Shi Hui-Zhong, Wang Ming-Wei, Huang Yu-Song, Liu Zhong, Li Ling, Wan Li-Ping
Department of Hematology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, No. 100 Haining Road, Shanghai, 200080, China.
Institute of Blood Transfusion, Chinese Academy of Medical Sciences and Peking Union Medical College, Chengdu 610052, China.
Heliyon. 2024 May 22;10(11):e31705. doi: 10.1016/j.heliyon.2024.e31705. eCollection 2024 Jun 15.
Acute myeloid leukemia (AML) is a prevalent hematological malignancy among adults. Recent studies suggest that the length of telomeres could significantly affect both the risk of developing AML and the overall survival (OS). Despite the limited focus on the prognostic value of telomere-related genes (TRGs) in AML, our study aims at addressing this gap by compiling a list of TRGs from TelNet, as well as collecting clinical information and TRGs expression data through the Gene Expression Omnibus (GEO) database. The GSE37642 dataset, sourced from GEO and based on the GPL96 platform, was divided into training and validation sets at a 6:4 ratio. Additionally, the GSE71014 dataset (based on the GPL10558 platform), GSE12417 dataset (based on the GPL96 and GPL570 platforms), and another portion of the GSE37642 dataset (based on the GPL570 platform) were designated as external testing sets. Univariate Cox regression analysis identified 96 TRGs significantly associated with OS. Subsequent Lasso-Cox stepwise regression analysis pinpointed eight TRGs (MCPH1, SLC25A6, STK19, PSAT1, KCTD15, DNMT3B, PSMD5, and TAF2) exhibiting robust predictive potential for patient survival. Both univariate and multivariate survival analyses unveiled TRG risk scores and age as independent prognostic variables. To refine the accuracy of survival prognosis, we developed both a nomogram integrating clinical parameters and a predictive risk score model based on TRGs. In subsequent investigations, associations were emphasized not solely regarding the TRG risk score and immune infiltration patterns but also concerning the response to immune-checkpoint inhibitor (ICI) therapy. In summary, the establishment of a telomere-associated genetic risk model offers a valuable tool for prognosticating AML outcomes, thereby facilitating informed treatment decisions.
急性髓系白血病（AML）是成人中常见的血液系统恶性肿瘤。最近的研究表明，端粒长度可显著影响AML的发病风险和总生存期（OS）。尽管对AML中端粒相关基因（TRG）的预后价值关注有限，但我们的研究旨在通过从TelNet汇编TRG列表，并通过基因表达综合数据库（GEO）收集临床信息和TRG表达数据来填补这一空白。源自GEO并基于GPL96平台的GSE37642数据集按6:4的比例分为训练集和验证集。此外，GSE71014数据集（基于GPL10558平台）、GSE12417数据集（基于GPL96和GPL570平台）以及GSE37642数据集的另一部分（基于GPL570平台）被指定为外部测试集。单变量Cox回归分析确定了96个与OS显著相关的TRG。随后的Lasso-Cox逐步回归分析确定了八个对患者生存具有强大预测潜力的TRG（MCPH1、SLC25A6、STK19、PSAT1、KCTD15、DNMT3B、PSMD5和TAF2）。单变量和多变量生存分析均显示TRG风险评分和年龄是独立的预后变量。为了提高生存预后的准确性，我们开发了一个整合临床参数的列线图和一个基于TRG的预测风险评分模型。在随后的研究中，不仅强调了TRG风险评分与免疫浸润模式之间的关联，还强调了对免疫检查点抑制剂（ICI）治疗的反应。总之，端粒相关遗传风险模型的建立为预测AML结局提供了一个有价值的工具，从而有助于做出明智的治疗决策。
