The First Affiliated Hospital, Institute of Hematology, School of Medicine, Jinan University, Guangzhou, China.
Department of Experimental Research, Sun Yat-sen University Cancer Center, State Key Laboratory Oncology in South China, Guangzhou, China.
Cancer Med. 2023 Apr;12(7):9055-9067. doi: 10.1002/cam4.5644. Epub 2023 Jan 27.
Acute myeloid leukemia (AML) is an aggressive heterogeneous hematological malignancy with remarkably heterogeneous outcomes. This study aimed to identify potential biomarkers for AML risk stratification via analysis of gene expression profiles.
RNA sequencing data from 167 adult AML patients in the Cancer Genome Atlas (TCGA) database were obtained for overall survival (OS) analysis, and 52 bone marrow (BM) samples from our clinical center were used for validation. Additionally, siRNA was used to investigate the role of prognostic genes in the apoptosis and proliferation of AML cells.
Co-expression of 103 long non-coding RNAs (lncRNAs) and mRNAs in the red module that were positively correlated with European Leukemia Network (ELN) risk stratification and age was identified by weighted gene co-expression network analysis (WGCNA). After screening by uni- and multivariate Cox regression, Kaplan-Meier survival, and protein-protein interaction analysis, four genes including the lncRNA LOC541471, GDAP1, SOD1, and STK25 were incorporated into calculating a risk score from coefficients of the multivariate Cox regression model. Notably, GDAP1 expression was the greatest contributor to OS among the four genes. Interestingly, the risk score, ELN risk stratification, and age were independent prognostic factors for AML patients, and a nomogram model constructed with these factors could illustrate and personalize the 1-, 3-, and 5-year OS rates of AML patients. The calibration and time-dependent receiver operating characteristic curves (ROCs) suggested that the nomogram had a good predictive performance. Furthermore, new risk stratification was developed for AML patients based on the nomogram model. Importantly, knockdown of LOC541471, GDPA1, SOD1, or STK25 promoted apoptosis and inhibited the proliferation of THP-1 cells compared to controls.
High expression of LOC541471, GDAP1, SOD1, and STK25 may be biomarkers for risk stratification of AML patients, which may provide novel insight into evaluating prognosis, monitoring progression, and designing combinational targeted therapies.
急性髓系白血病（AML）是一种具有显著异质性的侵袭性血液系统恶性肿瘤，其预后差异很大。本研究旨在通过分析基因表达谱，确定潜在的 AML 风险分层生物标志物。
从癌症基因组图谱（TCGA）数据库中获得 167 例成人 AML 患者的 RNA 测序数据进行总生存期（OS）分析，并使用我们临床中心的 52 个骨髓（BM）样本进行验证。此外，使用 siRNA 研究预后基因在 AML 细胞凋亡和增殖中的作用。
通过加权基因共表达网络分析（WGCNA）发现，红色模块中 103 个长链非编码 RNA（lncRNA）和 mRNAs 的共表达与欧洲白血病网络（ELN）风险分层和年龄呈正相关。通过单变量和多变量 Cox 回归、Kaplan-Meier 生存分析和蛋白质-蛋白质相互作用分析筛选后，从多变量 Cox 回归模型的系数中纳入了包括 lncRNA LOC541471、GDAP1、SOD1 和 STK25 在内的四个基因，计算风险评分。值得注意的是，在这四个基因中，GDAP1 表达对 OS 的贡献最大。有趣的是，风险评分、ELN 风险分层和年龄是 AML 患者的独立预后因素，并且使用这些因素构建的列线图模型可以说明和个性化 AML 患者的 1 年、3 年和 5 年 OS 率。校准和时间依赖性接受者操作特征曲线（ROC）表明，该列线图具有良好的预测性能。此外，基于列线图模型为 AML 患者开发了新的风险分层。重要的是，与对照组相比，敲低 LOC541471、GDAP1、SOD1 或 STK25 可促进 THP-1 细胞凋亡并抑制其增殖。
LOC541471、GDAP1、SOD1 和 STK25 的高表达可能是 AML 患者风险分层的生物标志物，这可能为评估预后、监测进展和设计联合靶向治疗提供新的见解。
