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

## TL;DR

This study builds a model using the tumor microenvironment to predict outcomes in acute myeloid leukemia patients and identifies key genes linked to survival.

## Contribution

A novel TME-based prognostic model for AML with eight essential survival-related genes is developed and validated.

## Key findings

- The TME model showed strong associations with survival and FAB classification in AML patients.
- Eight genes (CXCL12, GZMB, ITPR2, LYN, RAB9B, RGMB, RUFY4, TRIM16) were identified as strongly correlated with survival.
- The model achieved high AUC values for 1-, 3-, and 5-year survival prediction in the TCGA cohort.

## Abstract

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.

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.

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.

These findings present a TME-based prognostic model that offers a promising avenue for precise risk stratification and targeted therapeutic strategies in AML.