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Construction and Validation of a Novel Ferroptosis-Related Prognostic Model for Acute Myeloid Leukemia

ABSTRACT: Acute myeloid leukemia (AML) is a clonal malignant proliferative blood disorder with a poor prognosis. Ferroptosis, a novel form of programmed cell death, holds great promise for oncology treatment, and has been demonstrated to interfere with the development of various diseases. A range of genes are involved in regulating ferroptosis and can serve as markers of it. Nevertheless, the prognostic significance of these genes in AML remains poorly understood. Transcriptomic and clinical data for AML patients were acquired from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Univariate Cox analysis was performed to identify ferroptosis-related genes with prognostic value, and the least absolute shrinkage and selection operator (LASSO) algorithm and stepwise multivariate Cox regression analysis were utilized to optimize gene selection from the TCGA cohort (132 samples) for model construction. Tumor samples from the GEO database (136 samples and 104 samples) were used as validation groups to estimate the predictive performance of the risk model. Finally, an eight-gene prognostic signature (including CHAC1, CISD1, DPP4, GPX4, AIFM2, SQLE, PGD, and ACSF2) was identified for the prediction of survival probability and was used to stratify AML patients into high- and low-risk groups. Survival analysis illustrated significantly prolonged overall survival and lower mortality in the low-risk group. The area under the receiver operating characteristic curve demonstrated good results for the training set (1-year: 0.846, 2-years: 0.826, and 3-years: 0.837), which verified the accuracy of the model for predicting patient survival. Independent prognostic analysis indicated that the model could be used as a prognostic factor (p ≤ 0.001). Functional enrichment analyses revealed underlying mechanisms and notable differences in the immune status of the two risk groups. In brief, we conducted and validated a novel ferroptosis-related prognostic model for outcome prediction and risk stratification in AML, with great potential to guide individualized treatment strategies in the future.

SUBMITTER: Song Y

PROVIDER: S-EPMC8803125 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background: The abnormal expression of RNA-binding proteins (RBPs) in various malignant tumors is closely related to the occurrence and development of tumors. However, the role of RBPs in acute myeloid leukemia (AML) is unclear. Methods: We downloaded harmonized RNA-seq count data and clinical data for AML from UCSC Xena, including The Cancer Genome Atlas (TCGA), The Genotype-Tissue Expression (GTEx), and Therapeutically Applicable Research to Generate Effective Treatments (TARGET) cohorts. R package edgeR was used for differential expression analysis of 337 whole-blood data and 173 AML data. The prognostic value of these RBPs was systematically investigated by using univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO)-Cox regression analysis, and multivariate Cox regression analysis. C-index and calibration diagram were used to judge the accuracy of the model, and decision curve analysis (DCA) was used to judge the net benefit. The biological pathways involved were revealed by gene set enrichment analysis (GSEA). The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and the protein-protein interaction (PPI) network performed lateral verification on the selected gene set and LASSO results. Results: A prognostic model of 12-RBP signature was established. In addition, the net benefit and prediction accuracy of the prognostic model and the mixed model based on it were significantly higher than that of cytogenetics. It is verified in the TARGET cohort and shows good prediction effect. Both the selection of our gene set and the LASSO results have high credibility. Most of these pathways are involved in the development of the disease, and they also accumulate in leukemia and RNA-related pathways. Conclusion: The prognosis model of the 12-RBP signature found in this study is an optimized biomarker that can effectively stratify the risk of AML patients. Nomogram based on this prognostic model is a reliable method to predict the median survival time of patients. This study expands our current understanding of the role of RBPs in the occurrence of AML and may lay the foundation for future treatment of the disease.

Project description:BackgroundAcute myeloid leukemia (AML) is a clonal malignant disease with poor prognosis and a low overall survival rate. Although many studies on the treatment and detection of AML have been conducted, the molecular mechanism of AML development and progression has not been fully elucidated. The present study was designed to pursuit the molecular mechanism of AML using a comprehensive bioinformatics analysis, and build an applicable model to predict the survival probability of AML patients in clinical use.MethodsTo simplify the complicated regulatory networks, we performed the gene co-expression and PPI network based on WGCNA and STRING database using modularization design. Two machine learning methods, A least absolute shrinkage and selector operation (LASSO) algorithm and support vector machine-recursive feature elimination (SVM-RFE), were used to filter the common hub genes by five-fold cross-validation. The candidate hub genes were used to build the predictive model of AML by the cox-proportional hazards analysis, and validated in The Cancer Genome Atlas (TCGA) cohort and ohsu cohort, which were reliable in the experimental verification by qRT-PCR and western blotting in mRNA and protein levels.ResultsThree hub genes, FLT3, CD177 and TTPAL were used to build a clinically applicable model to predict the survival probability of AML patients and divided them into high and low groups. To compare the survival ability of the model with the classical clinical features, we generated the nomogram. The model displayed the most risk points contrast to other clinical characteristics, which was compatible with the data of cox multivariate regression.ConclusionThis study reveal the novel molecular mechanism of AML, and construct a clinical model significantly related to AML patient prognosis. We showed the integrated roles of critical pathways, hub genes associated, which provide potential targets and new research ideas for the treatment and early detection of AML.

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Construction and Validation of a Novel Ferroptosis-Related Prognostic Model for Acute Myeloid Leukemia

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