Department of Pediatrics (Children Hematological Oncology), Birth Defects and Childhood Hematological Oncology Laboratory, The Affiliated Hospital of Southwest Medical University, Sichuan Clinical Research Center for Birth Defects, Luzhou, Sichuan, China.
The Second Hospital, Center for Reproductive Medicine, Advanced Medical Research Institute, and Key Laboratory for Experimental Teratology of the Ministry of Education, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China.
Front Immunol. 2022 Dec 5;13:1038570. doi: 10.3389/fimmu.2022.1038570. eCollection 2022.
The heterogeneity of treatment response in acute myeloid leukemia (AML) patients poses great challenges for risk scoring and treatment stratification. Carbohydrate metabolism plays a crucial role in response to therapy in AML. In this multicohort study, we investigated whether carbohydrate metabolism related genes (CRGs) could improve prognostic classification and predict response of immunity and treatment in AML patients.
Using univariate regression and LASSO-Cox stepwise regression analysis, we developed a CRG prognostic signature that consists of 10 genes. Stratified by the median risk score, patients were divided into high-risk group and low-risk group. Using TCGA and GEO public data cohorts and our cohort (1031 non-M3 patients in total), we demonstrated the consistency and accuracy of the CRG score on the predictive performance of AML survival.
The overall survival (OS) was significantly shorter in high-risk group. Differentially expressed genes (DEGs) were identified in the high-risk group compared to the low-risk group. GO and GSEA analysis showed that the DEGs were mainly involved in immune response signaling pathways. Analysis of tumor-infiltrating immune cells confirmed that the immune microenvironment was strongly suppressed in high-risk group. The results of potential drugs for risk groups showed that inhibitors of carbohydrate metabolism were effective.
The CRG signature was involved in immune response in AML. A novel risk model based on CRGs proposed in our study is promising prognostic classifications in AML, which may provide novel insights for developing accurate targeted cancer therapies.
急性髓系白血病（AML）患者的治疗反应异质性对风险评分和治疗分层构成了巨大挑战。碳水化合物代谢在 AML 对治疗的反应中起着至关重要的作用。在这项多队列研究中，我们研究了碳水化合物代谢相关基因（CRGs）是否可以改善预后分类并预测 AML 患者的免疫和治疗反应。
使用单变量回归和 LASSO-Cox 逐步回归分析，我们开发了一个由 10 个基因组成的 CRG 预后特征。根据中位数风险评分，患者被分为高风险组和低风险组。使用 TCGA 和 GEO 公共数据集以及我们的队列（共 1031 例非 M3 患者），我们证明了 CRG 评分在 AML 生存预测性能上的一致性和准确性。
高风险组的总生存期（OS）明显缩短。与低风险组相比，高风险组中鉴定出差异表达基因（DEGs）。GO 和 GSEA 分析表明，DEGs 主要涉及免疫反应信号通路。肿瘤浸润免疫细胞分析证实高风险组的免疫微环境受到强烈抑制。风险组潜在药物分析表明，碳水化合物代谢抑制剂是有效的。
CRG 特征参与了 AML 中的免疫反应。我们提出的基于 CRGs 的新风险模型有望成为 AML 的预后分类，这可能为开发精确的靶向癌症治疗提供新的见解。
