{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T09:23:59Z","timestamp":1787822639122,"version":"build-2784847793"},"reference-count":64,"publisher":"Wiley","issue":"16","license":[{"start":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T00:00:00Z","timestamp":1786665600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T00:00:00Z","timestamp":1786665600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["faseb.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["The FASEB Journal"],"published-print":{"date-parts":[[2026,8,31]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Neuroblastoma (NB) is a common pediatric tumor that exhibits significant clinical heterogeneity and substantial prognostic variability. However, there is still a lack of effective prognostic tools. It has been found that the differentiation of myeloid cells in the tumor microenvironment (TME) has a profound influence on tumor progression and immune regulation in neuroblastoma. But the correlation between NB and myeloid cell differentiation\u2010related genes (MCDRGs) is still unclear. This study integrated bulk and single\u2010cell RNA sequencing data to identify key prognostic genes associated with myeloid cell differentiation, construct and validate a risk prediction model, and thoroughly investigate the molecular mechanisms through which these genes regulate the TME and influence NB prognosis. Using bioinformatic approaches, we retrieved NB\u2010related transcriptomic data from public databases, identified differentially expressed myeloid cell differentiation\u2010related genes (MCDRGs), and constructed a prognostic risk model using Cox regression and machine\u2010learning algorithms. Functional enrichment analysis, TME characterization, immune checkpoint profiling, drug sensitivity analysis, and somatic mutation analysis were performed between the high\u2010risk group (HRG) and low\u2010risk group (LRG). Single\u2010cell RNA sequencing data were used to dissect the cell type\u2010specific expression patterns of prognostic genes, which were further experimentally validated via reverse transcription quantitative polymerase chain reaction (RT\u2010qPCR). Five key prognostic genes (\n                    <jats:italic>FAXDC2<\/jats:italic>\n                    ,\n                    <jats:italic>GP1BB<\/jats:italic>\n                    ,\n                    <jats:italic>TRIB1<\/jats:italic>\n                    ,\n                    <jats:italic>ETV2<\/jats:italic>\n                    , and\n                    <jats:italic>H4C12<\/jats:italic>\n                    ) were identified from 187 candidate genes, and a robust risk prediction model was constructed. Significant differences were observed between the HRG and LRG in terms of TME scores, immune cell infiltration, and immune checkpoint expression. Ribosome biogenesis and cell cycle\u2013related pathways were enriched in the HRG, which displayed higher sensitivity to entinostat and sapitinib. Single\u2010cell analysis further highlighted fibroblasts and myeloid cells as key cell subsets in which prognostic genes exhibited dynamic expression patterns. RT\u2010qPCR further validated these gene expression trends. The prognostic model based on MCDRGs effectively predicts survival outcomes in patients with NB. This model systematically reveals the comprehensive mechanisms by which MCDRGs influence prognosis through regulating the TME, cell\u2013cell interactions, and therapeutic responses. These findings provide a new theoretical basis and potential targets for precise risk stratification and individualized treatment strategies in NB.\n                  <\/jats:p>","DOI":"10.1096\/fj.202601255r","type":"journal-article","created":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T17:54:42Z","timestamp":1786730082000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Integration of Bulk\n                    <scp>RNA<\/scp>\n                    Sequencing and Single\u2010Cell Sequencing to Identify Prognostic Genes Associated With\n                    <scp>MCDRGs<\/scp>\n                    in Neuroblastoma"],"prefix":"10.1096","volume":"40","author":[{"given":"Jie","family":"Lin","sequence":"first","affiliation":[{"name":"Department of Pediatric Urology West China Second University Hospital, Sichuan University  Chengdu Sichuan China"},{"name":"Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education  Chengdu Sichuan China"},{"name":"Sichuan Provincial Children's Hospital (Sichuan Provincial Children's Medical Center)  Meishan Sichuan China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiqiang","family":"Gao","sequence":"additional","affiliation":[{"name":"Shenzhen Medical Academy of Research and Translation  Shenzhen Guangdong China"},{"name":"Department of Urology Children's Hospital of Chongqing Medical University  Chongqing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daorui","family":"Qin","sequence":"additional","affiliation":[{"name":"Department of Pediatric Urology West China Second University Hospital, Sichuan University  Chengdu Sichuan China"},{"name":"Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education  Chengdu Sichuan China"},{"name":"Sichuan Provincial Children's Hospital (Sichuan Provincial 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