1. The establishment of a prognostic scoring model based on the new tumor immune microenvironment classification in acute myeloid leukemia
- Author
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Longzhen Cui, Wenhui Huang, Tingting Qian, Chaozeng Si, Lin Fu, Yan Liu, Tiansheng Zeng, and Cong Deng
- Subjects
Oncology ,Tumor immune microenvironment classification ,medicine.medical_specialty ,Immune microenvironment ,Population ,Internal medicine ,Tumor Microenvironment ,Humans ,Medicine ,education ,Precision treatment ,Tumor microenvironment ,education.field_of_study ,Acute myeloid leukemia ,Receiver operating characteristic ,business.industry ,Myeloid leukemia ,General Medicine ,Prognosis ,medicine.disease ,Hierarchical clustering ,Leukemia, Myeloid, Acute ,Leukemia ,ROC Curve ,Prognostic model ,Transcriptome ,business ,Research Article - Abstract
Background The high degree of heterogeneity brought great challenges to the diagnosis and treatment of acute myeloid leukemia (AML). Although several different AML prognostic scoring models have been proposed to assess the prognosis of patients, the accuracy still needs to be improved. As important components of the tumor microenvironment, immune cells played important roles in the physiological functions of tumors and had certain research value. Therefore, whether the tumor immune microenvironment (TIME) can be used to assess the prognosis of AML aroused our great interest. Methods The patients’ gene expression profile from 7 GEO databases was normalized after removing the batch effect. TIME cell components were explored through Xcell tools and then hierarchically clustered to establish TIME classification. Subsequently, a prognostic model was established by Lasso-Cox. Multiple GEO databases and the Cancer Genome Atlas dataset were employed to validate the prognostic performance of the model. Receiver operating characteristic (ROC) and the concordance index (C-index) were utilized to assess the prognostic efficacy. Results After analyzing the composition of TIME cells in AML, we found infiltration of ten types of cells with prognostic significance. Then using hierarchical clustering methods, we established a TIME classification system, which clustered all patients into three groups with distinct prognostic characteristics. Using the differential genes between the first and third groups in the TIME classification, we constructed a 121-gene prognostic model. The model successfully divided 1229 patients into the low and high groups which had obvious differences in prognosis. The high group with shorter overall survival had more patients older than 60 years and more poor-risk patients (both P Conclusion A prognostic model for AML based on the TIME classification was constructed in our study, which may provide a new strategy for precision treatment in AML.
- Published
- 2021