the GEO database (n = 136) was used to establish consensus clustering algorithm to stratify B-ALLs based on immune-related genes (IRGs), and the other from our cohort (n = 73) was used to validate the universality of established clustering algorithm. To elucidate the characteristics of each subtype, the prognosis, immune features, clinical information and genetic abnormalities were explored.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Based on the expression of 1315 IRGs, B-ALLs were classified into five distinct immune subtypes. Cluster1 had the favorable prognosis while cluster 2\u20135 had relatively unfavorable prognosis. Cluster 1 was strongly associated with clinical information indicative of a favorable prognosis [e.g. low white blood count (WBC) level] relative to cluster 2-5. In term of immune features, cluster 5 were characterized by high expression of multiple immune checkpoint genes [e.g. B and T lymphocyte attenuator (<jats:italic>BTLA<\/jats:italic>), cytotoxic T-lymphocyte-associated protein 4 (<jats:italic>CTLA4<\/jats:italic>), and T cell immunoreceptor with Ig and ITIM domains (<jats:italic>TIGIT<\/jats:italic>)]. Cluster 3 and 4 exhibited significantly downregulation of antigen processing and presentation and cytokine-cytokine receptor interaction, respectively. In terms of genetic abnormalities, cluster 1, 2 and 3 demonstrated a high incidence of <jats:italic>ETV6-RUNX1<\/jats:italic> fusion, <jats:italic>NRAS<\/jats:italic> mutation and <jats:italic>KRAS<\/jats:italic> mutation, respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Our study identified five immune subtypes that associated with distinct biological aberrations and clinical behaviors, which help us better understand the heterogeneity of TME and may provide valuable information for the precision therapy of pediatric B-ALL.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fonc.2025.1643160","type":"journal-article","created":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T04:18:05Z","timestamp":1757477885000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Transcriptome-based immune subtypes reveal the heterogeneity of tumor microenvironment in pediatric B-cell acute lymphocytic 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