Hong Yaonan, Liu Qi, Xin Chuanao, Hu Huijin, Zhuang Zhenchao, Ge Hangping, Shen Yingying, Zhao Yuechao, Zhou Yuhong, Ye Baodong, Wu Dijiong
Department of Hematology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, Zhejiang, People's Republic of China.
The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, People's Republic of China.
Int J Gen Med. 2024 Sep 4;17:3837-3853. doi: 10.2147/IJGM.S460164. eCollection 2024.
Limited data were available to understand the significance of ferroptosis in leukemia prognosis, regardless of the genomic background.
RNA-seq data from 151 AML patients were analyzed from The Cancer Genome Atlas (TCGA) database, along with 70 healthy samples from the Genotype-Tissue Expression (GTEx) database. Ferroptosis-related genes (FRGs) features were constructed by multivariate COX regression analysis and risk scores were calculated for each sample and a novel prediction model was identified. The validation was carried out using data from 35 AML patients and 13 healthy controls in our cohort. Drug sensitivity analysis was conducted on various chemotherapeutic drugs.
A signature of 10 FRGs was identified, as prognostic predictors for AML, and the risk scores were calculated to constructed the prognostic features of FRGs. Significantly lower overall survival was observed in the high-risk group. The predictive ability of these features for AML prognosis was confirmed using Cox regression analysis, ROC curves, and DCA. The prediction model performed well in our clinical practices, and had its potential superiority when comparing to classical NCCN risk stratification. Multiple chemotherapy drugs, including paclitaxel, dactinomycin, cisplatin, etc. had a lower IC50 in FRGs high-risk group than low-risk group.
The AML prognosis model based on FRGs accurately predicts AML prognosis and drug sensitivity, and the drugs identified worthy further investigation.
无论基因组背景如何,关于铁死亡在白血病预后中的意义,可用数据有限。
分析了来自癌症基因组图谱(TCGA)数据库的151例急性髓系白血病(AML)患者的RNA测序数据,以及来自基因型-组织表达(GTEx)数据库的70份健康样本。通过多变量COX回归分析构建铁死亡相关基因(FRGs)特征,计算每个样本的风险评分,并确定一种新的预测模型。使用我们队列中35例AML患者和13例健康对照的数据进行验证。对各种化疗药物进行药物敏感性分析。
确定了10个FRGs的特征,作为AML的预后预测指标,并计算风险评分以构建FRGs的预后特征。在高危组中观察到总体生存率显著降低。使用Cox回归分析、ROC曲线和决策曲线分析(DCA)证实了这些特征对AML预后的预测能力。该预测模型在我们的临床实践中表现良好,与经典的美国国立综合癌症网络(NCCN)风险分层相比具有潜在优势。多种化疗药物,包括紫杉醇、放线菌素D、顺铂等,在FRGs高危组中的半数抑制浓度(IC50)低于低危组。
基于FRGs的AML预后模型准确预测AML预后和药物敏感性,所确定的药物值得进一步研究。