Dataset Information

Development and Validation of a Novel Prognostic Model for Acute Myeloid Leukemia Based on Immune-Related Genes.

ABSTRACT: The prognosis of acute myeloid leukemia (AML) is closely related to immune response changes. Further exploration of the pathobiology of AML focusing on immune-related genes would contribute to the development of more advanced evaluation and treatment strategies. In this study, we established a novel immune-17 signature based on transcriptome data from The Cancer Genome Atlas (TCGA) and The Genotype-Tissue Expression (GTEx) databases. We found that immune biology processes and transcriptional dysregulations are critical factors in the development of AML through enrichment analyses. We also formulated a prognostic model to predict the overall survival of AML patients by using LASSO (Least Absolute Shrinkage and Selection Operator) regression analysis. Furthermore, we incorporated the immune-17 signature to improve the prognostic accuracy of the ELN2017 risk stratification system. We concluded that the immune-17 signature represents a novel useful model for evaluating AML survival outcomes and may be implemented to optimize treatment selection in the next future.

SUBMITTER: Li R

PROVIDER: S-EPMC8131848 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Development and Validation of a Novel Prognostic Model for Acute Myeloid Leukemia Based on Immune-Related Genes.

Li Ran R Ding Zuoyou Z Jin Peng P Wu Shishuang S Jiang Ge G Xiang Rufang R Wang Wenfang W Jin Zhen Z Li Xiaoyang X Xue Kai K Wu Xiaolu X Li Junmin J

Frontiers in immunology 20210505

The prognosis of acute myeloid leukemia (AML) is closely related to immune response changes. Further exploration of the pathobiology of AML focusing on immune-related genes would contribute to the development of more advanced evaluation and treatment strategies. In this study, we established a novel immune-17 signature based on transcriptome data from The Cancer Genome Atlas (TCGA) and The Genotype-Tissue Expression (GTEx) databases. We found that immune biology processes and transcriptional dys ...[more]

PMID: 34025649

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Project description:BackgroundAcute myeloid leukemia (AML) is a cancer arising in the bone marrow and is the most common type of adult leukemia. AML has a poor prognosis, and currently, its prognosis evaluation does not include immune status assessment. This study established an immune-related long non-coding RNA (lncRNA) prognostic risk model for AML based on immune lncRNAs screening.MethodsTo construct training and validation cohorts, The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) public databases were accessed to obtain gene expression profiles and clinical data. The correlation between lncRNAs and immunity genes was analyzed using the "limma" package, and the immune-related lncRNAs were obtained. Through least absolute shrinkage and selection operator regression, a prognostic model was established with immune-related lncRNAs. Using the median risk score, patients were divided into high- and low-risk groups. The Kaplan-Meier method was used for survival analysis, whereas the accuracy of the risk model was evaluated using time-dependent receiver operating characteristic curves, risk score distribution, survival status, and risk heat maps. We utilized univariate and multivariate Cox regression to examine the association between risk score and clinical variables and AML survival and prognosis.ResultsIn the immune-related lncRNA prognostic risk model, the prognosis was better for low-risk than for high-risk patients, indicating risk score of this model as an independent indicator of prognosis. The area under the curve value for 1-, 3-, and 5-year survival of TCGA patients was 0.817, 0.859, and 0.909, respectively, whereas that of GEO patients (of dataset GPL96-GSE37642) was 0.603, 0.652, and 0.624, respectively. Gene set enrichment analysis revealed the enrichment of multiple pathways, such as antigen processing, B-cell receptor signaling pathway, natural killer cell-mediated cytotoxicity, and chemokines, in high-risk patients.ConclusionsIn this study, immune-related lncRNA prognostic risk models effectively predicted AML survival and provided potential treatment targets.

Project description: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.

Dataset Information

Development and Validation of a Novel Prognostic Model for Acute Myeloid Leukemia Based on Immune-Related Genes.

Publications

Development and Validation of a Novel Prognostic Model for Acute Myeloid Leukemia Based on Immune-Related Genes.

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