PloS one · 2025
Bioinformatics-guided construction of a tumor microenvironment-derived prognostic model in acute myeloid leukemia.
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Navidinia Amir Abbas, Khayami Reza, Gholami Alireza, Fathi Mahnaz, Keshavarz Ali, Karami Najibe, Alipanah Hirad, Ahmadi Ali, Rostami Shahrbano, Chahardouli Bahram
Аннотация
BACKGROUND: The tumor microenvironment (TME) exerts a profound influence on the progression, therapeutic responses, and clinical outcomes of acute myeloid leukemia (AML), a prevalent hematologic malignancy in adults. This study aimed to establish a TME-based prognostic model to unveil novel therapeutic and prognostic avenues for AML.
METHODS: Gene expression profiles and clinical information for 134 AML patients were retrieved from The Cancer Genome Atlas (TCGA). The TME cellular components were evaluated using the ESTIMATE algorithm, and differentially expressed genes (DEGs) were identified. A Microenvironment Prognostic Model (MPM) was subsequently constructed through univariate Cox regression, LASSO regression, and multivariate Cox regression analyses. The predictive performance of the MPM was validated in a separate cohort of 312 AML patients from the TARGET database.
RESULTS: Kaplan-Meier analysis revealed significant associations between the TME, French-American-British (FAB) classification, and overall survival (p-values = 3.6e-07 and 0.011, respectively). LASSO-Cox regression identified eight essential genes (CXCL12, GZMB, ITPR2, LYN, RAB9B, RGMB, RUFY4, TRIM16) that exhibited a strong correlation with survival (p-value < 0.0001). The MPM demonstrated excellent prognostic performance, with area under the curve (AUC) values of 84.05, 85.73, and 89.54 for predicting 1-, 3-, and 5-year survival, respectively. External validation with the TARGET database underscored the robustness of this model, yielding AUC values of 60.5%, 56.7%, and 55.7% at the corresponding intervals.
CONCLUSION: These findings present a TME-based prognostic model that offers a promising avenue for precise risk stratification and targeted therapeutic strategies in AML.
METHODS: Gene expression profiles and clinical information for 134 AML patients were retrieved from The Cancer Genome Atlas (TCGA). The TME cellular components were evaluated using the ESTIMATE algorithm, and differentially expressed genes (DEGs) were identified. A Microenvironment Prognostic Model (MPM) was subsequently constructed through univariate Cox regression, LASSO regression, and multivariate Cox regression analyses. The predictive performance of the MPM was validated in a separate cohort of 312 AML patients from the TARGET database.
RESULTS: Kaplan-Meier analysis revealed significant associations between the TME, French-American-British (FAB) classification, and overall survival (p-values = 3.6e-07 and 0.011, respectively). LASSO-Cox regression identified eight essential genes (CXCL12, GZMB, ITPR2, LYN, RAB9B, RGMB, RUFY4, TRIM16) that exhibited a strong correlation with survival (p-value < 0.0001). The MPM demonstrated excellent prognostic performance, with area under the curve (AUC) values of 84.05, 85.73, and 89.54 for predicting 1-, 3-, and 5-year survival, respectively. External validation with the TARGET database underscored the robustness of this model, yielding AUC values of 60.5%, 56.7%, and 55.7% at the corresponding intervals.
CONCLUSION: These findings present a TME-based prognostic model that offers a promising avenue for precise risk stratification and targeted therapeutic strategies in AML.