Identification and Validation of a Prognostic Risk-Scoring Model Based on Ferroptosis-Associated Cluster in Acute Myeloid Leukemia - Wang, Jinghua; Zhuo, Zewei; Wang, Yanjun; Yang, Shuo; Chen, Jierong; Wang, Yulian; Geng, Suxia; Li, Minming; Du, Xin; Lai, Peilong; Weng, Jianyu (Frontiers in Cell and Developmental Biology, 2022)
Background: Emerging evidence has proven that ferroptosis plays an important role in the development of acute myeloid leukemia (AML), whereas the exact role of ferroptosis-associated genes in AML patients’ prognosis remained unclear. Materials and Methods: Gene expression profiles and corresponding clinical information of AML cases were obtained from TCGA database (TCGA-LAML), GEO database (GSE71014) and TARGET database (TARGET-AML). Patients in the TCGA cohort were well grouped into two clusters based on the ferroptosis-related genes and differentially expressed genes were screened between the two clusters. Univariate Cox and LASSO regression analyses were applied to select prognosis-related genes for the construction of a prognostic risk scoring model. Survival analysis was analyzed by Kaplan–Meier and receiver operator characteristic (ROC) curves. Furthermore, we explored the correlation of the prognostic risk scoring model with immune infiltration and chemotherapy response. Risk gene expression level was detected by quantitative reverse transcription polymerase chain reaction (qRT-PCR). Results: A 18-signature genes including ZSCAN4, ASTN1, CCL23, DLL3, EFNB3, FAM155B, FOXL1, HMX2, HRASLS, LGALS1, LHX6, MXRA5, PCDHB12, PRINS, TMEM56, TWIST1, ZFPM2, ZNF560 were developed to construct a prognostic risk scoring model. AML patients could be grouped into high- and low-risk groups and low-risk patients showed better survival than that of the high-risk patients. AUC values of 1-, 3-, and 5- years were 0.81, 0.827, and 0.786 in the training set, respectively, indicated a good predictive efficacy. In addition, age and risk score were the independent prognostic factors after univariate and multivariate Cox regression analyses. A nomogram containing clinical factors and prognostic risk scoring model was constructed to better estimate individual survival. Further analyses demonstrated that risk score was associated with the immune infiltration and response to chemotherapy. Our experiment data revealed that LGALS1 and TMEM56 showed notably decreased expression in AML samples than that of the normal samples. Conclusion: Our study showed that the prognostic risk scoring model and key risk gene may provide potential prognostic biomarkers and therapeutic option for AML patients.