融合基因被认为是癌症发生和进展的主要驱动因素之一。同时，非急性早幼粒细胞白血病（APL）儿童患者与儿童急性髓系白血病（AML）的治疗效果有限。因此，我们开发并验证了一个简单的临床评分系统，用于预测患有 AML 的非 APL 儿科患者的结果。共有 184 名入住我们医院的患有 AML 的非 APL 儿科患者以及来自 AML 的独立数据集（318 名患者） TARGET 数据库已包含在内。使用最小绝对收缩和选择操作（LASSO）和Cox回归分析来确定预后因素。然后，根据临床特征和融合基因开发列线图评分来预测 1 年、3 年和 5 年总生存期 (OS)。列线图评分的准确性由校准曲线和受试者工作特征 (ROC) 曲线确定。此外，还使用内部验证队列来评估其适用性。基于Cox和LASSO回归分析，使用临床特征和OS相关融合基因（CBFβ::MYH11、RUNX1::RUNX1T1、KMT2A::ELL）构建列线图评分和 KMT2A::MLLT10)，为预测患有 AML 的非 APL 儿科患者的 OS 提供了良好的校准和一致性。此外，得分较高的患者表现出较差的结果。列线图分数还在整个队列和内部验证中表现出良好的辨别力和校准能力。此外，人工神经网络证明该列线图评分具有良好的预测性能。我们基于融合基因的模型是非 APL 儿童 AML 患者的预后生物标志物。列线图评分可以提供个性化的预后预测，从而有利于临床决策。版权所有 © 2023 Weng、Chen、Wang、Ying、Guo、Ruan、Song、Xu、Zhang、Xu 和 Tang。

Fusion genes are considered to be one of the major drivers behind cancer initiation and progression. Meanwhile, non-acute promyelocytic leukemia (APL) pediatric patients with acute myeloid leukemia (AML) in children had limited treatment efficacy. Hence, we developed and validated a simple clinical scoring system for predicting outcomes in non-APL pediatric patients with AML.A total of 184 non-APL pediatric patients with AML who were admitted to our hospital and an independent dataset (318 patients) from the TARGET database were included. Least absolute shrinkage and selection operation (LASSO) and Cox regression analysis were used to identify prognostic factors. Then, a nomogram score was developed to predict the 1, 3, and 5 years overall survival (OS) based on their clinical characteristics and fusion genes. The accuracy of the nomogram score was determined by calibration curves and receiver operating characteristic (ROC) curves. Additionally, an internal verification cohort was used to assess its applicability.Based on Cox and LASSO regression analyses, a nomogram score was constructed using clinical characteristics and OS-related fusion genes (CBFβ::MYH11, RUNX1::RUNX1T1, KMT2A::ELL, and KMT2A::MLLT10), yielded good calibration and concordance for predicting OS of non-APL pediatric patients with AML. Furthermore, patients with higher scores exhibited worse outcomes. The nomogram score also demonstrated good discrimination and calibration in the whole cohort and internal validation. Furthermore, artificial neural networks demonstrated that this nomogram score exhibits good predictive performance.Our model based on the fusion gene is a prognostic biomarker for non-APL pediatric patients with AML. The nomogram score can provide personalized prognosis prediction, thereby benefiting clinical decision-making.Copyright © 2023 Weng, Chen, Wang, Ying, Guo, Ruan, Song, Xu, Zhang, Xu and Tang.