Ferroptosis-Related Gene Signature for Prognosis Prediction in Acute Myeloid Leukemia and Potential Therapeutic Options

Abstract

Yaonan Hong,1,2,* Qi Liu,1,2,* Chuanao Xin,1,2,* Huijin Hu,1,2 Zhenchao Zhuang,1– 3 Hangping Ge,1,2,4 Yingying Shen,1,2,4 Yuechao Zhao,1,2,4 Yuhong Zhou,1,2,4 Baodong Ye,1,2,4 Dijiong Wu1,2,4,5 1Department of Hematology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, Zhejiang, People’s Republic of China; 2The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, People’s Republic of China; 3Department of Clinical Laboratory, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, People’s Republic of China; 4National Traditional Chinese Medicine Clinical Research Base (Hematology), Hangzhou, Zhejiang, People’s Republic of China; 5Department of Oncology and Hematology, Wenzhou Hospital of Integrated Traditional Chinese and Western Medicine Affiliated to Zhejiang Chinese Medicine University, Wenzhou, Zhejiang, People’s Republic of China*These authors contributed equally to this workCorrespondence: Dijiong Wu, Department of Hematology, the First Affiliated Hospital of Zhejiang Chinese Medical University, Zhejiang Provincial Hospital of Chinese Medicine, Hangzhou, Zhejiang, 310006, People’s Republic of China, Tel [phone], Email [email protected]: Limited data were available to understand the significance of ferroptosis in leukemia prognosis, regardless of the genomic background.Methods: 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.Results: 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.Conclusion: The AML prognosis model based on FRGs accurately predicts AML prognosis and drug sensitivity, and the drugs identified worthy further investigation.Keywords: ferroptosis, prediction model, TCGA, acute myeloid leukemia, drug sensitivity

Keywords

• ferroptosis
• prediction model
• tcga
• acute myeloid leukemia
• drug sensitivity