Identification and Validation of a Prognostic Risk-Scoring Model Based on Ferroptosis-Associated Cluster in Acute Myeloid Leukemia

Nomogram Univariate Univariate analysis
DOI: 10.3389/fcell.2021.800267 Publication Date: 2022-01-21T07:57:57Z
ABSTRACT
Background: Emerging evidence has proven that ferroptosis plays an important role in the development of acute myeloid leukemia (AML), whereas exact ferroptosis-associated genes AML patients' prognosis remained unclear. Materials and Methods: Gene expression profiles corresponding clinical information cases were obtained from TCGA (TCGA-LAML), GEO (GSE71014), TARGET databases (TARGET-AML). Patients cohort well-grouped into two clusters based on ferroptosis-related genes, differentially expressed screened between clusters. Univariate Cox LASSO regression analyses applied to select prognosis-related for construction a prognostic risk-scoring model. Survival analysis was analyzed by Kaplan-Meier receiver operator characteristic curves. Furthermore, we explored correlation model with immune infiltration chemotherapy response. Risk gene level detected quantitative reverse transcription polymerase chain reaction. Results: Eighteen signature including ZSCAN4, ASTN1, CCL23, DLL3, EFNB3, FAM155B, FOXL1, HMX2, HRASLS, LGALS1, LHX6, MXRA5, PCDHB12, PRINS, TMEM56, TWIST1, ZFPM2, ZNF560, developed construct patients could be grouped high- low-risk groups, showed better survival than high-risk patients. Area under curve values 1, 3, 5 years 0.81, 0.827, 0.786 training set, respectively, indicating good predictive efficacy. In addition, age risk score independent factors after univariate multivariate analyses. A nomogram containing constructed estimate individual survival. Further demonstrated associated response chemotherapy. Our experiment data revealed LGALS1 TMEM56 notably decreased samples normal samples. Conclusion: study shows key may provide potential biomarkers therapeutic option
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