Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Laboratory of Clinical Immunology, Wuhan No. 1 Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
DNA Cell Biol. 2020 Sep;39(9):1595-1605. doi: 10.1089/dna.2020.5667. Epub 2020 Aug 12.
Autophagy, a highly conserved cellular protein degradation process, has been involved in acute myeloid leukemia (AML). The present study aims to establish a novel, autophagy-related prognostic signature for prediction of AML prognosis. Differentially expressed autophagy-related genes in AML and healthy samples were screened using GSE1159. Univariate Cox regression analysis was applied to determine survival-associated autophagy-related genes in The Cancer Genome Atlas (TCGA) AML cohort. Lasso regression was performed to develop multiple-gene prognostic signatures. A novel six-gene signature (including , , , , , and ) DC was established for AML prognosis prediction. The Kaplan-Meier survival analysis revealed that patients in the high-risk score group had poorer overall survival (OS). The receiver operating characteristic (ROC) curve validated its good performance in survival prediction in TCGA AML cohort, and the area under the curve value was 0.817. Moreover, our signature could independently predict OS. A nomogram was constructed, including the six-gene signature and other clinical parameters, and predictive efficiency was confirmed using the ROC curve and calibration curve. Furthermore, gene set enrichment analyses identified several tumor-associated pathways that may contribute to explain the potential molecular mechanisms of our signature. Overall, we developed a new autophagy-associated gene signature and nomogram to predict OS of AML patients, which may help in clinical decision-making for AML treatment.
自噬是一种高度保守的细胞蛋白降解过程,与急性髓系白血病(AML)有关。本研究旨在建立一种新的、与自噬相关的预后标志物,用于预测 AML 的预后。使用 GSE1159 筛选 AML 和健康样本中的差异表达的自噬相关基因。在 TCGA AML 队列中,应用单因素 Cox 回归分析确定与生存相关的自噬相关基因。使用 Lasso 回归构建多基因预后标志物。建立了一个新的六基因标志物(包括、、、、和)用于 AML 预后预测。Kaplan-Meier 生存分析显示,高风险评分组的患者总体生存率(OS)较差。ROC 曲线验证了该标志物在 TCGA AML 队列中的生存预测性能良好,曲线下面积值为 0.817。此外,我们的标志物可以独立预测 OS。构建了一个包含六个基因标志物和其他临床参数的列线图,并通过 ROC 曲线和校准曲线验证了预测效率。此外,基因集富集分析确定了几个与肿瘤相关的通路,这些通路可能有助于解释我们的标志物的潜在分子机制。总之,我们开发了一种新的与自噬相关的基因标志物和列线图,用于预测 AML 患者的 OS,这可能有助于 AML 治疗的临床决策。