Bioinformatics-guided construction of a tumor microenvironment-derived prognostic model in acute myeloid leukemia

Amir Abbas Navidinia, Reza Khayami, Alireza Gholami, Mahnaz Fathi, Ali Keshavarz, Najibe Karami, Hirad Alipanah, Ali Ahmadi, Shahrbano Rostami and Bahram Chahardouli

PLOS ONE, 2025, vol. 20, issue 7, 1-17

Abstract: 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

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0325145

DOI: 10.1371/journal.pone.0325145

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